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“I think I can safely say that nobody understands quantum mechanics.” Richard P. Feynman Quantum mechanics is the foundation of physics, which underlies chemistry, which is the foundation of biology –> nature. Scientists who wants to simulate nature, biology, chemistry, they need a better way of making calculations that can handle uncertainty. Quantum computing will impact our ability to solve problems that are hard to address by traditional supercomputers. Instead of bits, quantum computers consist of qubits. Quantum mechanics allow qubits to code more information than bits. And without quantum mechanics matter would not exist. Quantum Computers are particularly good at calculating properties of systems based on quantum mechanical. It includes molecules. Caffeine is a small molecule. It contains protons, neutrons, and electrons. Number of bits required to the molecule and bonds that hold it all together is approximately 10^48 (in case you do not know how big such number is here it is: 10000000000000000000000000000000000000000000000000). Just one molecule! ☕ Cup of coffee contains approximately 95 mg of caffeine – means 2.95 × 10^20 molecules (295000000000000000000 molecules). Smell your coffee before drinking and reflect that nature handles the single caffeine molecule effectively almost without visible effort. A quantum computer with 160 qubits could make such calculation. “With quantum computing, we really don’t know what we’re going to be able to solve. The answer is going to surprise us.” You might be wondering how quantum mechanics is even relevant to businesses today? Quantum computing may provide a new path to solve some of the hardest or most memory intensive problems in business and science. There are 4 categories of problems, quantum computers can solve much better than classical computers: Encryption and Cybersecurity – Our digital lives rely on cryptography. Current encryption algorithms, like RSA, can be broken, if one can figure out the two prime factors of a number with hundreds of digits. Classical computers would need enormous amount of time to solve it. Algorithm on a quantum computer could quickly calculate the prime numbers used in current encryption schemes Currently, quantum computers are too small and error prone to accomplish this. But it is only matter of time. Chemistry&Biology Research – Quantum computers could replicate chemical systems to give us new insights into molecules and reactions by simulating how the electrons in the atoms that make up molecules interact with each other. Designing new fertilizers is key in food production. Scientists hope quantum computers will give them a better understanding of this process in the near future and find more energy-efficient ways to make fertilizer. Optimization Problems (eg. logistics) rather than billions of trillions of individual operations, quantum computing can reduce the most difficult optimization problems down to a number of operations where even a classical computer could find the optimal answer quickly. Data Analysis – finding patterns is harder as the datasets get larger – and they are getting huge in many scientific fields. Quantum computers offer a fundamentally different and faster way to explore these large datasets and could help solve this important type of problem Progress of quantum computing is happening fast. There is great progress in developing algorithms that quantum computers may use. But the devices themselves still need a lot more work. In October 2019, Google’s Californian research lab became the first to achieve “quantum supremacy”, performing a calculation that would be practically impossible for even the most powerful classical supercomputer. The University of Science and Technology of China achieved quantum supremacy only 14 months later, claiming its quantum computer to be 10 billion times faster than Google’s. IBM hopes to have a 1,000-qubit machine by 2023. The history of quantum computers started in 1935 with EPR Paradox … but everyone can start learning about quantum computers & quantum physics (and what #qubits are) from comics: “The Talk” by Scott Aaronson & Zach Weinersmith. Learning about complex topics in engaging way is essential (especially during pandemic). In case comics are not for you, there is one book which explained quantum computing without unnecessary difficult terms and advanced math: “Q is for Quantum” by Terry Rudolph Part of the trouble with quantum computing is that it involves new weird terms and unknown concepts. Author of that book found a way to explain basic concepts of quantum mechanics in a way it could be understandable for everyone. He presumed readers only to know basic arithmetic. If you would like to try if that book is for you – free first chapter could be downloaded from: https://www.qisforquantum.org After some theory, there is time to start practicing. It looks like good moment for developers and other IT specialists to start exploring quantum computing. Let’s start with 3 programming languages where you can design and execute quantum circuits: - Microsoft Q# & Quantum Development Kit – https://docs.microsoft.com/en-us/azure/quantum/overview-what-is-qsharp-and-qdk - IBM Qiskit – open-source quantum development kit https://qiskit.org - Google Cirq – Python library for programing quantum computers https://quantumai.google/cirq All those three are built with user-friendly development environments with sample documentation to help developers start their quantum journey. Digital transformation would not slow down, new emerging technologies would be adopted across industries. If you want to be ready for next wave of digital transformation, it is a good time to learn some basics about quantum computing.

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Researchers at PSI have compared the electron distribution below the oxide layer of two semiconductors. The investigation is part of an effort to develop particularly stable quantum bits –and thus, in turn, particularly efficient quantum computers. They have now published their latest research, which is supported in part by Microsoft, in the scientific journal Advanced Quantum Technologies. By now, the future of computing is inconceivable without quantum computers. For the most part, these are still in the research phase. They hold the promise of speeding up certain calculations and simulations by orders of magnitude compared to classical computers. Quantum bits, or qubits for short, form the basis of quantum computers. So-called topological quantum bits are a novel type that might prove to be superior. To find out how these could be created, an international team of researchers has carried out measurements at the Swiss Light Source SLS at PSI. More stable quantum bits “Computer bits that follow the laws of quantum mechanics can be achieved in different ways,” explains Niels Schröter, one of the study’s authors. He was a researcher at PSI until April 2021, when he moved to the Max Planck Institute of Microstructure Physics in Halle, Germany. “Most types of qubits unfortunately lose their information quickly; you could say they are forgetful qubits.” There is a technical solution to this: Each qubit is backed up with a system of additional qubits that correct any errors that occur. But this means that the total number of qubits needed for an operational quantum computer quickly rises into the millions. “Microsoft’s approach, which we are now collaborating on, is quite different,” Schröter continues. “We want to help create a new kind of qubit that is immune to leakage of information. This would allow us to use just a few qubits to achieve a slim, functioning quantum computer.” The researchers hope to obtain such immunity with so-called topological quantum bits. These would be something completely new that no research group has yet been able to create. Topological materials became more widely known through the Nobel Prize in Physics in 2016. Topology is originally a field of mathematics that explores, among other things, how geometric objects behave when they are deformed. However, the mathematical language developed for this can also be applied to other physical properties of materials. Quantum bits in topological materials would then be topological qubits. Quasiparticles in semiconductor nanowires It is known that thin-film systems of certain semiconductors and superconductors could lead to exotic electron states that would act as such topological qubits. Specifically, ultra-thin, short wires made of a semiconductor material could be considered for this purpose. These have a diameter of only 100 nanometres and are 1,000 nanometres (i.e., 0.0001 centimetres) long. On their outer surface, in the longitudinal direction, the top half of the wires is coated with a thin layer of a superconductor. The rest of the wire is not coated so that a natural oxide layer forms there. Computer simulations for optimising these components predict that the crucial, quantum mechanical electron states are only located at the interface between the semiconductor and the superconductor and not between the semiconductor and its oxide layer. “The collective, asymmetric distribution of electrons generated in these nanowires can be physically described as so-called quasiparticles,” says Gabriel Aeppli, head of the Photon Science Division at PSI, who was also involved in the current study. “Now, if suitable semiconductor and superconductor materials are chosen, these electrons should give rise to special quasiparticles called Majorana fermions at the ends of the nanowires.” Majorana fermions are topological states. They could therefore act as information carriers, ergo as quantum bits in a quantum computer. “Over the course of the last decade, recipes to create Majorana fermions have already been studied and refined by research groups around the world,” Aeppli continues. “But to continue with this analogy: we still didn’t know which cooking pot would give us the best results for this recipe.” Indium antimonide has the advantage A central concern of the current research project was therefore the comparison of two “cooking pots”. The researchers investigated two different semiconductors and their natural oxide layer: on the one hand indium arsenide and on the other indium antimonide. At SLS, the PSI researchers used an investigation method called soft X-ray angle-resolved photoelectron spectroscopy – SX-ARPES for short. A novel computer model developed by Noa Marom’s group at Carnegie Mellon University, USA, together with Vladimir Strocov from PSI, was used to interpret the complex experimental data. “The computer models used up to now led to an unmanageably large number of spurious results. With our new method, we can now look at all the results, automatically filter out the physically relevant ones, and properly interpret the experimental outcome,” explains Strocov. Through their combination of SX-ARPES experiments and computer models, the researchers have now been able to show that indium antimonide has a particularly low electron density below its oxide layer. This would be advantageous for the formation of topological Majorana fermions in the planned nanowires. “From the point of view of electron distribution under the oxide layer, indium antimonide is therefore better suited than indium arsenide to serve as a carrier material for topological quantum bits,” concludes Niels Schröter. However, he points out that in the search for the best materials for a topological quantum computer, other advantages and disadvantages must certainly be weighed against each other. “Our advanced spectroscopic methods will certainly be instrumental in the quest for the quantum computing materials,” says Strocov. “PSI is currently taking big steps to expand quantum research and engineering in Switzerland, and SLS is an essential part of that.” Text: Paul Scherrer Institute/Laura Hennemann The Paul Scherrer Institute PSI develops, builds and operates large, complex research facilities and makes them available to the national and international research community. The institute’s own key research priorities are in the fields of matter and materials, energy and environment and human health. PSI is committed to the training of future generations. Therefore about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether PSI employs 2100 people, thus being the largest research institute in Switzerland. The annual budget amounts to approximately CHF 400 million. PSI is part of the ETH Domain, with the other members being the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL Lausanne, as well as Eawag (Swiss Federal Institute of Aquatic Science and Technology), Empa (Swiss Federal Laboratories for Materials Science and Technology) and WSL (Swiss Federal Institute for Forest, Snow and Landscape Research). (Last updated in May 2020) - Semiconductors reach the quantum world – press release from 22 December 2021 - Exploring the practical benefits of exotic materials – article from 1 September 2021 - New material also reveals new quasiparticles – press release from 7 May 2019 Dr. Vladimir N. Strocov Research Group Spectroscopy of Novel Materials Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland Telephone: +41 56 310 53 11, e-mail: email@example.com [English, French, Russian] Dr. Niels Schröter Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany Telephone: +49 345 5582 793, e-mail: firstname.lastname@example.org, email@example.com [German, English] Prof. Dr. Gabriel Aeppli Head of the Photon Science Division Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland and Department of Physics, ETH Zurich and Topological Matter Laboratory, EPF Lausanne Telephone: +41 56 310 42 32, e-mail: firstname.lastname@example.org [German, English, French] Electronic structure of InAs and InSb surfaces: density functional theory and angle-resolved photoemission spectroscopy Shuyang Yang Niels B. M. Schröter, V. N. Strocov, S. Schuwalow, M. Rajpalk, K. Ohtani, P. Krogstrup, G. W. Winkler, J. Gukelberger, D. Gresch, G. Aeppli, R. M. Lutchyn, N. Marom Advanced Quantum Technologies 20. January 2022

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Politicians are often excoriated for changing (or flip-flopping) their stances on various political topics. Changes in position often leads to defeat at the polls. In the field of quantum computing, however, it appears flip-flopping is a great idea. Jeremy Hsu (@jeremyhsu) reports, researchers from the U.S. and Australia have developed a flip-flop qubit that could make the construction of quantum computers much easier. For those unfamiliar with the term “qubit,” it’s shorthand for a quantum bit. In traditional computers, a bit is a piece of information that is either a “1” or a “0.” A qubit has the fascinating property of being able to be simultaneously a “1” and a “0.” This means a quantum computer using a qubit can perform two calculations at once. Watch the following video for a fuller explanation as to why quantum computers are going to be faster than traditional computers. Qubits are notoriously difficult to create and maintain. Because they operate at the atomic or sub-atomic level, qubits can easily be disrupted (i.e., knocked out of their superposition states). To limit interference, qubits are often created in highly shielded, extremely low temperature environments. In their attempts to find the best qubit, scientists have tried numerous exotic materials. The flip-flop qubit, however, uses silicon. Hsu explains, “Australian and U.S. researchers have developed qubits based on either the nuclear or electron spin state of phosphorus atoms embedded in silicon. Their latest work has yielded the concept of a ‘flip-flop qubit’ that combines both electron and nuclear spin states — an approach that enables neighboring qubits to remain coupled together despite being separated by larger physical distances. In turn, that makes it much easier to build control schemes for the large arrays of qubits necessary for full-fledged quantum computing.” Watch the following video for more information. “[T]he real challenge when trying to fabricate and operate 100, 1,000, or millions of qubits is how to lay out the classical components, such as interconnects and readout transistors,” Andrea Morello, a quantum physicist at the University of New South Wales, told Hsu. “So, having the qubits spaced out by 200 to 500 nanometers from each other means that we have all that space between them to intersperse all the classical control and readout infrastructure, while using fabrication technologies that are already commonplace in the semiconductor industry.” In other words, Morello believes quantum computers may someday be able to be manufactured in the same way as classical computers. He calls this breakthrough, “A stroke of genius.” Quantum Computing Storage Storage of quantum information is another challenge being worked on by scientists. Dominic John Galeon (@domgaleon) reports, “One of the challenges in quantum communications is extending how long entangled particles can hold information. Researchers from the Australian National University may have found a way to do this using erbium crystals.” In a press release, Australian National University (ANU) Research School of Physics associate professor Matthew Sellars stated, “We have shown that an erbium-doped crystal is the perfect material to form the building blocks of a quantum internet that will unlock the full potential of future quantum computers. We had this idea 10 years ago, but many of our peers told us that such a simple idea couldn’t work. Seeing this result, it feels great to know that our approach was the right one.” Kyree Leary (@KyreeLeary) reports the ANU team is not the only team working on quantum storage. “For the first time,” she writes, “researchers [from the California Institute of Technology] have developed nanoscale quantum memory chips that store information in individual photons. The chips were able to store data for 75 nanoseconds before release, with a success rate of 97 percent.” The ANU team might be impressed with the Caltech’s work with photons, but won’t be impressed with storage time of Caltech’s photons. “The ANU team [was] able to successfully store quantum information for 1.3 seconds. That’s a quantum memory that’s 10,000 times longer compared to other efforts. Plus, it eliminates the need for a conversion process since the erbium crystals operate in the same bandwidth as current fiber optic networks.” The Caltech team admits “in order to be a viable component in quantum networking, the chips will need to be able to retain the information for one millisecond.” Brooks Hays reports a team from Yale University has developed a system using sound to store quantum data. She writes, “Scientists have designed a new quantum computer chip that uses sound waves to store and convert quantum data. The device uses a bulk acoustic wave resonator to store, move and translate quantum information embedded in qubits, or quantum bits. The new, simple and more efficient method for quantum data storage could accelerate quantum computing technology.” Every week new breakthroughs or new ways of doing things are announced in the field of quantum computing. Yet it seems a fully-functioning general-use quantum computer remains elusively out of reach. For some specific types of problems, quantum computers are humanities best hope of finding solutions. Traditional computing methods simply take too long to be workable. Even when one is successfully built and fully functional, don’t expect to see one on your desk. Quantum computers are extremely expensive to build and maintain. Nevertheless, there is a global-wide race to develop the first fully-functional, general-use quantum computer. Jeremy Hsu, “Flip-Flop Qubit Could Make Silicon the King of Quantum Computing,” IEEE Spectrum, 13 September 2017. Dominic John Galeon, “Scientists Just Successfully Stored Quantum Information 10,000 Times Longer Than Ever Before,” Futurism, 13 September 2017. Kyree Leary, “A New Computer Chip Can Store Quantum Information in the Form of Light,” Futurism, 12 September 2017. Brooks Hays, “New quantum computer chip uses sounds waves to store data,” UPI, 22 September 2017.

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We are getting closer to the most spectacular early quantum algorithm – Shor’s algorithm for factoring large composite numbers which can be used to break the most widely used public key cryptography systems. But before we can tackle this algorithm, there is one more thing that we need to understand – the quantum Fourier transform. The discrete Fourier transform Let us leave the quantum world for a few moments and take a look at a classical area in mathematics – the discrete Fourier transform. To motivate the definition to come, let us for a moment assume that we are observing a time dependent signal, i.e. a function f(t) depending on the time t. Let us also assume that we are interested in processing this signal further, using a digital computer. This will force us to reduce the full signal to a finite number of values, i.e. to a set of values f(0), f(1), …, f(N-1) at N discrete points in time. Now assume that we wanted to calculate the classical Fourier transform of this function, i.e. the function given by (the exact formula depends on a few conventions) If we now replace the function f by the sum of those functions which have value f(i) over a range of length 1, i.e. by its discrete version, this formula turns into Now we could code the discrete values f(0), f(1), .. as a sequence xk of numbers, and we could apply the same process to the Fourier transform and replace it by a sequence Xk of numbers, representing again the values at the discrete points 0, 1, …. The above formula then tells us that these numbers would be given by We could now look at this formula as giving us an abstract mapping from the set of sequences of N numbers to itself, provided by (putting in a factor ) This transformation is called the discrete Fourier transform. There is a different way to look at this which is useful to derive some of the properties of the discrete Fourier transform. For every k, we can build the N-dimensional complex vector with components by setting These vectors are mutually orthogonal with respect to the standard hermitian product. In fact, the product of any two of these vectors is given by If , this is clearly N. Otherwise, we can write this as If q is not equal to one, i.e. if k is not equal to k’, then, according to the usual formula for a geometric series, this is equal to However, this is zero, because due to the periodicity of the exponential function. Using this orthogonal basis, we can write the formula for the discrete Fourier transform simply as the hermitian product In particular, the discrete Fourier transform can be seen as the linear transformation that maps the k-th unit vector onto the vector . By adding a factor , this can be turned into a unitary transformation, i.e. in a certain sense the discrete Fourier transform is simply a rotation. This relation can also be used to easily derive a formula for the inverse of the discrete Fourier transform. In fact, if we expand the vector x into the basis , we obtain Fourier transforms of a periodic sequence For what follows, let us adapt and simplify our notation a bit. First, we add a factor to the formula for the Fourier coefficient, so that the Fourier transform is unitary. Second, we use the symbol to denote the N-th root of unity, i.e. With this notation, the formula for the Fourier coefficient is then and the inverse is given by Let us now study a special case that is highly relevant for the applications to the factoring of large numbers – the Fourier transform of a periodic sequence. Thus, suppose there is a sequence xk and a number r called the period such that for all values of s and t that lead to valid indices. Thus, roughly speaking, after r indices, the values of the sequence repeat themselves. It is also common to reserve the term period for the smallest number with this property. We call the number the frequency of the sequence. Let us now assume that the frequency is a natural number, i.e. that the period divides N, and let us try to understand what this means for the Fourier transform. Using the periodicity, we can write the coefficients as follows. where s runs from 0 to u-1. We can now write the first of the sums as follows. Now this is again a geometric series with ! We can therefore conclude as above that this is zero unless q is one, i.e. unless k is a multiple of the frequency u. Thus we have shown that if the sequence xk is periodic with integral frequency u, then all Fourier coefficients Xk are zero for which k is not a multiple of the frequency. In the later applications, this fact will be applied in a situation where we know that a sequence is periodic, but the period is unknown. We will then perform a Fourier transformation and inspect the coefficients Xk. We can then conclude that the unknown frequency u must be a common divisor of all those indices k for which Xk is different from zero. This is exactly true if the period divides N, and we will see later that in important cases, it is still approximately true if the period does not divide N. A quantum algorithm to compute the discrete Fourier transform Let us now carry over our considerations to the world of quantum computing. In a quantum computer with n qubits, the Hilbert space of states is N-dimensional with N=2n, and is spanned by the basis . Every vector can be described by the sequence of its coefficients when expanding it into this basis. Consequently, the discrete Fourier transform defines a unitary transformation on the Hilbert by applying the mapping Now this is a unitary transformation, and as any such transformation, can be implemented by a quantum circuit. We will not go into details on this, see for instance , section 7.8 or section 5.1 of (but be careful, these authors use a different sign convention for the Fourier transform). The important part, however, is that a quantum Fourier transform for N=2n can be realized with O(n2) quantum gates. In contrast to this, the best known classical algorithms for computing the discrete Fourier transform are commonly known as fast Fourier transform and require O(Nn) steps. Thus it seems that we have again found a quantum algorithm which is substantially faster than its classical counterpart. Unfortunately, this is not really true, as we have not yet defined our measurement procedure. In fact, if we measure the result of applying a quantum Fourier transform, we destroy the superposition. In addition, the coefficients in which we are interested are the amplitudes of the possible outcomes, and there is no obvious way to measure them – even if we perform the transformation several times and measure over and over again, we only obtain approximations to the probabilities which are the absolute values of the squared amplitudes, so we do not obtain the phase information. Therefore, it is far from clear whether a quantum algorithm can help to compute the Fourier transform more efficiently than it is possible with a classical algorithm. However, most applications of the Fourier transform in quantum algorithms are indirect, using the transform as a sort of amplification step to focus amplitudes on interesting states. In the next post, we will look at Shor’s algorithm with exploits the periodicity properties of the Fourier transform to factor large composite numbers.

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- Introduction & Top Questions - Electromagnetic waves and the electromagnetic spectrum The first two decades of the 20th century left the status of the nature of light confused. That light is a wave phenomenon was indisputable: there were countless examples of interference effects—the signature of waves—and a well-developed electromagnetic wave theory. However, there was also undeniable evidence that light consists of a collection of particles with well-defined energies and momenta. This paradoxical wave-particle duality was soon seen to be shared by all elements of the material world. In 1923 the French physicist Louis de Broglie suggested that wave-particle duality is a feature common to light and all matter. In direct analogy to photons, de Broglie proposed that electrons with momentum p should exhibit wave properties with an associated wavelength λ = h/p. Four years later, de Broglie’s hypothesis of matter waves, or de Broglie waves, was experimentally confirmed by Clinton Davisson and Lester Germer at Bell Laboratories with their observation of electron diffraction effects. A radically new mathematical framework for describing the microscopic world, incorporating de Broglie’s hypothesis, was formulated in 1926–27 by the German physicist Werner Heisenberg and the Austrian physicist Erwin Schrödinger, among others. In quantum mechanics, the dominant theory of 20th-century physics, the Newtonian notion of a classical particle with a well-defined trajectory is replaced by the wave function, a nonlocalized function of space and time. The interpretation of the wave function, originally suggested by the German physicist Max Born, is statistical—the wave function provides the means for calculating the probability of finding a particle at any point in space. When a measurement is made to detect a particle, it always appears as pointlike, and its position immediately after the measurement is well defined. But before a measurement is made, or between successive measurements, the particle’s position is not well defined; instead, the state of the particle is specified by its evolving wave function. The quantum mechanics embodied in the 1926–27 formulation is nonrelativistic—that is, it applies only to particles whose speeds are significantly less than the speed of light. The quantum mechanical description of light was not fully realized until the late 1940s (see below Quantum electrodynamics). However, light and matter share a common central feature—a complementary relation between wave and particle aspects—that can be illustrated without resorting to the formalisms of relativistic quantum mechanics. The same interference pattern demonstrated in Young’s double-slit experiment is produced when a beam of matter, such as electrons, impinges on a double-slit apparatus. Concentrating on light, the interference pattern clearly demonstrates its wave properties. But what of its particle properties? Can an individual photon be followed through the two-slit apparatus, and if so, what is the origin of the resulting interference pattern? The superposition of two waves, one passing through each slit, produces the pattern in Young’s apparatus. Yet, if light is considered a collection of particle-like photons, each can pass only through one slit or the other. Soon after Einstein’s photon hypothesis in 1905, it was suggested that the two-slit interference pattern might be caused by the interaction of photons that passed through different slits. This interpretation was ruled out in 1909 when the English physicist Geoffrey Taylor reported a diffraction pattern in the shadow of a needle recorded on a photographic plate exposed to a very weak light source, weak enough that only one photon could be present in the apparatus at any one time. Photons were not interfering with one another; each photon was contributing to the diffraction pattern on its own. In modern versions of this two-slit interference experiment, the photographic plate is replaced with a detector that is capable of recording the arrival of individual photons. Each photon arrives whole and intact at one point on the detector. It is impossible to predict the arrival position of any one photon, but the cumulative effect of many independent photon impacts on the detector results in the gradual buildup of an interference pattern. The magnitude of the classical interference pattern at any one point is therefore a measure of the probability of any one photon’s arriving at that point. The interpretation of this seemingly paradoxical behaviour (shared by light and matter), which is in fact predicted by the laws of quantum mechanics, has been debated by the scientific community since its discovery more than 100 years ago. The American physicist Richard Feynman summarized the situation in 1965: We choose to examine a phenomenon which is impossible, absolutely impossible, to explain in any classical way, and which has in it the heart of quantum mechanics. In reality, it contains the only mystery. In a wholly unexpected fashion, quantum mechanics resolved the long wave-particle debate over the nature of light by rejecting both models. The behaviour of light cannot be fully accounted for by a classical wave model or by a classical particle model. These pictures are useful in their respective regimes, but ultimately they are approximate, complementary descriptions of an underlying reality that is described quantum mechanically. Quantum optics, the study and application of the quantum interactions of light with matter, is an active and expanding field of experiment and theory. Progress in the development of light sources and detection techniques since the early 1980s has allowed increasingly sophisticated optical tests of the foundations of quantum mechanics. Basic quantum effects such as single photon interference, along with more esoteric issues such as the meaning of the measurement process, have been more clearly elucidated. Entangled states of two or more photons with highly correlated properties (such as polarization direction) have been generated and used to test the fundamental issue of nonlocality in quantum mechanics (see quantum mechanics: Paradox of Einstein, Podolsky, and Rosen). Novel technological applications of quantum optics are also under study, including quantum cryptography and quantum computing.

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Researchers from MIT and elsewhere have recorded, for the first time, the “temporal coherence” of a graphene qubit—meaning how long it can maintain a special state that allows it to represent two logical states simultaneously. The demonstration, which used a new kind of graphene-based qubit, represents a critical step forward for practical quantum computing, the researchers say. Superconducting quantum bits (simply, qubits) are artificial atoms that use various methods to produce bits of quantum information, the fundamental component of quantum computers. Similar to traditional binary circuits in computers, qubits can maintain one of two states corresponding to the classic binary bits, a 0 or 1. But these qubits can also be a superposition of both states simultaneously, which could allow quantum computers to solve complex problems that are practically impossible for traditional computers. The amount of time that these qubits stay in this superposition state is referred to as their “coherence time.” The longer the coherence time, the greater the ability for the qubit to compute complex problems. Recently, researchers have been incorporating graphene-based materials into superconducting quantum computing devices, which promise faster, more efficient computing, among other perks. Until now, however, there’s been no recorded coherence for these advanced qubits, so there’s no knowing if they’re feasible for practical quantum computing. In a paper published today in Nature Nanotechnology, the researchers demonstrate, for the first time, a coherent qubit made from graphene and exotic materials. These materials enable the qubit to change states through voltage, much like transistors in today’s traditional computer chips—and unlike most other types of superconducting qubits. Moreover, the researchers put a number to that coherence, clocking it at 55 nanoseconds, before the qubit returns to its ground state. Find your dream job in the space industry. Check our Space Job Board » The work combined expertise from co-authors William D. Oliver, a physics professor of the practice and Lincoln Laboratory Fellow whose work focuses on quantum computing systems, and Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT who researches innovations in graphene. “Our motivation is to use the unique properties of graphene to improve the performance of superconducting qubits,” says first author Joel I-Jan Wang, a postdoc in Oliver’s group in the Research Laboratory of Electronics (RLE) at MIT. “In this work, we show for the first time that a superconducting qubit made from graphene is temporally quantum coherent, a key requisite for building more sophisticated quantum circuits. Ours is the first device to show a measurable coherence time—a primary metric of a qubit—that’s long enough for humans to control.” There are 14 other co-authors, including Daniel Rodan-Legrain, a graduate student in Jarillo-Herrero’s group who contributed equally to the work with Wang; MIT researchers from RLE, the Department of Physics, the Department of Electrical Engineering and Computer Science, and Lincoln Laboratory; and researchers from the Laboratory of Irradiated Solids at the École Polytechnique and the Advanced Materials Laboratory of the National Institute for Materials Science. A pristine graphene sandwich Superconducting qubits rely on a structure known as a “Josephson junction,” where an insulator (usually an oxide) is sandwiched between two superconducting materials (usually aluminum). In traditional tunable qubit designs, a current loop creates a small magnetic field that causes electrons to hop back and forth between the superconducting materials, causing the qubit to switch states. But this flowing current consumes a lot of energy and causes other issues. Recently, a few research groups have replaced the insulator with graphene, an atom-thick layer of carbon that’s inexpensive to mass produce and has unique properties that might enable faster, more efficient computation. To fabricate their qubit, the researchers turned to a class of materials, called van der Waals materials—atomic-thin materials that can be stacked like Legos on top of one another, with little to no resistance or damage. These materials can be stacked in specific ways to create various electronic systems. Despite their near-flawless surface quality, only a few research groups have ever applied van der Waals materials to quantum circuits, and none have previously been shown to exhibit temporal coherence. For their Josephson junction, the researchers sandwiched a sheet of graphene in between the two layers of a van der Waals insulator called hexagonal boron nitride (hBN). Importantly, graphene takes on the superconductivity of the superconducting materials it touches. The selected van der Waals materials can be made to usher electrons around using voltage, instead of the traditional current-based magnetic field. Therefore, so can the graphene—and so can the entire qubit. When voltage gets applied to the qubit, electrons bounce back and forth between two superconducting leads connected by graphene, changing the qubit from ground (0) to excited or superposition state (1). The bottom hBN layer serves as a substrate to host the graphene. The top hBN layer encapsulates the graphene, protecting it from any contamination. Because the materials are so pristine, the traveling electrons never interact with defects. This represents the ideal “ballistic transport” for qubits, where a majority of electrons move from one superconducting lead to another without scattering with impurities, making a quick, precise change of states. How voltage helps The work can help tackle the qubit “scaling problem,” Wang says. Currently, only about 1,000 qubits can fit on a single chip. Having qubits controlled by voltage will be especially important as millions of qubits start being crammed on a single chip. “Without voltage control, you’ll also need thousands or millions of current loops too, and that takes up a lot of space and leads to energy dissipation,” he says. Additionally, voltage control means greater efficiency and a more localized, precise targeting of individual qubits on a chip, without “cross talk.” That happens when a little bit of the magnetic field created by the current interferes with a qubit it’s not targeting, causing computation problems. For now, the researchers’ qubit has a brief lifetime. For reference, conventional superconducting qubits that hold promise for practical application have documented coherence times of a few tens of microseconds, a few hundred times greater than the researchers’ qubit. But the researchers are already addressing several issues that cause this short lifetime, most of which require structural modifications. They’re also using their new coherence-probing method to further investigate how electrons move ballistically around the qubits, with aims of extending the coherence of qubits in general. Massachusetts Institute of Technology Joel I-Jan Wang et al. Coherent control of a hybrid superconducting circuit made with graphene-based van der Waals heterostructures, Nature Nanotechnology (2018). DOI: 10.1038/s41565-018-0329-2 This visualisation shows layers of graphene used for membranes Credit: University of Manchester

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Newswise — Using AI and computer automation, Technion researchers have developed a “conjecture generator” that creates mathematical conjectures, which are considered to be the starting point for developing mathematical theorems. They have already used it to generate a number of previously unknown formulas. The study, which was published in Nature, was carried out by undergraduates from different faculties under the tutelage of Assistant Professor Ido Kaminer of the Andrew and Erna Viterbi Faculty of Electrical Engineering at the Technion. The project deals with one of the most fundamental elements of mathematics – mathematical constants. A mathematical constant is a number with a fixed value that emerges naturally from different mathematical calculations and mathematical structures in different fields. Many mathematical constants are of great importance in mathematics, but also in disciplines that are external to mathematics, including biology, physics, and ecology. The golden ratio and Euler’s number are examples of such fundamental constants. Perhaps the most famous constant is pi, which was studied in ancient times in the context of the circumference of a circle. Today, pi appears in numerous formulas in all branches of science, with many math aficionados competing over who can recall more digits after the decimal point: The Technion researchers proposed and examined a new idea: The use of computer algorithms to automatically generate mathematical conjectures that appear in the form of formulas for mathematical constants. A conjecture is a mathematical conclusion or proposition that has not been proved; once the conjecture is proved, it becomes a theorem. Discovery of a mathematical conjecture on fundamental constants is relatively rare, and its source often lies in mathematical genius and exceptional human intuition. Newton, Riemann, Goldbach, Gauss, Euler, and Ramanujan are examples of such genius, and the new approach presented in the paper is named after Srinivasa Ramanujan. Ramanujan, an Indian mathematician born in 1887, grew up in a poor family, yet managed to arrive in Cambridge at the age of 26 at the initiative of British mathematicians Godfrey Hardy and John Littlewood. Within a few years he fell ill and returned to India, where he died at the age of 32. During his brief life he accomplished great achievements in the world of mathematics. One of Ramanujan’s rare capabilities was the intuitive formulation of unproven mathematical formulas. The Technion research team therefore decided to name their algorithm “the Ramanujan Machine,” as it generates conjectures without proving them, by “imitating” intuition using AI and considerable computer automation. According to Prof. Kaminer, “Our results are impressive because the computer doesn’t care if proving the formula is easy or difficult, and doesn’t base the new results on any prior mathematical knowledge, but only on the numbers in mathematical constants. To a large degree, our algorithms work in the same way as Ramanujan himself, who presented results without proof. It’s important to point out that the algorithm itself is incapable of proving the conjectures it found – at this point, the task is left to be resolved by human mathematicians.” The conjectures generated by the Technion’s Ramanujan Machine have delivered new formulas for well-known mathematical constants such as pi, Euler’s number (e), Apéry’s constant (which is related to the Riemann zeta function), and the Catalan constant. Surprisingly, the algorithms developed by the Technion researchers succeeded not only in creating known formulas for these famous constants, but in discovering several conjectures that were heretofore unknown. The researchers estimate this algorithm will be able to significantly expedite the generation of mathematical conjectures on fundamental constants and help to identify new relationships between these constants. As mentioned, until now, these conjectures were based on rare genius. This is why in hundreds of years of research, only a few dozens of formulas were found. It took the Technion’s Ramanujan Machine just a few hours to discover all the formulas for pi discovered by Gauss, the “Prince of Mathematics,” during a lifetime of work, along with dozens of new formulas that were unknown to Gauss. According to the researchers, “Similar ideas can in the future lead to the development of mathematical conjectures in all areas of mathematics, and in this way provide a meaningful tool for mathematical research.” The research team has launched a website, RamanujanMachine.com, which is intended to inspire the public to be more involved in the advancement of mathematical research by providing algorithmic tools that will be available to mathematicians and the public at large. Even before the article was published, hundreds of students, experts, and amateur mathematicians had signed up to the website. The research study started out as an undergraduate project in the Rothschild Scholars Technion Program for Excellence with the participation of Gal Raayoni and George Pisha, and continued as part of the research projects conducted in the Andrew and Erna Viterbi Faculty of Electrical Engineering with the participation of Shahar Gottlieb, Yoav Harris, and Doron Haviv. This is also where the most significant breakthrough was made – by an algorithm developed by Shahar Gottlieb – which led to the article’s publication in Nature. Prof. Kaminer adds that the most interesting mathematical discovery made by the Ramanujan Machine’s algorithms to date relates to a new algebraic structure concealed within a Catalan constant. The structure was discovered by high school student Yahel Manor, who participated in the project as part of the Alpha Program for science-oriented youth. Prof. Kaminer added that, “Industry colleagues Uri Mendlovic and Yaron Hadad also participated in the study, and contributed greatly to the mathematical and algorithmic concepts that form the foundation for the Ramanujan Machine. It is important to emphasize that the entire project was executed on a voluntary basis, received no funding, and participants joined the team out of pure scientific curiosity.” Prof. Ido Kaminer is the head of the Robert and Ruth Magid Electron Beam Quantum Dynamics Laboratory. He is a faculty member in the Andrew and Erna Viterbi Faculty of Electrical Engineering and the Solid State Institute, and affiliated with the Helen Diller Quantum Center and the Russell Berrie Nanotechology Institute. For more than a century, the Technion – Israel Institute of Technology has pioneered in science and technology education and delivered world-changing impact. Proudly a global university, the Technion has long leveraged boundary-crossing collaborations to advance breakthrough research and technologies. Now with a presence in three countries, the Technion will prepare the next generation of global innovators. Technion people, ideas and inventions make immeasurable contributions to the world, innovating in fields from cancer research and sustainable energy to quantum computing and computer science to do good around the world. The American Technion Society supports visionary education and world-changing impact through the Technion – Israel Institute of Technology. Based in New York City, we represent thousands of US donors, alumni and stakeholders who invest in the Technion’s growth and innovation to advance critical research and technologies that serve the State of Israel and the global good. Over more than 75 years, our nationwide supporter network has funded new Technion scholarships, research, labs, and facilities that have helped deliver world-changing contributions and extend Technion education to campuses in three countries.

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“Quantum computers promise to solve problems that conventional computers cannot, because qubits can exist in multiple states at the same time. Using this quantum physics phenomenon, qubits can perform large amounts of calculations simultaneously, which can greatly speed up the speed of solving complex problems. Quantum computers promise to solve problems that conventional computers cannot, because qubits can exist in multiple states at the same time. Using this quantum physics phenomenon, qubits can perform large amounts of calculations simultaneously, which can greatly speed up the speed of solving complex problems. Traditional computer vs quantum computer One of the differences between quantum computers and traditional computers is computing power. The former can solve a large number of operations that are difficult for traditional computers to handle. For example, given the same complex data task, a quantum computer can do it in a matter of minutes, while today’s best-performing conventional computers would take thousands of years. The key to this is the “qubits” that are the core of a quantum computer. From the perspective of quantum physics, qubits can exist in multiple states at the same time, and can perform a large number of operations several times higher than traditional computers, greatly speeding up the speed of solving complex problems. For most quantum computers, qubits must be kept operating at extremely cold temperatures close to stopping atoms from moving. As a result, qubits are often placed in a special refrigerator, also known as a “quantum refrigerator,” while other devices are placed around the quantum refrigerator. But controlling a quantum processor requires hundreds of wires to go in and out of the refrigerator, a wire design that would greatly constrain the ability of a quantum system to scale to the hundreds or thousands of qubits needed to demonstrate quantum utility, while also preventing Making qubits send and receive information is very difficult. This has also become a problem that scientists must solve in the process of advancing the development of quantum computing. However, as companies managed to increase the number of qubits in a chip, and thus the computing power of the chip, they started to run into a problem. The ultimate goal is to minimize the number of wires going into the chiller. Intel recognizes that quantum control is an integral part of the puzzle it needs to solve to develop large-scale commercial quantum systems. Intel unveils cryogenic chips At the IEEE International Electron Devices Conference in San Francisco this week, Intel Corp. unveiled a cryogenic chip designed to speed up a quantum computer they have developed in collaboration with the QuTech research group at Delft University. The chip, called Horse Ridge, one of the coldest areas in Oregon, uses specially designed transistors to provide microwave control signals for Intel’s quantum computing chips. The chip is designed to operate at 4 Kelvin, slightly above the temperature of the qubit chip itself. The company made the chip using its 22-nanometer FinFET process, although the transistors that make up the control circuitry required extensive redesign. The chip can control multiple qubits in a quantum computer, and Intel sees the development of the chip as a major milestone on the road toward a truly viable quantum computer. Intel claims that Horse Ridge lays the foundation for future controllers that will be able to control thousands or even millions of qubits, enabling the realization of quantum computers. Miniaturization is key, they claim, and it’s worth noting that miniaturization is one of Intel’s strong suits. Dealing with the Difficulties of Quantum Computers While most quantum chips and computers need to be placed at absolute zero to function properly, the Horse Ridge chip operates at about 4 degrees Kelvin, which is slightly warmer than absolute zero. Because each of these particles is individually controlled, the wiring’s ability to scale quantum computing systems to hundreds or thousands of qubits achieves remarkable performance levels. Horse Ridge SoCs use sophisticated signal processing techniques to convert instructions into microwave pulses that can manipulate the state of qubits. The solution is to put as much control and readout electronics as possible into the refrigerator, possibly even integrating them on a qubit chip. Horse Ridge integrates the control electronics onto a chip used to operate inside the refrigerator using the qubit chip. It is programmed with instructions corresponding to basic qubit operations, which are converted into microwave pulses that can control the state of the qubits. Milestones of Horse Ridge For a long time, in the race to realize the functions of quantum computers and unleash their potential, researchers have paid more attention to the manufacture of qubits, building test chips to demonstrate the powerful capabilities of a few qubits in superposition states. But early quantum hardware development at Intel, including the design, testing and characterization of silicon spin qubit and superconducting qubit systems, identified the main bottlenecks preventing quantum computing from scaling commercially: interconnect and control. Intel sees Horse Ridge as opening an “elegant solution” that allows multiple qubits to be controlled, and sets a clear path for building systems that can control more qubits in the future, an important milestone toward quantum utility . Through Horse Ridge, Intel is able to scale quantum systems to the hundreds or thousands of qubits needed to demonstrate quantum utility, and thus the millions of qubits needed to achieve commercially viable quantum solutions. Manufacturing the control chip, which is done in-house at Intel, will greatly improve the company’s ability to design, test and optimize commercially viable quantum computers. These devices are often custom-designed to control individual qubits, requiring hundreds of connecting wires to go in and out of the refrigerator. But this extensive control cable for each qubit hinders the ability to scale quantum systems to the hundreds or thousands of qubits needed to demonstrate quantum utility, let alone commercially viable quantum solutions. Millions of qubits. With Horse Ridge, Intel can radically simplify the control electronics needed to operate quantum systems. Replacing these bulky instruments with highly integrated SoCs will simplify system design and allow the use of sophisticated signal processing techniques to speed up setup times, improve qubit performance, and enable systems to efficiently scale to larger qubit counts. Microsoft and Amazon join the fray Quantum computing is a hot research field. Although we have not yet seen what a quantum computer is, IBM, Google, and Amazon are already vying for the quantum market. Quantum computing will enable most consumption to be realized through the cloud. If it can be successful, quantum computers will have a very amazing increase in computing power. Judging from the prototype pictures disclosed by various companies, they are all huge and controlled by hundreds of wires. A behemoth with dense lines. Of course, other quantum computing companies with massive qubit numbers are working on the same problem. Earlier this year, Google described some ideas for a cryogenic control circuit for its machines. In short, Intel’s breakthroughs are very helpful for them to launch higher-integrated quantum chips. ・Microsoft announced a cloud computing service called Azure Quantum at its Ignite conference in November; it integrates Microsoft’s previously released quantum programming tools with cloud services, allowing coders to work on simulated quantum hardware or on real quantum computers Run quantum code. ・Amazon unveiled a preview of Amazon Braket at AWS re:Invent in December; it also said that the creation of the “AWS Quantum Computing Center,” a physics lab near Caltech, is underway Bringing together the world’s leading quantum computing researchers and engineers to accelerate the development of quantum computing hardware and software. Quantum computing currently faces many challenges, one of which is that superconducting qubits only really work at temperatures close to absolute zero. Both Google and IBM required bulky control and cooling systems to develop quantum computing, some tubes larger than a human being, and hundreds of wires to connect to external microwave transmitters. Despite the great emphasis placed on the qubits themselves, the ability to control multiple qubits simultaneously has been an industry challenge. Intel recognizes that quantum control is one of the most pressing challenges for us to develop large-scale commercial quantum computing systems. That’s why we’re investing in quantum error correction and control. Competitors to Intel, Google and IBM, are primarily focused on superconducting qubits, the quantum computing systems driven by them that need to operate in the millikelvin range, just a tad above absolute zero. But Intel believes that silicon spin qubits have the potential to work at higher temperatures, about 1 Kelvin, in hopes of enabling differentiated competition. Given that Intel once tried to recreate the leadership of computer chips in the field of mobile chips, poured years of effort and huge sums of money, but it still ended in failure, now calling Horse Ridge a disruptive achievement and a “killer” that surpassed Google and IBM. It’s too early.

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NIST's Atomic Clocks All clocks must have a regular, constant or repetitive process or action to mark off equal increments of time. Examples include the daily movement of the sun across the sky, a swinging pendulum or vibrating crystal. In the case of atomic clocks, the beat is kept by a transition between two energy levels in an atom. NIST-F1 and NIST-F2 are microwave clocks, based on a particular vibration in cesium atoms of about 9 billion cycles per second. Optical atomic clocks are based on ions or atoms vibrating at optical frequencies (visible, ultraviolet or infrared light), which are about 100,000 times higher than microwave frequencies. Because optical clocks divide time into smaller units—like a ruler with finer tick marks—they ultimately could be perhaps 100 times more accurate and stable than microwave clocks. Higher frequency is one of the features enabling improved accuracy and stability. One key advance making optical atomic clocks possible was the development of frequency combs at JILA, NIST and elsewhere. Frequency combs link optical frequencies to lower frequencies that can be correlated with microwave standards and counted. NIST's first all-optical atomic clock, and the best in the world for several years, was based on a single mercury ion. Its performance was then surpassed by NIST's quantum logic clock, based on a single aluminum ion. This clock got its nickname because it borrows techniques from experimental quantum computing. Aluminum is insensitive to changes in magnetic and electric fields and temperature, making it a great ion for atomic clocks, but it wasn't practical until NIST developed new quantum computing technologies. NIST and JILA are leaders in the development of so-called optical lattice clocks. These clocks trap thousands of heavy metal atoms in an "optical lattice" formed by intersecting laser beams. Research clocks at NIST use ytterbium atoms and JILA research clocks use strontium atoms. Thanks to the presence of so many atoms, these clocks offer the advantages of strong signals and parallel processing. In addition, the atoms are held virtually still in the lattice, reducing errors from atomic motion and collisions that otherwise would need to be corrected. Optical lattice clocks are rapidly improving, and continue to set new performance records so often that it is difficult to keep track of the latest records. Both the JILA strontium and NIST ytterbium optical lattice clocks are rapidly advancing in stability. And now, for the first time in decades, a single type of atomic clock, an optical lattice clock, simultaneously holds the records for both precision and stability – and it is likely optical lattice clock performance will continue to significantly improve. This rapid improvement in optical lattice clocks at JILA and NIST results from key scientific breakthroughs. One has been the development of extremely stable lasers, including the world's most stable laser at JILA. Another key breakthrough has been development of new theories about how atoms trapped in the optical lattices interact, and application of these theories to significantly reduce the uncertainties in optical lattice clocks. And much of the improvement results from the hard and creative work of many scientists, students and postdoctoral fellows to continually find new ways to make a series of many small improvements in clock performance. NIST also has demonstrated a calcium atomic clock that is extremely stable for short time periods. This clock has the potential to be made portable, making it attractive for commercial applications. Evaluating Atomic Clock Performance Accuracy refers to a clock's capability to measure the accepted value of the frequency at which the clock atoms vibrate, or resonate. Accuracy is crucial for time measurements that must be traced to primary standards such as NIST-F1 and NIST-F2. Technical terms for accuracy include "systematic uncertainty" or "fractional frequency uncertainty"—that is, how well scientists can define shifts from the true frequency of an atom with confidence. Cesium standards like NIST-F1 and NIST-F2 are the ultimate "rulers" for time because the definition of the SI second is based on the cesium atom. More specifically, the SI unit of frequency, the Hertz, is defined internationally by the oscillations of a cesium atom. Officially, no atomic clock can be more accurate than the best cesium clock by definition. That is, only a direct measurement of the particular cesium transition can be considered the ultimate measurement of accuracy, and all other (non-cesium) clocks can only be compared to the accuracy of a cesium clock. This is partly a semantic issue. If after further development and testing the definition of the second (or Hertz) were changed to be based on the strontium atom transition, for example, the NIST/JILA strontium atom lattice clock would become the most accurate clock in the world. To get around this measurement hurdle, NIST scientists evaluate optical atomic clocks by comparing them to each other (to obtain a ratio, or relative frequency, for which there is no official unit), and by measuring all deviations from the true resonant frequency of the atom involved, carefully accounting for all possible perturbations such as magnetic fields in the environment. The optical clock performance is also directly compared to the NIST-F1 standard. For several years both NIST ion clocks have had measured relative uncertainties much smaller than NIST-F1's. (In general literature, NIST sometimes uses the term "precise" to describe the performance of optical clocks, because it less technical and has a more positive connotation than uncertainty. Precision implies that repeated measurements fall within a particular error spread around a given value. In everyday definitions of precision, this value is not necessarily the "correct" one—you can be precise without necessarily being accurate. However, in the context of optical clocks, NIST uses precision specifically to mean the spread around the true or accepted value for the atom's resonant frequency.) Stability is another important metric for evaluating atomic clocks. NIST defines stability as how precisely the duration of each clock tick matches every other tick. Because the ticks of any atomic clock must be averaged for some period to provide the best results, a key benefit of high stability is that optimal results can be achieved very quickly. Stability is not traceable to a time standard, but in many applications stability is more important than absolute accuracy. For example, most communications and GPS positioning applications depend on synchronization of different clocks, requiring stability but not necessarily the greatest accuracy. (Other common terms for stability include precision.) The optical lattice clocks at NIST and JILA are much more stable than NIST-F1. NIST-F1 must be averaged for about 400,000 seconds (about five days) to achieve its best performance of about 1 second in 100 million years. In contrast, the ytterbium and strontium lattice clocks reach that level of performance in a few seconds of averaging, and after a few hours of averaging are about 100 times more stable than NIST-F1. NIST scientists are also working to improve the portability of next-generation atomic clocks for applications outside the laboratory.

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In the early days of research on black holes, before they even had that name, physicists did not yet know if these bizarre objects existed in the real world. They might have been a quirk of the complicated math used in the then still young general theory of relativity, which describes gravity. Over the years, though, evidence has accumulated that black holes are very real and even exist right here in our galaxy. Today another strange prediction from general relativity—wormholes, those fantastical sounding tunnels to the other side of the universe—hang in the same sort of balance. Are they real? And if they are out there in our cosmos, could humans hope to use them for getting around? After their prediction in 1935, research seemed to point toward no—wormholes appeared unlikely to be an element of reality. But new work offers hints of how they could arise, and the process may be easier than physicists have long thought. The original idea of a wormhole came from physicists Albert Einstein and Nathan Rosen. They studied the strange equations that we now know describe that unescapable pocket of space we call a black hole and asked what they really represented. Einstein and Rosen discovered that, theoretically at least, a black hole’s surface might work as a bridge that connected to a second patch of space. The journey might be as if you went down the drain of your bathtub, and instead of getting stuck in the pipes, you came out into another tub just like the first. Subsequent work expanded this idea but turned up two persistent challenges that prevent the formation of easily spotted, humanly usable wormholes: fragility and tininess. First, it turns out that in general relativity, the gravitational attraction of any normal matter passing through a wormhole acts to pull the tunnel shut. Making a stable wormhole requires some kind of extra, atypical ingredient that acts to keep the hole open, which researchers call “exotic” matter. Second, the kinds of wormhole-creating processes that scientists had studied rely on effects that could prevent a macroscopic traveler from entering. The challenge is that the process that creates the wormhole and the exotic matter that stabilizes it cannot stray too far from familiar physics. “Exotic” does not mean physicists can dream up any sort of stuff that gets the job done on paper. But so far, familiar physics has delivered only microscopic wormholes. A bigger wormhole seems to require a process or type of matter that is both unusual and believable. “That’s the delicacy,” says Brianna Grado-White, a physicist and wormhole researcher at Brandeis University. A breakthrough occurred in late 2017, when physicists Ping Gao and Daniel Jafferis, both then at Harvard University, and Aron Wall, then at the Institute for Advanced Study in Princeton, N.J., discovered a way to prop open wormholes with quantum entanglement—a kind of long-distance connection between quantum entities. The peculiar nature of entanglement allows it to provide the exotic ingredient needed for wormhole stability. And because entanglement is a standard feature of quantum physics, it is relatively easy to create. “It’s really a beautiful theoretical idea,” says Nabil Iqbal, a physicist at Durham University in England, who was not involved in the research. Though the method helps to stabilize wormholes, it can still deliver only microscopic ones. But this new approach has inspired a stream of work that uses the entanglement trick with different sorts of matter in the hopes of bigger, longer-lasting holes. One easy-to-picture idea comes from a preprint study by Iqbal and his Durham University colleague Simon Ross. The two tried to see if they could make the Gao-Jafferis-Wall method produce a large wormhole. “We thought it would be interesting, from a sci-fi point of view, to push the limits and see whether this thing could exist,” Iqbal says. Their work showed how special disturbances within the magnetic fields surrounding a black hole could, in theory, generate stable wormholes. Unfortunately, the effect still only forms microscopic wormholes, and Iqbal says it is highly unlikely the situation would occur in reality. Iqbal and Ross’s work highlights the delicate part of wormhole construction: finding a realistic process that does not require something added from way beyond the bounds of familiar physics. Physicist Juan Maldacena of the Institute for Advanced Study, who had suggested connections between wormholes and entanglement back in 2013, and his collaborator Alexey Milekhin of Princeton University have found a method that could produce large holes. The catch in their approach is that the mysterious dark matter that fills our universe must behave in a particular way, and we may not live in a universe anything like this. “We have a limited toolbox,” Grado-White says. “To get something to look the way we need it, there’s only so many things we can do with that toolbox.” The boom in wormhole research continues. So far, nothing like a made-to-order human-sized wormhole machine looks likely, but the results do show progress. “We’re learning that we can, in fact, build wormholes that stay open using simple quantum effects,” Grado-White says. “For a very long time, we didn’t think these things were possible to build—it turns out that we can.”

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Whether it’s an app, a software feature, or an interface element, programmers possess the magical ability to create something new out of virtually nothing. Just give them the hardware and a coding language, and they can spin up a program. But what if there was no other software to learn from, and computer hardware didn’t yet exist? Welcome to the world of Ada Lovelace, the 19th-century English writer and mathematician most famous for being popularly described as the world’s first computer programmer — and all approximately one full century before the creation of the first programmable, electronic, general-purpose digital computers. Lovelace only lived to the age of 36, but did enough during her short life to more than cement her legacy in the history of computing. (In Steve Jobs biographer Walter Isaacson’s book The Innovators, she is the title of chapter one: ground zero of the tech revolution.) Working with the English polymath Charles Babbage on his proposed mechanical general-purpose computer, the Analytical Engine, Lovelace recognized its potential for more than just calculation. This conceptual leap, seeing the manipulation of digits as being not simply the key to faster math, underpins most of what has followed in the world of computation. “To Babbage, the Engine was little more than a big calculating machine,” Christopher Hollings, Departmental Lecturer in Mathematics and its History at the Mathematical Institute of the University of Oxford, and co-author of Ada Lovelace: The Making of a Computer Scientist, told Digital Trends. “But Lovelace seems to have recognized that its programmable nature might mean that it would be capable of much more, that it might be able to do creative mathematics, or even compose original music. The fact that she was speculating about the capabilities of a machine that never existed, in combination with the fact that her comments tally with what we now know of computing, is what has given her writings modern interest.” Hollings said that there is a popular myth that Ada Lovelace was pushed into studying math by her mother to divert her from any “dangerous poetical tendencies” that she might have inherited from her absentee father, the Romantic poet Lord Byron. (Who, like his daughter, tragically died at the age of 36.) However, he noted, the truth is likely to be “much more prosaic — and interesting” than that. “Lady Byron had, unusually for a woman at that time, been educated in mathematics in her youth, had enjoyed it, and wanted to pass that on to her own daughter,” Hollings explained. “And I think the desire to study mathematics is the strongest influence on what Lovelace did in computing. From the mid-1830s, she was determined to learn higher mathematics and she put in years of work in order to do so, and this led directly into her collaboration with Babbage.” Lovelace’s insights into computing included hypothesizing about the concept of a computer able to be programmed and reprogrammed to perform limitless activities; seeing the potential for storing, manipulating, processing, and acting upon anything — from words to music — that could be expressed in symbols; describing one of the first step-by-step computer algorithms, and — finally — posing the question of whether a machine can ever truly think (she believed not). As such, while her work concerned hardware that never appeared during her lifetime, she nonetheless laid crucial foundational steps. Lovelace served as a first in another important way: One of the first tragic stories in the history of computing. Beyond the “notes” (some 19,136 words in total) she wrote in connection to Babbage’s Analytical Engine, she never published another scientific paper. As noted, she also died young, of uterine cancer, after several turbulent years, including a toxic relationship and problems with opiates. These have shaped several of the previous popular tellings of her story — although this is now changing. “Much of the interest in the past has been more to do with who her father was, and the romantic idea of an unconventional aristocrat,” Hollings said. “Lurid tales of adultery, gambling, and drug addiction have also been thrown into the mix, probably in a way that they would not have been — certainly not with the same emphasis — if the discussion were about a man.” Nonetheless, today Lovelace is widely viewed as both a feminist icon and a computing pioneer. She is frequently referenced in history books, has multiple biographies dedicated to exploring her life, and is namechecked in various places — whether that’s the naming of Ada, a programming language developed by the U.S. Defense Department, or of internal cryptocurrency used by the Cardano public blockchain. In all, she’s one of the most famous names in her field and, while her untimely death means there will continue to be debate around what she did or didn’t contribute, Ada Lovelace has more than cemented her place in history. And with people continuing to probe questions like whether or not a machine could ever achieve sentience, don’t expect that to change any time soon. - Nvidia’s next GPUs will be designed partially by AI - The UX Pioneer: Louisa Heinrich’s quest to humanize tech - How Hedy Lamarr built the foundations of Wi-Fi 80 years ago - Nvidia RTX 4090 could be twice as powerful as the RTX 3090 - Researchers create ‘missing jigsaw piece’ in development of quantum computing

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One of the hottest topics of conversation among the C-suite has been quantum computing. Unlike traditional computers, businesses use quantum computers to complete complex tasks faster and more efficiently. This is mainly due to their extraordinary ability to compute and analyze large volumes of data. Indeed, Google recently made news by claiming quantum supremacy, saying that its computers can execute tasks that a classical computer cannot. Many other giant firms are boasting about their lightning-fast supercomputers and opting for AI/ML consulting firms. However, we are curious as to what quantum computing is and what applications it has in the actual world. We’ll go over all of this in this post, as well as some of the most practical quantum computing applications. What Is Quantum Computing? Quantum computing is a branch of computing that focuses on developing computer technology based on quantum theory’s concepts. Quantum computers utilize quantum physics’ distinctive properties, such as superposition, entanglement, and quantum interference, in computing. This method introduces new concepts to traditional programming methods. Although quantum computing has obvious scalability and incoherence issues, it allows for several simultaneous operations and eliminates the tunnel effect. How Does Quantum Computing Work? The qubit rather than the conventional bit serves as the fundamental unit of information in quantum computing. Basically, a quantum computer comprises three major components: an area that houses the qubits, a means for sending signals to the qubits, and a classical computer that runs a program and sends instructions. The key feature of this advanced system is that it allows for the coherent superposition of ones and zeros. Classical computers can only encode data in bits with values of 1 or 0, severely limiting their possibilities. In contrast, Quantum computing makes use of quantum bits, also known as qubits. It takes advantage of subatomic particles’ unusual capacity to exist in many states, such as using 1 and 0 at the same time. As a result, instead of 1s or 0s, quantum computers execute computations based on the probability of an object’s condition. This means that they can process exponentially more data than traditional computers. To maximize coherence and minimize interference, the unit that holds qubits is kept at a temperature just above absolute zero. This is applicable in some techniques of qubit storage. Other forms of qubit housing use a vacuum chamber to help minimize vibrations and stabilize the qubits. Quantum Computer Uses And Application Areas Although a quantum computer cannot perform all tasks quicker than a classical computer, there are a few areas where quantum computers have the potential to have a significant effect. Here are some of its best applications. Quantum simulators are machines that exploit quantum effects to actively answer queries about model systems and, through them, real systems. Because quantum computers utilize quantum phenomena in their computing, they are particularly good at mimicking other quantum systems. Quantum simulation can be approached from both a theoretical and an experimental standpoint, paving the path for new discoveries. Understanding the precise quantum dynamics of chemical reactions, for example, can have enormous environmental benefits. We could develop technologies that are faster and more energy-efficient. Due to the increasing amount of cyber-attacks that occur on a daily basis around the world, the online security environment has become rather vulnerable. Despite the fact that organizations are instituting the necessary security standards, traditional digital systems find the process challenging and unfeasible. As a result, cybersecurity has remained a major worry all over the world. We are becoming even more vulnerable to these risks as our reliance on technology grows. Quantum computing, along with machine learning, can aid in the development of various strategies to combat these cyber threats. The intractability of problems like integer factorization is used in conventional cryptography, which is commonly used to safeguard data transfer. Many of these problems could be solved more quickly with quantum computers. Additionally, Quantum computing can also aid in the development of encryption systems, commonly known as quantum cryptography. Quantum computers have unique properties that make them potentially more effective at addressing complicated optimization issues. This is accomplished by using the quantum property of superposition to represent all possible answers and identifying economic & impactful solutions. For these issues, traditional approaches have either exponentially increasing compute times or sub-optimal performance. Quantum optimization methods, such as quantum approximate optimization algorithms, promise to provide answers that improve on sub-optimal solutions without requiring exponentially larger computation durations. As a result, we can identify solutions that were previously unthinkable by using quantum-inspired optimization methods. Web Pages Ranking A quantum algorithm discovered in 1996 dramatically speeds up the solution to unstructured data searches by running the search in fewer steps than any other method. It’s thought that a quantum computer could rank the most important Web pages faster than traditional computers and that this quantum speedup would improve as the number of pages to rank grew. Furthermore, according to many researchers, top AI development companies, and AI/ML consulting firms, a quantum computer will be able to spit out a yes-or-no answer 10 times faster than a traditional computer when evaluating whether the Web’s page rankings should be changed. Data security, optimization, and searches will all be altered by quantum computers. Despite the fact that quantum computers will be able to crack many of today’s encryption techniques, it is likely that they will develop hack-proof alternatives. Quantum computing differs from regular computing in how it operates and what are its uses. The race is obviously on, even though a genuine quantum computer is still a long way off. Quantum computers aren’t meant to be a replacement for traditional computers; rather, they’re supposed to be an additional tool for tackling specific challenges. As a result, quantum computing has significantly increased in power and can now be utilized for large-scale data processing and simulations. If you have any concerns regarding how quantum computing can affect your business or how to get started, please contact us at Ksolves, the top AI development company.

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The materials could open up possibilities for a new kind of devices based on spintronics, which makes use of a characteristic of electrons called spin, instead of using their electrical charge the way electronic devices do. It could also allow for much faster control of existing technologies such as magnetic data storage. Topological insulators are materials that possess paradoxical properties. The three-dimensional bulk of the material behaves just like a conventional insulator (such as quartz or glass), which blocks the movement of electric currents. Yet the material’s outer surface behaves as an extremely good conductor, allowing electricity to flow freely. The key to understanding the properties of any solid material is to analyze the behavior of electrons within the material — in particular determining what combinations of energy, momentum and spin are possible for these electrons, explains MIT assistant professor of physics Nuh Gedik, senior author of two recent papers describing the new findings. This set of combinations is what determines a material’s key properties — such as whether it is a metal or not, or whether it is transparent or opaque. “It’s very important, but it’s very challenging to measure,” Gedik says. The traditional way of measuring this is to shine a light on a chunk of the solid material: The light knocks electrons out of the solid, and their energy, momentum and spin can be measured once they are ejected. The challenge, Gedik says, is that such measurements just give you data for one particular point. In order to fill in additional points on this landscape, the traditional approach is to rotate the material slightly, take another reading, then rotate it again, and so on — a very slow process. Gedik and his team, including graduate students Yihua Wang and James McIver, and MIT Pappalardo postdoctoral fellow David Hsieh, instead devised a method that can provide a detailed three-dimensional mapping of the electron energy, momentum and spin states all at once. They did this by using short, intense pulses of circularly polarized laser light whose time of travel can be precisely measured. By using this new technique, the MIT researchers were able to image how the spin and motion are related, for electrons travelling in all different directions and with different momenta, all in a fraction of the time it would take using alternative methods, Wang says. This method was described in a paper by Gedik and his team that appeared Nov. 11 in the journal Physical Review Letters. In addition to demonstrating this novel method and showing its effectiveness, Gedik says, “we learned something that was not expected.” They found that instead of the spin being precisely aligned perpendicular to the direction of the electrons’ motion, when the electrons moved with higher energies there was an unexpected tilt, a sort of warping of the expected alignment. Understanding that distortion “will be important when these materials are used in new technologies,” Gedik says. The team’s high-speed method of measuring electron motion and spin is not limited to studying topological insulators, but could also have applications for studying materials such as magnets and superconductors, the researchers say. One unusual characteristic of the way electrons flow across the surface of these materials is that unlike in ordinary metal conductors, impurities in the material have very little effect on the overall electrical conductivity. In most metals, impurities quickly degrade the conductivity and thus hinder the flow of electricity. This relative imperviousness to impurities could make topological insulators an important new material for some electronic applications, though the materials are so new that the most important applications may not yet be foreseen. One possibility is that they could be used for transmission of electrical current in situations where ordinary metals would heat up too much (because of the blocking effect of impurities), damaging the materials. In a second paper, appearing today in the journal Nature Nanotechnology, Gedik and his team show that a method similar to the one they used to map the electron states can also be used to control the flow of electrons across the surface of these materials. That works because the electrons always spin in a direction nearly perpendicular to their direction of travel, but only electrons spinning in a particular direction are affected by a given circularly polarized laser beam. Thus, that beam can be used to push aside all of the electrons flowing in one direction, leaving a usable electric current flowing the other way. “This has very immediate device possibilities,” Gedik says, because it allows the flow of current to be controlled completely by a laser beam, with no direct electronic interaction. One possible application would be in a new kind of electromagnetic storage, such as that used in computer hard drives, which now use an electric current to “flip” each storage bit from a 0 to a 1 or vice versa. Being able to control the bits with light could offer a much quicker response time, the team says. This harnessing of electron behavior could also be a key enabling technology that could lead to the creation of spintronic circuits, using the spin of the electrons to carry information instead of their electric charge. Among other things, such devices could be an important part of creating new quantum computing systems, which many researchers think could have significant advantages over ordinary computers for solving certain kinds of highly complex problems. Professor of physics Zhi-Xun Shen of Stanford University, who was not involved in this work, says the MIT team has confirmed the theorized structure of the topological surface by using their novel experimental method. In addition to this confirmation, he says, their second paper “is to date one of the most direct experimental evidences for optical coupling” between the laser and the surface currents, and thus “has interesting potential for opto-spintronics.”

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You probably don’t realize it, but you very likely already employ quantum technology on a regular basis. Get in your car, switch on Waze or Google Maps, and you are already harnessing quantum effects. A GPS receiver works by measuring the tiny time delays in signals from multiple satellites separated in space. Doing this requires very stable and very accurate time measurement: enter the atomic clock. Such clocks, which reside inside every GPS satellite, often use quantum superposition. They employ atoms of Cesium or Rubidium to achieve an extremely stable “tick,” one accessible only within the atoms themselves. The primary standard for time, operated using this kind of physics, is so stable that it will lose just one second in 100 million years. That kind of stability powers not just GPS but other systems as well, including the synchronization protocols that govern Internet operations. A clock that loses just a second in 100 million years (or more) may sound like more than we need, but this early application of quantum technology represents the start of something much bigger - building a new generation of quantum-enhanced sensors. Quantum sensors turn the inherent weakness of quantum technology - its instability against the environment - into a strength. It takes a huge amount of work to isolate a quantum system in a way that allows it to be used faithfully as a clock. In general these devices are REALLY sensitive to everything around them; the most sensitive experiments to date have shown that such clocks can measure the effect of lifting the clock by a bit more than one foot (gravity changes as you move away from the center of the Earth). But quantum sensors deliver more than just sensitivity - quantum sensors also give the benefit of stability over long times. Conventional sensor instruments slowly change over time, meaning that averaging longer to reduce measurement noise becomes impossible. But because quantum sensors use immutable quantities - like the structure of atoms - their measurements tend to be very stable over long times. Let’s explore one exciting kind of quantum sensor based on the same core technology as used in atomic clocks - cold trapped atoms. Cold atoms can be exploited for ultra-sensitive interferometric measurements using the wavelike nature of matter. Instead of building interferometers with light reflected off of (matter-based) mirrors (as widely used in telecom optical modulators), one can build atom interferometers using matter “reflected”off of pulses of light. Such atom interferometers have the benefit that the atoms themselves have mass, making them sensitive to both gravity and general acceleration. Accordingly, there is an emerging area of work on quantum-enabled “PNT” or positioning, navigation, and timing. Here, atomic accelerometers may enable dead reckoning navigation in environments such as space or GPS-denied battlefields. More broadly, leveraging these capabilities and advantages, atomic devices are routinely used for both magnetometry and gravimetry. They could thus be deployed by military personnel to detect underground, hardened structures, submarines, or hidden weapons systems. Imagine a detector which can measure via changes in gravity whether a mountain is being hollowed out in a hostile nation with a furtive weapons program. In civilian applications these devices form the basis of new ways to monitor the climate - from underground aquifer levels through to ice-sheet thickness. Totally new forms of Earth observation for the space sector are now emerging, enabled by new small-form quantum sensors. Those capabilities flow into new data streams for long-term weather forecasting and insurance against weather events in agriculture. And of course the mining industry has long relied on advanced instrumentation for improved aerial survey and productivity enhancement. Of course trapped atoms aren’t the only technology relevant to quantum sensing. There’s been a huge amount of research showing how solid-state devices like imperfections in diamond can be used as sensitive magnetometers. These have the advantage that they can be used in biological environments - even in vivo. They may not be as sensitive as atomic devices, but by virtue of their tiny size they can access new applications that are not possible with trapped atoms. Overall, quantum sensing provides a route to gain massive technological advantages well before quantum computing comes online. And if you aren’t sure how much impact quantum sensors may have, just take a step back and think about how atomic clocks and GPS have already shaped your daily life. Q-CTRL’s work in quantum sensing Q-CTRL is active across all applications of quantum technology, producing the fundamental enabling capabilities in quantum control to help our customers realize the true potential of quantum tech. But with quantum sensors we go one step further, taking a “software-first” approach to building and designing our own hardware powered by quantum control. Placing the advantages of quantum control front and center enables huge reductions in system size and improvements in noise rejection, ultimately unlocking totally new applications. We’re excited to be building a new generation of atomic navigation systems for space exploration and terrestrial applications. And we’re thrilled to have assembled one of the most impressive teams of quantum sensing experts in the world. Partially adapted from The Leap into Quantum Technology: A Primer for National Security Professionals

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Posts showcasing the wonder, beauty, and potential of cutting-edge materials research—freely contributed by physicists from across the country. (Funsize Physics is not responsible for any minds that are blown.) You may have heard that there are three main phases of matter: solids, liquids, and gases (plus plasma if you want to get fancy). Liquids can take virtually any shape and deform instantly. Solid materials possess interesting electronic and magnetic properties essential to our daily life. But how about designing rigid liquids with magnetic properties? Impossible? Not anymore. Click to learn more! Instead of pencil, paper, and eraser, we can use combinations of lasers and magnetic materials to write, read, and and erase information by varying the temperature and magnetic field. Here we apply our laser "pencil" to magnetic "paper" to write the letter “N” (Go Huskers!!). This technique allows us write, erase, and rewrite tiny magnetic memories like those found in your computer hard drive and other devices. Click to learn how it works! It’s a hot summer day. You desperately want something cold to drink, but unfortunately, your bottle of root beer has been sitting in a hot car all day. You put it into a bucket full of ice to cool it down. But it’s taking forever! How, you wonder, could you speed the process up? The same question is important for understanding how electronic devices work, and how we can make them work better by controlling the temperature of the electrons that power them. Read on to find out what a bottle of root beer in a cooler full of ice and a nanowire in a vat of liquid helium have in common! Diodes, also known as rectifiers, are a basic component of modern electronics. As we work to create smaller, more powerful and more energy-efficient electronic devices, reducing the size of diodes is a major objective. Recently, a research team from the University of Georgia developed the world's smallest diode using a single DNA molecule. This diode is so small that it cannot be seen by conventional microscopes. We think we're pretty familiar with how ordinary liquids behave, but it turns out that some of the basic things we know are no longer true when we look at these liquids on short enough length scales and fast enough time scales. The liquids start to behave more like solids, pushing back when you push on them, and slipping across solid surfaces instead of being dragged along. Click to ride the tiny-but-mighty new wave of nanofluidics! Scientists are working to develop electronic devices that store and process information by manipulating a property of electrons called spin—a research area aptly known as spintronics. The semiconductors we are developing will not only be faster and cheaper than those used in conventional devices, but will also have more functionality. Materials that are absolutely perfect—in other words, materials that contain no defect of any kind—are usually not very interesting. Imagine being married to a saint: you would quickly be bored out of your mind! Defects and impurities can considerably change many properties of materials in ways that allow a wide range of applications. Semiconductors are materials with properties intermediate between metals and non-conducting insulators, defined by the amount of energy needed to make an electron conductive in the material. The non-conducting electrons occupy a continuum of energy states, but two of these states (the “heavy hole” and “light hole”) are nearly identical in energy. The heavy hole is easy to observe and study, but the light hole eludes most observers. Solids are generally divided into metals, which conduct electricity, and insulators, which do not. Some oxides straddle this boundary, however: a material's structure and properties suggest it should be a metal, but it sometimes behaves as an insulator. Researchers at the University of California, Santa Barbara are digging into the mechanisms of this transformation and are aiming to harness it for use in novel electronic devices. You may know that the media used in magnetic recording technologies, such as computer hard drives, are made of millions of tiny nanomagnets. Each nanomagnet can be switched up or down to record bits of information as ones and zeros. These media are constantly subjected to magnetic fields in order to write, read, and erase information. If you have ever placed a magnet too close to your laptop or cell phone, you know that exposure to an external magnetic field can disrupt information stored this way. Did you know that it is possible for the nanomagnets to "remember" their previous state, if carefully manipulated under specific magnetic field and temperature conditions? Using a kind of memory called topological magnetic memory, scientists have found out how to imprint memory into magnetic thin films by cooling the material under the right conditions. Inside solids, the properties of photons can be altered in ways that create a kind of "artificial gravity" that affects light. Researchers at the University of Pittsburgh tracked photons with a streak camera and found that whey they enter a solid-state structure, they act just like a ball being thrown in the air: they slow down as they move up, come to a momentary stop, and fall back the other way. Studying this "slow reflection" will allow us to manipulate light's behavior, including its speed and direction, with potential applications in telecommunications and quantum computing technologies. In a unique state of matter called a superfluid, tiny "tornadoes" form that may play an important role in nanotechnology, superconductivity, and other applications. Just as tornadoes are invisible air currents that become visible when they suck debris into their cores, the quantum vortices in superfluids attract atoms that make the vortices visible. Quantum vortices are so small they can only be imaged using very short-wavelength x-rays, however. Would you rather have data storage that is compact or reliable? Both, of course! Digital electronic devices like hard drives rely on magnetic memory to store data, encoding information as “0”s and “1”s that correspond to the direction of the magnetic moment, or spin, of atoms in individual bits of material. For magnetic memory to work, the magnetization should not change until the data is erased or rewritten. Unfortunately, some magnetic materials that are promising for high density storage have low data stability, which can be improved by squeezing or stretching the crystal structures of magnetic memory materials, enhancing a material property called magnetic anisotropy. Neutron radiation detection is an important issue for the space program, satellite communications, and national defense. But since neutrons have no electric charge, they can pass through many kinds of solid objects without stopping. This makes it difficult to build devices to detect them, so we need special materials that can absorb neutrons and leave a measurable signature when they do. Researchers at the University of Nebraska-Lincoln are studying the effects of solar neutron radiation on two types of materials on the International Space Station (ISS), using detectors made of very stable compounds that contain boron-10 and lithium-6. To increase our use of solar energy, we need to create more efficient, stable, and cost-effective solar cells. What if we could use an inkjet printer to fabricate them? A new type of solar cell uses a class of materials called perovskites, which have a special crystal structure that interacts with light in a way that produces an electric voltage. We've developed a method to produce perovskite thin films using an inket printer, which in the future could pave the way to manufacture solar cells that are surprisingly simple and cheap. Fool's gold is a beautiful mineral often mistaken for gold, but recent research shows that its scientific value may be great indeed. Using a liquid similar to Gatorade, it can be turned into a magnet at the flick of a switch! Read on to learn more! Think of the hard disk in your computer. Information is stored there in the form of magnetic "bits." But do you know how small a magnet can be? Some molecules make magnetic magic, and these special molecules may give rise to the ultrafast, high precision, low power devices of the future. For the past two decades, giant bubble enthusiasts have been creating soap film bubbles of ever-increasing volumes. As of 2020, the world record for a free-floating soap bubble stands at 96.27 cubic meters, a volume equal to about 25,000 U.S. gallons! For a spherical bubble, this corresponds to a diameter of more than 18 feet and a surface area of over 1,000 square feet. How are such large films created and how do they remain stable? What is the secret to giant bubble juice? Click to find out more! How can you fabricate a huge number of nanostructures in a split second? Self-assembly is a fast technique for the mass production of materials and complex structures. But before self-assembly is ready for prime time, scientists need to establish ways to control this process, so that desired nanostructures emerge from the unstructured soup of basic building blocks that are fast-moving atoms and molecules. Superconductors are materials that permit electrical current to flow without energy loss. Their amazing properties form the basis for MRI (magnetic resonance imaging) devices and high-speed maglev trains, as well as emerging technologies such as quantum computers. At the heart of all superconductors is the bunching of electrons into pairs. Click the image to learn more about the "dancing" behavior of these electron pairs!

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Google's quantum beyond-classical experiment used 53 noisy qubits to demonstrate it could perform a calculation in 200 seconds on a quantum computer that would take 10,000 years on the largest classical computer using existing algorithms. This marks the beginning of the Noisy Intermediate-Scale Quantum (NISQ) computing era. In the coming years, quantum devices with tens-to-hundreds of noisy qubits are expected to become a reality. Quantum computing relies on properties of quantum mechanics to compute problems that would be out of reach for classical computers. A quantum computer uses qubits. Qubits are like regular bits in a computer, but with the added ability to be put into a superposition and share entanglement with one another. Classical computers perform deterministic classical operations or can emulate probabilistic processes using sampling methods. By harnessing superposition and entanglement, quantum computers can perform quantum operations that are difficult to emulate at scale with classical computers. Ideas for leveraging NISQ quantum computing include optimization, quantum simulation, cryptography, and machine learning. Quantum machine learning Quantum machine learning (QML) is built on two concepts: quantum data and hybrid quantum-classical models. Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer, like the samples gathered from the Sycamore processor for Google’s demonstration of quantum supremacy. Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. The quantum supremacy experiment showed it is possible to sample from an extremely complex joint probability distribution of 2^53 Hilbert space. The quantum data generated by NISQ processors are noisy and typically entangled just before the measurement occurs. Heuristic machine learning techniques can create models that maximize extraction of useful classical information from noisy entangled data. The TensorFlow Quantum (TFQ) library provides primitives to develop models that disentangle and generalize correlations in quantum data—opening up opportunities to improve existing quantum algorithms or discover new quantum algorithms. The following are examples of quantum data that can be generated or simulated on a quantum device: - Chemical simulation —Extract information about chemical structures and dynamics with potential applications to material science, computational chemistry, computational biology, and drug discovery. - Quantum matter simulation —Model and design high temperature superconductivity or other exotic states of matter which exhibits many-body quantum effects. - Quantum control —Hybrid quantum-classical models can be variationally trained to perform optimal open or closed-loop control, calibration, and error mitigation. This includes error detection and correction strategies for quantum devices and quantum processors. - Quantum communication networks —Use machine learning to discriminate among non-orthogonal quantum states, with application to design and construction of structured quantum repeaters, quantum receivers, and purification units. - Quantum metrology —Quantum-enhanced high precision measurements such as quantum sensing and quantum imaging are inherently done on probes that are small-scale quantum devices and could be designed or improved by variational quantum models. Hybrid quantum-classical models A quantum model can represent and generalize data with a quantum mechanical origin. Because near-term quantum processors are still fairly small and noisy, quantum models cannot generalize quantum data using quantum processors alone. NISQ processors must work in concert with classical co-processors to become effective. Since TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is used as the base platform to experiment with hybrid quantum-classical algorithms. A quantum neural network (QNN) is used to describe a parameterized quantum computational model that is best executed on a quantum computer. This term is often interchangeable with parameterized quantum circuit (PQC). A goal of TensorFlow Quantum is to help discover algorithms for the NISQ-era, with particular interest in: - Use classical machine learning to enhance NISQ algorithms. The hope is that techniques from classical machine learning can enhance our understanding of quantum computing. In meta-learning for quantum neural networks via classical recurrent neural networks, a recurrent neural network (RNN) is used to discover that optimization of the control parameters for algorithms like the QAOA and VQE are more efficient than simple off the shelf optimizers. And machine learning for quantum control uses reinforcement learning to help mitigate errors and produce higher quality quantum gates. - Model quantum data with quantum circuits. Classically modeling quantum data is possible if you have an exact description of the datasource—but sometimes this isn’t possible. To solve this problem, you can try modeling on the quantum computer itself and measure/observe the important statistics. Quantum convolutional neural networks shows a quantum circuit designed with a structure analogous to a convolutional neural network (CNN) to detect different topological phases of matter. The quantum computer holds the data and the model. The classical processor sees only measurement samples from the model output and never the data itself. In Robust entanglement renormalization on a noisy quantum computer, the authors learn to compress information about quantum many-body systems using a DMERA model. Other areas of interest in quantum machine learning include: - Modeling purely classical data on quantum computers. - Quantum-inspired classical algorithms. - Supervised learning with quantum classifiers. - Adaptive layer-wise learning for quantum neural network. - Quantum dynamics learning. - Generative modeling of mixed quantum states . - Classification with quantum neural networks on near term processors.

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Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory. The quantum computer, following the laws of quantum physics, would gain enormous processing power through the ability to be in multiple states, and to perform tasks using all possible permutations simultaneously. A Comparison of Classical and Quantum Computing Classical computing relies, at its ultimate level, on principles expressed by Boolean algebra. Data must be processed in an exclusive binary state at any point in time or bits. While the time that each transistor or capacitor need be either in 0 or 1 before switching states is now measurable in billionths of a second, there is still a limit as to how quickly these devices can be made to switch state. As we progress to smaller and faster circuits, we begin to reach the physical limits of materials and the threshold for classical laws of physics to apply. Beyond this, the quantum world takes over. In a quantum computer, a number of elemental particles such as electrons or photons can be used with either their charge or polarization acting as a representation of 0 and/or 1. Each of these particles is known as a quantum bit, or qubit, the nature and behavior of these particles form the basis of quantum computing. Quantum Superposition and Entanglement The two most relevant aspects of quantum physics are the principles of superposition and entanglement. - Superposition: Think of a qubit as an electron in a magnetic field. The electron’s spin may be either in alignment with the field, which is known as a spin-up state, or opposite to the field, which is known as a spin-down state. According to quantum law, the particle enters a superposition of states, in which it behaves as if it were in both states simultaneously. Each qubit utilized could take a superposition of both 0 and 1. - Entanglement: Particles that have interacted at some point retain a type of connection and can be entangled with each other in pairs, in a process known as correlation. Knowing the spin state of one entangled particle – up or down – allows one to know that the spin of its mate is in the opposite direction. Quantum entanglement allows qubits that are separated by incredible distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated. Taken together, quantum superposition and entanglement create an enormously enhanced computing power. Where a 2-bit register in an ordinary computer can store only one of four binary configurations (00, 01, 10, or 11) at any given time, a 2-qubit register in a quantum computer can store all four numbers simultaneously, because each qubit represents two values. If more qubits are added, the increased capacity is expanded exponentially. Difficulties with Quantum Computers - Interference – During the computation phase of a quantum calculation, the slightest disturbance in a quantum system (say a stray photon or wave of EM radiation) causes the quantum computation to collapse, a process known as de-coherence. A quantum computer must be totally isolated from all external interference during the computation phase. - Error correction – Given the nature of quantum computing, error correction is ultra critical – even a single error in a calculation can cause the validity of the entire computation to collapse. - Output observance – Closely related to the above two, retrieving output data after a quantum calculation is complete risks corrupting the data. The Future of Quantum Computing The biggest and most important one is the ability to factorize a very large number into two prime numbers. That’s really important because that’s what almost all encryption of internet applications use and can be de-encrypted. A quantum computer should be able to do that relatively quickly. Calculating the positions of individual atoms in very large molecules like polymers and in viruses. The way that the particles interact with each other – if you have a quantum computer you could use it to develop drugs and understand how molecules work a bit better. Even though there are many problems to overcome, the breakthroughs in the last 15 years, and especially in the last 3, have made some form of practical quantum computing possible. However, the potential that this technology offers is attracting tremendous interest from both the government and the private sector. It is this potential that is rapidly breaking down the barriers to this technology, but whether all barriers can be broken, and when, is very much an open question. This text is also available in Ahmed Banafa’s LinkedIn profile Ahmed Banafa, Author the Books:

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pic 1. Quantum entanglement is sharing a deep connection – in a quantum scale of things. When the physical and statistical properties of a particle are fundamentally dependent on the properties of one or several other particles, these particles are said to be entangled. Without any physical interaction these particles can remain deeply connected to each other, even when they are vast distances apart. Entanglement is a theoretical prediction that comes from the equations of quantum mechanics. Two particles can become entangled if they share the same state in a way that makes it possible to consider them not individually, but as a system. A laser beam fired through a certain type of crystal can cause individual photons to be split into pairs of entangled photons. Remarkably, quantum mechanics says that even if you separate those particles and send them to opposite directions they can remain entangled and inextricably connected. At least in theory. Conservation of the entanglement is very much susceptible to noise and “quantumness” disrupting decoherence (read more in Superposition text) if we are dealing with anything else but a perfectly isolated laboratory environment. To understand the profound meaning of entanglement you can consider the quality that an electron possesses called the “spin”. Generally, just as it is common for other quantum qualities, the spin of an electron remains uncertain and fuzzy until it is measured, as explained by Superposition. With two entangled particles whenever one of them is measured with spin up the other one must, no matter how far away it is from its entangled pair, be spin down. In other words whenever we inflict a measurement on one entangled particle, we automatically change its counterpart to correlate no matter the distance and with nothing, no physical force, attaching these two particles to one another. Fig 1. Changing one of the entangled particles spin will immediately do so with the other one, seemingly faster than light. This is what Einstein called “The spooky action at a distance”. The physicists Niels Bohr and Werner Heisenberg argued in 1934 among other quantum theory questions that an object’s state only truly existed once it became associated with a measurement, which meant somebody needed to observe it experimentally. Until then, its nature was merely a possibility. Upon measurement the system’s spin is fixed either up or down. To other physicists, such as Albert Einstein and Erwin Schrödinger, this was as preposterous as saying a cat inside a box is neither alive nor dead until you look. A paradox in other words. No action taken on the first particle could instantaneously affect the other, since this would involve information being transmitted faster than light, which is forbidden by the theory of relativity. Theory of relativity states for example that if anything were to travel faster than light it would violate the laws of causality and is a theory many times tested to not fall short. From Einstein’s work with Podolsky and Rosen an idea to solve this “spooky action at a distance” was argued to be solved with the thought of a more deterministic theory still unknown to science and hidden local variables that were coded in the particles and that could not be later influenced. In 1964 John Stewart Bell made a theoretical article that argued quantum physics to be incompatible with the local hidden variables theories, and that was later proven correct. Decades later, Bohr’s ideas still stand strong, and the strange nature of quantum entanglement is a solid part of modern physics. An interesting theory tested in the laboratory (using entanglement) is one that aims to quantize general relativity and unify the foundations of modern physics by leaving out time altogether. The results of testing this in 2013 with a toy Universe model suggested that time itself is an emergent phenomenon that comes about because of the nature of entanglement and that. While not working as the unifying theory for modern physics it paves a way for more research regarding entanglement. Entanglement continues to boggle the minds and remains as a part of the strange world of subatomic physics that we call quantum. More to read and links to text: Malin, Shimon, World Scientific 2012: Nature loves to hide: Quantum Physics and reality, a western perspective Science alert website, What is Quantum Entanglement? https://www.sciencealert.com/entanglement Fobes 11 August 2015, Chad Orzel: How Quantum Randomness Saves Relativity https://www.forbes.com/sites/chadorzel/2015/08/11/how-quantum-randomness-saves-relativity/ Space.com website 31 July 2019, Yasemin Saplakoglu: ‘Spooky’ Quantum Entanglement Finally Captured in Stunning Photo https://www.space.com/quantum-entanglement-photo.html Medium webpage 23 October 2013, The Physics ArXiv Blog: Quantum Experiment Shows How Time ‘Emerges’ from Entanglement https://medium.com/the-physics-arxiv-blog/quantum-experiment-shows-how-time-emerges-from-entanglement-d5d3dc850933 Ekaterina Moreva, Giorgio Brida, Marco Gramegna et al. 17 October 2013, Time from quantum entanglement: an experimental illustration, https://arxiv.org/pdf/1310.4691 Noora Heiskanen with thanks to Silvia Cotroneo and Jani-Petri Martikainen

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Superconductivity is a fascinating phenomenon in which, below a so-called critical temperature, a material loses all its resistance to electrical currents. In certain materials, at low temperatures, all electrons are entangled in a single, macroscopic quantum state, meaning that they no longer behave as individual particles but as a collective – resulting in superconductivity. The general theory for this collective electron behaviour has been known for a long time, but one family of materials, the cuprates, refuses to conform to the paradigm. It was long thought that for these materials the mechanism that ‘glues together’ the electrons must be special, but recently the attention has shifted and now physicists investigate the non-superconducting states of cuprates, hoping to find out their differences with normal superconductors. Most superconductors, when heated to exceed their critical temperature, change into ‘ordinary’ metals. The quantum entanglement that causes the collective behaviour of the electrons fades away, and the electrons start to behave like an ordinary ‘gas’ of charged particles. Cuprates are special, first of all because their critical temperature is considerably higher than that of other superconductors. On top of that, they have very special measurable properties even in their ‘metal phase’. In 2009, physicist Nigel Hussey observed experimentally that the electrons in these materials form a new type of structure, different from that in ordinary metals, and the term ‘strange metal’ was born. At nearly the same time, originating in Stanford in the United States, physicists started applying the theoretical machinery of string theory – a theory for a very different phenomenon, the behavior of gravity at the quantum level – to the description of electrons in metals. Completely unexpectedly, this machinery turned out to be able to predict certain phenomena that experimentally were known to occur in cuprates and other strange metals. Theoretical physicists Jan Zaanen and Koenraad Schalm (Leiden University) were involved in the early stages of these developments and made important contributions. In 2017, the pioneering work was transformed into a national research programme funded by NWO: Strange Metals. The programme is a special collaboration that involves both experimental and theoretical groups. Special behaviour at low temperatures The higher the temperature of a material, the more ‘noise’ measurements will show. To make the special properties of the strange metal state clearly visible, one would like to study the material at a temperature that is as low as possible, at most 1 degree above the absolute temperature minimum of -273°C. The obstacle for this is superconductivity itself: most strange metals already turn into superconductors when cooled to temperatures around -200°C. For this reason, in the Strange Metals programme, the choice was made to focus exclusively on a material with the chemical name Bi2Sr2CuO6, also known as ‘Bi2201’. This material becomes superconducting at about 35 degrees above the absolute minimum temperature. That is still too ‘hot’ for good measurements, but now the researchers can use a trick: superconductivity can be suppressed by a magnetic field. The general rule of thumb is: the larger the critical temperature of a material, the stronger the magnetic field required to suppress superconductivity. Since for Bi2201 the critical temperature is already quite low, the required magnetic field comes just within reach of the biggest magnets available in the Netherlands. This allowed PhD students Jake Ayres and Maarten Berben working within the groups of Hussey (HFML-FELIX, Bristol) and Van Heumen to eventually study the strange metal state of Bi2201 at various low temperatures and various magnetic field strengths. In this domain, the differences between strange metals and ordinary metals become strikingly visible. For ordinary metals, for example, one expects the electrical resistance to increase quadratically with temperature: increase the temperature by a factor of two, and the resistance will grow by a factor of four. The same holds if it is not the temperature but the magnetic field that is increased. The Dutch/UK team has now shown that these golden rules do not hold for cuprates. In these materials a new phase exists where the resistance depends linearly on the temperature and field strength: if one of these increases by a factor of two, so does the resistance. Contrary to what was observed before, the group discovered that this behaviour persists for a large range of the parameters. At the moment, there are two widely accepted theories that could explain the linear behaviour of the resistance. The first theory assumes that the linear behaviour only occurs near very specific values of the temperature and magnetic field strength. With the new measurements, this theory has now come under considerable pressure. The second theory is the theory of extreme quantum entanglement that comes from the string theoretic approach. Within this theory it is possible to observe the linear behavior for a large range of parameters. Surprisingly, therefore, it seems that to describe strange metals, one truly needs a theory that can also be used to describe quantum gravity! Quantum gravity in the lab The link between strange metals and quantum gravity has special observable effects. In an extensive analysis, the team shows that within the conventional models of electrical transport, it is absolutely impossible to properly explain the data. Their analysis shows that there exists a previously unobserved mechanism that makes the electrons lose energy. This loss occurs at extremely short time scales related to a fundamental constant of nature in quantum mechanics: Planck’s constant. According to general theory, this is the shortest time scale at which a quantum system can lose energy – something which moreover is only possible when the system is maximally entangled. This fingerprint of quantum gravity behaviour in the data excites many supporters of the link with string theory: it would be a first clue of physics far beyond the usual model of metals. To shed further light on the tension between ‘normal’ and ‘strange’ behaviour of metals, further experiments are needed. In that respect, promising developments still lie ahead within the Strange Metals program. Using a technique called ‘optical spectroscopy’, Van Heumen expects to be able to provide new details soon, and the groups of Mark Golden (Amsterdam) and Milan Allan (Leiden) are also working on results that could cause new surprises when it comes to the mysterious relation between quantum gravity and strange metals. Incoherent transport across the strange metal regime of overdoped cuprates, J. Ayres, M. Berben, M. Čulo, Y.-T. Hsu, E. van Heumen, Y. Huang, J. Zaanen, T. Kondo, T. Takeuchi, J. R. Cooper, C. Putzke, S. Friedemann, A. Carrington and N. E. Hussey. Nature 595 (2021) 661-666.

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A new method of relaying information by transferring the state of electrons moves scientists one step closer to creating fully functional quantum computers. Quantum computing has the potential to revolutionize technology, medicine, and science by providing faster and more efficient processors, sensors, and communication devices. But transferring information and correcting errors within a quantum system remains a challenge to making effective quantum computers. How do quantum computers work? A quantum computer operates on the principles of quantum mechanics, a unique set of rules that govern at the extremely small scale of atoms and subatomic particles. When dealing with particles at these scales, many of the rules that govern classical physics no longer apply and quantum effects emerge. A quantum computer can perform complex calculations, factor extremely large numbers, and simulate the behaviors of atoms and particles at levels that classical computers cannot. Quantum computers have the potential to provide more insight into principles of physics and chemistry by simulating the behavior of matter at unusual conditions at the molecular level. These simulations could be useful in developing new energy sources and studying the conditions of planets and galaxies or comparing compounds that could lead to new drug therapies. “You and I are quantum systems. The particles in our body obey quantum physics. But, if you try to compute what happens with all of the atoms in our body, you cannot do it on a regular computer,” says John Nichol, an assistant professor of physics at the University of Rochester. “A quantum computer could easily do this.” Quantum computers could also open doors for faster database searches and cryptography. “It turns out that almost all of modern cryptography is based on the extreme difficulty for regular computers to factor large numbers,” Nichol says. “Quantum computers can easily factor large numbers and break encryption schemes, so you can imagine why lots of governments are interested in this.” Ones and zeroes in quantum computers A regular computer consists of billions of transistors, called bits. Quantum computers, on the other hand, are based on quantum bits, also known as qubits, which can be made from a single electron. Unlike ordinary transistors, which can be either “0” or “1,” qubits can be both “0” and “1” at the same time. The ability for individual qubits to occupy these “superposition states,” where they are simultaneously in multiple states, underlies the great potential of quantum computers. Just like ordinary computers, however, quantum computers need a way to transfer information between qubits, and this presents a major experimental challenge. “A quantum computer needs to have many qubits, and they’re really difficult to make and operate,” Nichol says. “The state-of-the art right now is doing something with only a few qubits, so we’re still a long ways away from realizing the full potential of quantum computers.” All computers, including both regular and quantum computers and devices like smartphones, also have to perform error correction. A regular computer contains copies of bits so if one of the bits goes bad, “the rest are just going to take a majority vote” and fix the error. However, quantum bits cannot be copied, Nichol says, “so you have to be very clever about how you correct for errors. What we’re doing here is one step in that direction.” Quantum error correction requires that individual qubits interact with many other qubits. This can be difficult because an individual electron is like a bar magnet with a north pole and a south pole that can point either up or down. The direction of the pole—whether the north pole is pointing up or down, for instance—is known as the electron’s magnetic moment or quantum state. If certain kinds of particles have the same magnetic moment, they can’t be in the same place at the same time. That is, two electrons in the same quantum state cannot sit on top of each other. “This is one of the main reasons something like a penny, which is made out of metal, doesn’t collapse on itself,” Nichol says. “The electrons are pushing themselves apart because they cannot be in the same place at the same time.” If two electrons are in opposite states, they can sit on top of each other. A surprising consequence of this is that if the electrons are close enough, their states will swap back and forth in time. “If you have one electron that’s up and another electron that’s down and you push them together for just the right amount of time, they will swap,” Nichol says. “They did not switch places, but their states switched.” To force this phenomenon, Nichol and his colleagues cooled down a semiconductor chip to extremely low temperatures. Using quantum dots—nanoscale semiconductors—they trapped four electrons in a row, then moved the electrons so they came in contact and their states switched. “There’s an easy way to switch the state between two neighboring electrons, but doing it over long distances—in our case, it’s four electrons—requires a lot of control and technical skill,” Nichol says. “Our research shows this is now a viable approach to send information over long distances.” One step closer Transmitting the state of an electron back and forth across an array of qubits, without moving the position of electrons, provides a striking example of the possibilities quantum physics could enable for information science. “This experiment demonstrates that information in quantum states can be transferred without actually transferring the individual electron spins down the chain,” says Michael Manfra, a professor of physics and astronomy at Purdue University. “It is an important step for showing how information can be transmitted quantum-mechanically—in manners quite different than our classical intuition would lead us to believe.” Nichol likens this to the steps that led from the first computing devices to today’s computers. That said, will we all someday have quantum computers to replace our desktop computers? “If you had asked that question of IBM in the 1960s, they probably would’ve says no, there’s no way that’s going to happen,” Nichol says. “That’s my reaction now. But, who knows?” The research appears in Nature. Source: University of Rochester

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Written By Sneha Senthilkumar (Grade 11) Quantum computing is the new future of information technology. Mighty and possibly revolutionary, these computers will be a force to reckon with within a few decades down the line. Google’s quantum computer, called ‘Sycamore’, was able to solve a specific problem in 200 seconds, while estimating a powerful supercomputer would take a jaw-dropping 10,000 years to perform the same task. Quantum computing is holding a promise of becoming the ‘Belle of the Ball’, taking digital computation and problem-solving capacities to a level we never would have thought possible. All right. Enough with the majestic introduction. You probably must be thinking of something along the lines of: “Why does she just add ‘quantum’ in front of every word in the dictionary?” or “Does this mean I can finish my history project quicker, with this computer?”. Unfortunately, I don’t really have the best answers for those kinds of queries. But, I can give you an idea as to what these kinds of computers can do, how they work, and the promises they hold for the world in the future. The computer we use in our day to day lives is a very average computer. It functions based on the binary system, using ‘bits’. Bits can exist as either 0’s or 1’s. There is a lot more certainty in regular bits, as we are very sure of the state of the bit (either 0 or 1). However quantum computers have a slightly weirder unit of information. They use qubits (quantum bits). Other than the fact that I have yet again added the word ‘quantum’ in front of a regular dictionary word, there is something else that makes it different. The qubits can exist as a 0, and as a 1, at the same time. This phenomena is known as superposition, probably the most important concept in quantum computing too. In quantum physics, a particle, such as an electron, can exist in two different states or places at the same time. However, the catch is that, if a measurement is made on the particle, the wave function will collapse (it will return to a single state/place), and there will be no more superposition. Because of this fragile nature of superposition, the qubits can’t interact with any other particle (which is technically what I meant by ‘measurement’ in the previous paragraph). If it does get disturbed, then the qubit, that was once existing as both 0 and 1 simultaneously, will return to either a 0 or a 1, just like a classic bit from a classical system. As you can see here, we cannot exactly tell which state (0 or 1) it will become if the wave-function collapses. This is because quantum computing, just like quantum physics, is purely based on probabilities. That is what made physicists skeptical about quantum physics in the first place – the lack of certainty. Yes, the risks of loosing the superposition are there. If the qubit interacts with something, it will collapse to either 0 or 1, making the quantum computer not so ‘quantum’, anymore. But if there is no disturbance, the system will evolve in a deterministic way and maintain its superposition. The ability to remain ‘quantum’ (by maintaining the superposition) over ‘classical’ is known as quantum coherence. So, now you know how important it is not to make a measurement in superposition, and how it affects the qubit. Let’s discuss a bit about how we ensure that there is no disturbance in the quantum computer. So far, the best developed method of ensuring this is by using superconductors. A superconductor is basically a special type of material through which a charge can move, without resistance, thereby it does not loose any energy. For example, in electricity cables, there is always some electrical energy lost to the surroundings in the form of thermal energy, as it travels through the cables. This is because of resistance. So, if you have no resistance, then there is no energy lost, hence you get 100% efficiency. But, what does this mean for the qubits? Well qubits are made out of superconductive material, such as aluminium under certain conditions, which makes sure there is no resistance. When the qubit moves without resistance, it means that these qubits won’t interact with anything in its surroundings, which means no ‘measurement’ made! Are you able to make the connections? So why superconductors? What is so special? Superconductors will prevent the electrons from interacting with each other and other particles in the material. Superconductors do this by pairing up electrons loosely, far enough that they don’t interact with each other, yet still held together by loose bond, preventing them from interacting with other particles. Hence all the electrons, forming a (what we call) electron superfluid, will move through the system without getting disturbed. So, the quantum coherence is not jeopardised. Mission accomplished. The quantum computer is stored at temperatures near absolute zero (0 Kelvin/-273℃), which is almost as cold as the vacuum in space. This helps eliminate any possibilities of error in the system too. Quantum computing mainly comes into play when we are faced with large and more complex problems, which regular computers, even supercomputers, don’t have the power to solve. Although quantum computers are mostly used by military for cryptography, scientists are trying to find ways to bring these computers to the masses. Research has only just begun, as the complexity of the algorithms as well as the built of the computer prove challenging. Currently the only quantum computers are owned by IBM, Google, D-Wave Systems, Toshiba, and a handful of other companies. There is still a very long way to go, but the first step is often the most important leap to innovation. Featured Image Courtesy – Phys.org

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Diamond memory, drinkable seawater and energy through the air. This week’s coolest things make the most of the elements. What is it? Scientists in Japan made a 2-inch-diameter diamond wafer that could store 25 million terabytes of quantum data, theoretically enough to record a billion Blu-ray discs. Why does it matter? Diamond could be a very useful material for quantum computing and memory storage. But so far, researchers have been able to produce only 4-millimeter wafers of the necessary purity, while industrial uses require wafers of at least 2 inches — 14 times larger. How does it work? Although diamond is a form of carbon, its ability to store information comes from nitrogen, a common impurity. In particular, scientists take advantage of a defect called nitrogen-vacancy center, where a nitrogen atom sits next to an empty space in the crystal lattice. A little nitrogen goes a long way, and too much is problematic. It’s a difficult balance to strike, and researchers have failed to make industrial-size wafers without an excess of nitrogen. The scientists, from Saga University and Adamant Namiki Precision Jewel Co., devised a new method for creating the diamond wafers by growing crystals on a stepped substrate surface instead of a flat one. That reduced strain on the material, resulting in improved quality and limiting nitrogen contamination to a minuscule 3 parts per billion. “This new technology is expected to propel the advancement of quantum applications,” Adamant Namiki said in a statement. The company plans to commercialize the product in 2023. Top and above: Engineers were able to send microwave power more than 1 kilometer to a dish antenna. Scaled up, this technology could enable energy to be delivered from space to troops on the ground. Image and video credits: Naval Research Laboratory. What is it? Scientists from the Naval Research Laboratory wirelessly beamed 1.6 kilowatts (kW) of electrical energy across more than 1 kilometer (km). Why does it matter? The Pentagon tasked NRL researchers with demonstrating the delivery of 1 kW of power at a distance of 1 km, explained principal investigator Chris Rodenbeck. The test showed the possibility of sending electricity power to remote locations, such as on-the-ground military operations. In the long run, the technology could be used to deliver energy from space to Earth. How does it work? Engineers generated electricity, converted it to a 10-gigahertz microwave beam, and sent it through a dish antenna aimed at a receiver more than a kilometer away. The receiver consisted of a highway-sign-size array of tens of thousands of antennas working in what’s known as the X-band frequency (commonly used for police speed radar guns). Diodes converted the microwave power into DC power. What is it? Researchers from Virginia Tech and the U.S. Department of Energy homed in on an unappreciated factor in what causes batteries to decay. Why does it matter? Rechargeable batteries degrade over time. The research team discovered that it’s not just the properties of the electrode particles that cause decay, but also the interactions between them. “If you want to build a better battery, you need to look at how to put the particles together,” said Yijin Liu, a senior author of a paper in Science. How does it work? The researchers used X-ray tomography to create 3D images of battery cathodes at different ages. They identified more than 2,000 particles and described their size, shape and surface roughness, as well as how often they came into contact with one another. They found that after 10 charging cycles, traits such as surface-to-volume ratio and roundness of particles contributed most to their decay. But after 50 charging cycles, breakdown was driven primarily by interactions between particles, including how far apart they were, how varied their shapes and whether they were oriented similarly. Manufacturers could account for these particle-particle interactions to design longer-lasting batteries. What is it? Tufts University neuroscientists discovered a previously unknown ability of astrocytes, which make up nearly half of all brain cells. Why does it matter? Scientists knew that astrocytes were important in helping neurons grow and transmit signals in the brain. The research could open ways to attack ailments like epilepsy and Alzheimer’s. How does it work? The team programmed mice with genetically encoded voltage indicators that allowed them to visualize electrical activity with light. The study showed for the first time that astrocytes are electrically active, like neurons, and that the two cell types affect each other’s activity. “Neurons and astrocytes talk with each other in a way that has not been known about before,” said Chris Dulla, an author on a paper in Nature Neuroscience. Because there is so much that is not known about how the brain works, he added, discovering new fundamental processes that control brain function is key to developing novel treatments for neurological diseases. This portable unit, which weighs less than 22 pounds and does not require the use of filters, can be powered by a small, portable solar panel. Image credit: Video credit: MIT. What is it? MIT researchers developed a portable, filter-free desalination system the size of a small suitcase. Why does it matter? Portable devices for purifying water typically require a steady supply of energy to pump water through filters that need to be periodically replaced. At a mere 20 pounds, the new system, described in Environmental Science and Technology, eliminates filters and needs only as much energy as a phone charger. How does it work? The device uses a low-power pump to run water between two charged membranes that attract or repel particles such as salt ions, bacteria and viruses. Then it uses electrodialysis to remove any remaining salt. “It was successful even in its first run, which was quite exciting and surprising. But I think the main reason we were successful is the accumulation of all these little advances that we made along the way,” said senior author Jongyoon Han.

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(April 3, 2019) -- Building on the Air Force’s need to develop tech devices that require minimal charging in the field, the University of Texas at San Antonio (UTSA) is using principles in quantum science and engineering to build a graphene-based logic device. This new technology will improve the energy efficiency of battery-dependent devices from cell phones to computers. “We are developing devices that can operate almost battery-less,” said Ethan Ahn, UTSA assistant professor in electrical engineering. UTSA engineers are using spintronics, the study of an electron’s intrinsic quantum mechanical property called spin, to allow low-power operation with a possible application in quantum computing. “An electron is a little, but very strong magnet,” said Ahn. “Just imagine that an electron spins on its own axis, either up or down.” Traditional tech devices use the electronic charge of electrons for power. In spintronics, researchers are tapping the inherent spin of electrons as a new power source. With this new approach, devices will require fewer electrons to operate. There are hurdles, however, in harnessing the power of spin. In quantum computing that harnesses spin of electrons to transmit information, the challenge for researchers is how to capture spin as efficiently as possible. “If you have 100 electrons injected to the channel to power the next logic circuit, you may only get to use one or two spins because the injection efficiency is very low. This is 98 percent spin lost,” said Ahn. To prevent the loss of spin, Ahn has developed the new idea of the “zero-power carbon interconnect” by using nanomaterials as both the spin transport channel and the tunnel barrier. These nanomaterials are like a sheet of paper, a two-dimensional layer of carbon atoms just a few nanometers in thickness, and it’s the point of contact where spin injection is inputted into the device. Ahn’s prototype is an interconnect built with a reduced graphene oxide layer. “It’s novel because we are using graphene, a nanomaterial, to enhance spin injection. By controlling the amount of oxide on the graphene layers, we can fine tune electrons’ conductivity,” said Ahn. Graphene has widespread appeal because it’s the world's strongest nanomaterial. In fact, the room temperature conductivity of graphene is higher than that of any other known material. If successful, the zero-power carbon interconnect that Ahn is creating with his collaborators at UT-Austin and Michigan State University would be integrated into the logic component of a computer chip. The device, once developed, will be submitted to the U.S. Air Force Office of Scientific Research, which is supporting UTSA’s work with a three-year grant. “The military needs smaller devices that can operate in remote fields without need to recharge batteries,” said Ahn. “If our zero-power carbon interconnect is successful, it will improve the efficiency of graphene spintronics — a crucial step in advancing the next generation of low-power electronics like quantum computing.” This interconnect could also be highly beneficial to the cloud computing industry. According to the Data Knowledge Center, on-demand cloud computing platforms such as Amazon Web Services alone consume about two percent of the nation’s energy. If the zero-power carbon interconnect is successful, cloud servers such as those that offer streaming services like Netflix or host data, could operate faster and with less electricity. Learn more about the UTSA Nano Lab. Learn more about the UTSA Department of Electrical and Computer Engineering. Celebrate UTSA’s 50th Anniversary and share social media posts about the 50th using the hashtag #UTSA50. Adult Mental Health First Aid consists of 6 hours of instructor-led training, in which teaches adults how to recognize the signs and symptoms that suggest a potential mental health challenge, how to listen nonjudgmentally and give reassurance to the individual, student/colleague, who may be experiencing a mental health challenge, and how to refer a person to appropriate professional support and services at UTSAJohn Peace Library (4.04.12C), Main Campus Refusing to Forget is a collaborative effort to examine and expose the devastating impact of state-sanctioned racial violence throughout the early 20th century in the Texas Borderlands. This will be a two-day workshop.Gregory Luna Room, Buena Vista Building, Downtown Campus Join fellow UTSA accounting alumni for this fun-filled event that includes breakfast, a round of golf, an awards luncheon and great prizes. All proceeds from the tournament benefit student scholarships.Canyon Springs Golf Club, 24405 Wilderness Oak, San Antonio, TX 78260 The Faculty Coffee Chat is designed to provide faculty members the space to discuss current issues they are facing in an inclusive and supportive environment.Virtual Event These sessions are focused on incoming Freshman who are attending the UTSA Summer Orientation and are intending to major in areas within the College of Liberal and Fine Arts! We'll have important information and giveaways for you - come meet us.Willow room (SU 2.02.1), Main Campus Part 1 of our Career Skills Summer Workshop Series! Jeffrey Patten from the UTSA Career Center will be presenting on how to build a resume.Virtual Event Week 2 of our Career Skills Summer Workshop series.Virtual Event The University of Texas at San Antonio is dedicated to the advancement of knowledge through research and discovery, teaching and learning, community engagement and public service. As an institution of access and excellence, UTSA embraces multicultural traditions and serves as a center for intellectual and creative resources as well as a catalyst for socioeconomic development and the commercialization of intellectual property - for Texas, the nation and the world. To be a premier public research university, providing access to educational excellence and preparing citizen leaders for the global environment. We encourage an environment of dialogue and discovery, where integrity, excellence, inclusiveness, respect, collaboration and innovation are fostered. UTSA is a proud Hispanic Serving Institution (HSI) as designated by the U.S. Department of Education. The University of Texas at San Antonio, a Hispanic Serving Institution situated in a global city that has been a crossroads of peoples and cultures for centuries, values diversity and inclusion in all aspects of university life. As an institution expressly founded to advance the education of Mexican Americans and other underserved communities, our university is committed to ending generations of discrimination and inequity. UTSA, a premier public research university, fosters academic excellence through a community of dialogue, discovery and innovation that embraces the uniqueness of each voice.

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Bell's TheoremJohn Bell showed in 1964 how the 1935 "thought experiment" of Einstein, Podolsky, and Rosen (EPR) could be made into a real experiment. Einstein was especially bothered by the "nonlocal" aspect of quantum mechanics exhibited by a measurement at one place instantaneously determining the properties (position and momentum, and later spin) of a particle detected at another place. The spacelike separation between the two measurements implied something "travelling" faster than the speed of light between the two. Actually, at the 1927 Solvay Conference, Einstein had already complained about "action at a distance" and faster-than-light effects when, in a single-slit version of the two-slit experiment, the detection of a single particle at one place instantaneously collapsed the probability (Ψ2) of finding it at a distant place. And we now know that Einstein probably saw this implicit violation of his theory of special relativity as early as 1905, when he formulated both relativity theory and the light-quantum hypothesis. See our history of Einstein's thought. EPR proposed the existence of supplementary parameters or "local hidden variables" that could communicate information between the two measurements. Einstein's colleagues Erwin Schrödinger, Max Planck, David Bohm, and others hoped that the hidden variables would allow a return to deterministic physics. They wanted to eliminate mysterious quantum phenomena like superposition of states, quantum entanglement and nonlocality, action at a distance, and - perhaps most important for Schrödinger - the irreducible statistical chance associated with the collapse of the wave function. Einstein's famous remark on quantum indeterminacy was that "God does not play dice." According to Wolfgang Pauli (in correspondence with Max Born), Einstein was less concerned with the return of determinism than he was with the restoration of "local reality" and the elimination of "action at a distance." In 1964, John Bell put limits on any supplementary parameters or "hidden variables" that might eliminate nonlocality and restore a deterministic physics in the form of what he called an "inequality," the violation of which would confirm standard quantum mechanics. Bell also described his key assertions in the simple idea that "local hidden variables" will never be found that give the same results as quantum mechanics. This has come to be known as Bell's Theorem. In a 1990 lecture at CERN, shortly before his untimely death, Bell made it plain that the violation of his inequality had shown the "Einstein program" to be a failure. It just is a fact that quantum mechanical predictions and experiments, in so far as they have been done, do not agree with [my] inequality. And that's just a brutal fact of nature... No action at a distance led you to determinism, in the case of parallel polarisers, but determinism, in the case of off-parallel polarisers, leads you back to action at a distance. Now, in my opinion, in answer to the question that you posed at the beginning, I don't know this phrase is too strong and active an assertion, I cannot say that action at a distance is required in physics. But I can say that you cannot get away with no action at a distance. You cannot separate off what happens in one place and what happens in another. Somehow they have to be described and explained jointly. Well, that's just the fact of the situation; the Einstein program fails, that's too bad for Einstein, but should we worry about that?Bell's Theorem has been tested in numerous real EPR experiments over the years, by John Clauser, Alain Aspect, Michael Horne, Albert Shimony, and Richard Holt (in various CHSH-type experiments) and most recently by Nicolas Gisin and his colleagues in Geneva with entangled photons sent over miles of fiber optics. In the 1989 book, Sixty-two Years of Uncertainty, Abner Shimony summarized the significance of various versions of Bell's Theorem. All versions of Bell's theorem are variations, and usually generalizations, of the pioneering paper of J.S. Bell of 1964, entitled "On the Einstein-Podolsky-Rosen Paradox." All of them consider an ensemble of pairs of particles prepared in a uniform manner, so that statistical correlations may be expected between outcomes of tests performed on the particles of each pair. If each pair in the ensemble is characterized by the same quantum state Φ, then the quantum mechanical predictions for correlations of the outcomes can in principle be calculated when the tests are specified. On the other hand, if it is assumed that the statistical behavior of the pairs is governed by a theory which satisfies certain independence conditions (always similar to the Parameter and Outcome Independence conditions stated below, though the exact details vary from version to version of Bell's theorem), then it is possible to derive a restriction upon the statistical correlations of the outcomes of tests upon the two particles. The restriction is stated in the form of an inequality, known by the collective name of "Bell's Inequality." Each version of Bell's theorem exhibits a choice of Φ and of the tests upon the two particles such that the quantum mechanical predictions of correlations violates one of the Bell's Inequalities. The theorem therefore asserts that no physical theory satisfying the specified independence conditions can agree in all circumstances with the predictions of quantum mechanics. The theorem becomes physically significant when the Experimental arrangement is such that relativistic locality prima facie requires that the independence conditions be satisfied. Because such arrangements are in principle possible (and, in fact, actually realizable, if certain reasonable assumptions are made), one can restate Bell's Theorem more dramatically as follows: no local physical theory can agree in all circumstances with the predictions of quantum mechanics. The reason philosophers like Shimony have difficulty with two-particle wave-function collapses is clear from his exposition. It is quite wrong to describe two distinct particles, 1 and 2, with 1 entering the right analyzer and 2 entering the left analyzer. Just as a single particle cannot be localized in the two-slit experiment, neither particle in an EPR experiment is localizable until there is a measurement, at which time both become localized (to within the usual quantum indeterminacy) however far apart they are at that time (in the rest frame of the experiment). The reason we know everything about the "other" particle as soon as we measure one is, as Einstein knew well, but later writers often ignore, found in the various conservation laws (of energy, momentum, spin, etc.). If Bell's inequalities were not violated, the much more fundamental laws of conservation of momentum, angular momentum and spin would be violated. For a correct description of how quantum mechanics describes two particles in an entangled quantum state, see our description of the EPR experiment.Fig. 1. An ensemble of particle pairs 1 + 2 is emitted in a uniform manner from the source. Particle 1 enters an analyzer with a controllable parameter a, and the possible outcomes are sm (m = 1,2,...). Particle 2 enters an analyzer with controllable parameter b, and the possible outcomes are tn (n = 1,2,...).Figure 1 shows a source from which particle pairs, labeled 1 and 2, are emitted in a uniform manner. The complete state of a pair 1+2 is denoted by k, where k belongs to a space K of complete states. No assumption is made about the structure of K, except that probability measures can be defined on it. Because of the uniform experimental control of emission, it is reasonable to suppose that there is a definite probability measure w defined over K which governs the ensemble of pairs; but the uniformity need not be such that w is a delta-function, i.e., that every pair of the ensemble is in the same complete state k. Particle 1 enters an analyzer with a controllable parameter a, which the experimenter can specify, for instance, by turning a knob. Likewise, particle 2 enters an analyzer with a controllable parameter b.

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Modern construction is an effort of precision. Manufacturers must use components manufactured to meet specific standards - such as beams of a desired composition or rivets of a specific size. The building industry relies on manufacturers to create these components reliably and reproducibly to build secure bridges and healthy skyscrapers. Now imagine a construction on a smaller scale - less than 1 / 100th of the thickness of a piece of paper. It's the nanoscale. It is at this scale that scientists are working to develop potentially revolutionary technologies in areas such as quantum computing. It's also a scale where traditional manufacturing methods simply will not work. Our standard tools, even miniaturized, are too bulky and too corrosive to make nano-scale reproducible components. Researchers at the University of Washington have developed a method for reproducible nanoscale fabrication. The team adapted light-based technology widely used in biology, known as an optical trap or optical tweezer, to operate in a water-free liquid environment containing carbon-rich organic solvents, thus enabling potential new applications. As the team reports in an article published Oct. 30 in Nature Communications, optical tweezers act as a light-based "pulling beam" capable of assembling nanoscale semiconductor materials into larger structures. Unlike sci-fi tractor bundles, which catch spaceships, the team uses optical tweezers to trap materials nearly a billion times less than one meter. "This is a new approach to manufacturing at the nanoscale," said Peter Pauzauskie, associate author and associate professor of materials science and engineering, faculty member of the Molecular Engineering & Sciences Institute and from the Institute for Nano-engineering systems. scientist at the Pacific Northwest National Laboratory. "The manufacturing process does not involve any chamber surface that minimizes the formation of deformations or other defects." All components are suspended in solution and we can control the size and shape of the nanostructure when assembled piece by piece . " "The use of this technique in an organic solvent allows us to work with components that would deteriorate or corrode on contact with water or air," said Vincent Holmberg, co-lead author, assistant professor of chemical engineering at the University of Washington, professor at Clean Energy. Institute and Institute of Engineering and Molecular Sciences. "Organic solvents also help us to overheat the material we work with, allowing us to control material transformations and stimulate chemistry." To demonstrate the potential of this approach, researchers used optical precelles to construct a new nanowire heterostructure, which is a nanowire consisting of discrete sections made of different materials. The starting materials of the heterostructure of nanowires were shorter "nanorodes" in crystalline germanium, each one hundred nanometers long and several tens of nanometers in diameter, about 5,000 times thinner than a hair human. Each is capped with a metallic bismuth nanocrystal. The researchers then used the light-based "tractor beam" to capture one of the germanium nanorods. The beam energy also overheats the nanodod, melting the bismuth plug. They then guide a second nanoref in the "tractor beam" and weld them end to end, thanks to the molten bismuth cap. The researchers could then repeat the process until they assembled a patterned heterostructure of nanowires with repeating semiconductor metal junctions five to ten times longer than the different building blocks. "We started to call this optically-oriented" photon nanosocket "assembly process, which is to weld two components together at the nanoscale using light," said Holmberg. Nanowires that contain junctions between materials, such as the Germanium-bismuth junctions synthesized by the UW team, could possibly be a means of creating topological qubits for applications in quantum computing. The tractor beam is actually a highly focused laser that creates a type of optical trap, a Nobel Prize winning method developed by Arthur Ashkin in the 1970s. To date, optical traps have been used almost exclusively in water-based or vacuum-based environments. The Pauzauskie and Holmberg teams adapted optical trapping to work in the more volatile environment of organic solvents. "Generating a stable optical trap in any type of environment is a delicate balancing act and we were fortunate to have two very talented graduate students working together on this project," said Holmberg. The photons that make up the laser beam generate a force on the objects in the immediate vicinity of the optical trap. Researchers can adjust the properties of the laser so that the force generated can trap or release an object, be it a germanium nanorode or a longer nanowire. "This is the type of precision needed for reliable and reproducible nanofabrication methods without chaotic interaction with other surfaces or materials that can introduce defects or deformations into nanomaterials," said Pauzauskie. The researchers believe that their nanosoldering approach could allow the additive manufacturing of nanoscale structures with different sets of materials for other applications. "We hope this demonstration will inspire researchers to use optical trapping for the manipulation and assembly of a broader set of nanoscale materials, whether or not these materials are compatible with water," he said. Mr. Holmberg. The lead co-authors of the paper are Elena Pandres, a UW graduate student in chemical engineering, and Matthew Crane, a UW PhD and postdoctoral researcher in UW's Department of Chemistry. Co-author is E. James Davis, Professor Emeritus of Chemical Engineering at the University of Washington. The research was funded by the National Science Foundation, the UW Molecular Engineering Materials Center, the UW Molecular Engineering & Sciences Institute, the UW Institute for Nano-engineering Systems, the UW Clean Energy Institute, Washington State, Washington Research Foundation and the Air Force's Scientific Research Office. Matthew J. Crane, Elena P. Pandres, E. James Davis, Vincent C. Holmberg, Peter J. Pauzauskie. Optically oriented attachment of nanoscale metal-semiconductor heterostructures in organic solvents via photonic nanosoldering. Nature Communications, 2019; 10 (1) DOI: 10.1038/s41467-019-12827-w

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A research team with the Technical University of Munich (TUM) have designed a quantum cryptography chip aimed at the security demands of the quantum computing revolution. The RISC-V chip, which was already sent to manufacturing according to the researchers' design, aims to be a working proof of concept for protecting systems against quantum computing-based attacks, which are generally considered to be one of the most important security frontiers of the future. Alongside the RISC-V based hardware implementation (which includes ASIC and FPGA structures), the researchers also developed 29 additional instructions for the architecture that enable the required workloads to be correctly processed on-chip. Traditional cryptography is generally based on both the sender and receiver holding the same "unlock" key for any given encrypted data. These keys (which may include letters, digits, and special characters) have increased in length as time passes, accompanying increases in hardware performance available in the general computing sphere. The idea is to thwart brute-force attacks that would simply try out enough character combinations that would allow them to eventually reach the correct answer that unlocks the encrypted messages' contents. Given a big enough size of the security key (and also depending on the encryption protocol used), it's virtually impossible for current hardware — even with the extreme parallelization enabled by the most recent GPUs — to try out enough combinations in a short enough timeframe to make the effort worthwhile. A piece of information cryptographically encoded by one of the most popular encryption algorithms used today, AES-128, would be impossible to crack by even the most powerful distributed computing efforts of today, Bitcoin. For reference, it would take the network around 70,000,000,000,000,000,000,000,000 years to do so (kudos if you can count that high a number) and it's estimated our universe has existed for only 14 billion years, comparatively speaking. Encryption-breaking algorithms on the quantum computing sphere would require quantum systems with an estimated 2,953 logical qubits for near-instant decryption of an AES-128 key, and 6,681 logical qubits for AES-256. Current quantum technology has achieved a "mere" 100 qubits total, so we're still somewhat far-off from a security collapse. But quantum computing is advancing at a breakneck speed since the first actual first quantum computer manifestation — a dual-qubit system showcased in 1998 by Isaac Chuang of the Los Alamos National Laboratory, Neil Gershenfeld of the Massachusetts Institute of Technology (MIT), and Mark Kubinec of the University of California at Berkeley that could be loaded with data and output a solution. The acceleration in qubit counts for new quantum systems and the potential appearance of new decryption algorithms could upend current encryption techniques much faster than expected, and that's the reason why the TUM research team is focussing on pre-empting security challenges that are expected to eventually materialize. In designing their quantum-security chip, the TUM researchers took a cohesive (and world first) hardware and software co-design approach, with purpose-designed hardware that accelerates the current poster-child for quantum cryptography, the lattice-based Kyber algorithm. The researchers say they've achieved a 10x performance increase compared to current software Kyber encryption solutions, while using around eight times less energy. The chip also features support and a 21x performance increase for an even more advanced form of quantum encryption, Supersingular Isogeny Key Encapsulation (SIKE), which is expected to be deployed when lattice-based approaches, such as Kyber, no longer cut it. In addition to the Kyber and SIKE acceleration, the research team is also using this chip as an accelerator for smart hardware Trojan detection. Hardware Trojan is a term that refers to the addition of hardware-based solutions that aim to circumvent typical security mechanisms by offering backdoors that either siphon information to a remote attacker, or enable silent, undetected access to the compromised system's processing. These hardware Trojans can be secretly implemented in various stages of hardware fabrication (such as in the design or manufacturing stages), and concerns regarding this potential attack vector hit the public space with the (fake) reports of certain Supermicro-manufactured motherboards allegedly featuring purpose-built chips that siphoned data to China. To fill the void of information on this novel security exploit, the TUM researchers have also fitted their chip with four distinct hardware Trojans. These will literally destroy their proof-of-concept chip layer by layer while feeding each step of the process to newly-developed machine learning algorithms, training them in identifying hardware functions even in the absence of technical information regarding what the hardware does exactly. This helps to identify components (Trojans) that perform functions unrelated to the chip’s actual tasks that may have made their way into the final design. This research will also likely provide lasting effects in the reverse-engineering space, and it's likely that it's being pursued by other parties (academic or otherwise). Quantum computing stands at the forefront of a brave new world in the technology sphere. As I wrote this article, I was reminded of Arthur C. Clarke's Third Law: “Any sufficiently advanced technology is indistinguishable from magic”. I for one am having difficulty in distinguishing one from the other.

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What is quantum computing? Quantum computers have the ability to certain computations exponentially faster than any classically designed (super) computers ever could. This could lead to unprecedented advances in artificial intelligence disease eradication and much more. However, in the wrong hands these computers will also have the ability to break the current encryption algorithms keeping the internet, and our data, safe. Classical computers (which you are probably currently viewing this page on) work on bits that can be either 1 or 0. You can think of this as a light switch turning a lightbulb on or off. On a quantum computer, the quantum bit (or qubit) can be 1, 0, or anything in between. This means the “light switch” can be on, off, or both at the same time. For data information, this means that a quantum computer can not only store multiple bits of data on the same qubits at the same time, but it can also compute all of that data at the same time also, making them exponentially more powerful than a classical computer. For example, lets say you have four classical bits to store a number. Four bits allows you store any decimal number from 0 to 15, but only one at a time. If you wanted to store all 16 numbers you would need 16*4 classical bits. A quantum computer can store all 16 numbers on the same four bits at the same time, and do any calculation on them requested. This means a quantum computer with just 32 qubits, you could be in 232 = 4,294,967,296 states simultaneously which could translate to approximately 500 MB of data. Are quantum computers available? Governments around the world and high-tech giants such as Microsoft, Google, IBM, and more have pledged or already invested millions and even billions of dollars to develop large-scale quantum computers and there does not seem to be any reason to slow down now. Google has recently announced it has achieved quantum supremacy, which is a term that signifies a single task was done on a quantum computer that would take thousands of years (even if possible) on a classical computer. This is another leap forward for quantum computing and moves us ever closer to the day large-scale quantum computers are available. With high-tech companies such as Google, Microsoft, Amazon, IBM, and more having already build small-scale quantum computers and countries like the United States, China, Russia and more already pledging billions of dollars to be the first to build a large-scale quantum computer, the race is on. It is expected that in as little as five years from now we could begin to see some of the benefits of quantum computers to the technology, science, health, and environmental sectors. However, quantum computers large enough to break current cryptographic algorithms are more likely a decade away. If large scale quantum computers are not available, am I safe? The short answer is no. While the cryptographic security used today cannot be broken with current methods, it can be stored, and broken in the future when quantum computers are available. This type of attack is called a ‘harvest and decrypt’ attack. If information being sent today is still sensitive more than a decade from now (like banking data and classified governmental documents) then that information is already in danger. A longer answer is that today, you are more likely to be hacked by having an insecure password, or by having no security set up at all. Governments around the world are working on standardizing quantum-safe solutions and companies are beginning to invest money into testing these solutions on their devices. It will be years before the quantum-safe solutions are implemented on servers and clouds and other connected devices, but the sooner we begin this transition the sooner we will be ready. What is at risk? Any device connected to a network is vulnerable to a quantum-enabled attacked, one quantum-computers are available. A big concern of industry today is the expected transition period. It took more than 15 years for industries to adopt ECC (elliptic curve cryptography) over RSA and the transition to quantum-safe solutions is expected to be even more challenging. How are we helping? At PQSecure, we focus on the risks that quantum computers pose, and that threat is to cryptography. There are multiple layers of security and encryption in place on most websites and cloud servers today to keep your data safe. However, if a single layer is broken, all of your stored data could be a risk. By making sure these layers are quantum-safe, we can avoid the risk of quantum computers, when they arrive. PQSecure creates quantum-safe solutions that are designed to be power and energy efficient which makes them ideal for small, embedded devices such as IoT. With the fast growth of the IoT industry, these devices pose additional risks to your home or work networks. Each new devices adds an additional entry point for quantum-enabled hackers to access your data. And with the focus on these devices to be cheaper and faster, many of these IoT companies cannot find a viable solution, and thus are selling IoT devices without these crucial built-in security features.

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In 1994, Peter Shor, a mathematician then at Bell Labs in New Jersey, proved that a quantum computer would have the power to solve some problems exponentially faster than a classical machine. The question was: Could one be built? Skeptics argued that quantum states were too delicate — the environment would inevitably jumble the information in the quantum computer, making it not quantum at all. A year later, Shor responded. Classical error-correcting schemes measured individual bits to check for errors, but that approach wouldn’t work for quantum bits, or “qubits,” since any measurement would destroy the quantum state, and hence the calculation. Shor figured out a way to detect whether an error had occurred without measuring the state of the qubit itself. Shor’s code marked the beginning of the field of quantum error correction. The field has flourished. Most physicists see it as the only path to building a commandingly powerful quantum computer. “We won’t be able to scale up quantum computers to the degree that they can solve really hard problems without it,” said John Preskill, a physicist at the California Institute of Technology. As with quantum computing in general, it’s one thing to develop an error-correcting code, and quite another to implement it in a working machine. But at the beginning of October, researchers led by Chris Monroe, a physicist at the University of Maryland, reported that they had demonstrated many of the ingredients necessary to run an error-corrected circuit like Shor’s. So how did Shor crack the conundrums he faced? He used the added complexity of quantum mechanics to his advantage. Repeat Repeat Repeat Shor modeled his protocol after the classical repeater code, which involves making copies of each bit of information, then periodically checking those copies against each other. If one of the bits is different from the others, the computer can correct the error and continue the calculation. Shor designed a quantum version of this. He used three individual “physical” qubits to encode a single qubit of information — the “logical” qubit. Shor’s quantum repeater code couldn’t be exactly the same as the classical version, though. The essential power of quantum computation comes from the fact that qubits can exist in a “superposition” of being in a combination of 0 and 1 at the same time. Since measuring a quantum state would destroy the superposition, there wasn’t a straightforward way to check to see whether an error had occurred. Instead, he found a way to tell if the three physical qubits were in the same state as one another. If one of the qubits was different, it would indicate that an error had occurred. The task is not unlike solving a simple logic puzzle. You’re given three balls that look identical, but one of the balls might have a different weight. You also have a simple balance scale. What measurements will let you determine whether there is an oddball in the mix, and if so, which one it is? The answer is to first pick two balls and compare their weights, then replace one of the balls with the remaining ball and check again. If the scale was balanced both times, then all balls are identical. If it was balanced only once, then one of the replaced balls is the odd one out. If the scales are imbalanced both times, the ball that stayed still is the culprit. Shor’s code replaces the scales with two extra “ancilla” qubits. The first of these compares the first and second physical qubits; the other compares the second and third. By measuring the states of these ancillary qubits, you learn if the three information-containing qubits are in identical states without disturbing the state of any of them. This code protects against a bit flip, which is the only possible error that can occur in classical computing. But qubits have one more potential source of error. Superpositions are the key to quantum computing, but it’s not just the value of the qubit that’s important. The relative “phase” between qubits matters too. You can think of this phase as a wave — it tells you the location of the wave’s peaks and troughs. When two waves are in phase, their ripples are synchronized. If they collide, they will constructively interfere, merging into a single wave double the size. But if the waves are out of phase, then when one wave is at its peak, the other is at its nadir, and they will cancel each other out. A quantum algorithm takes advantage of this phase relationship among its qubits. It sets up a situation where the correct answer to a calculation constructively interferes and is therefore amplified, while the incorrect answer gets suppressed by destructive interference. But if an error causes the phase to flip, then destructive interference can switch to constructive interference, and the quantum computer will start amplifying the wrong answer. Shor found that he could correct for phase errors using a similar principle to the one he used for bit flips. Each logical qubit gets encoded into three qubits, and ancilla qubits check to see if one of the phases has flipped. Shor then combined the two codes. The result was a code that translated one logical qubit into nine physical qubits that offered both bit and phase checks. Tolerant to a Fault Shor’s code would in principle protect a single logical qubit from errors. But what if there was a mistake in the error measurements themselves? Then, in your attempt to correct the nonexistent error, you would flip a bit and unwittingly introduce a real error. In some cases, this could cause a cascade of errors to propagate through the code. Shor’s code also didn’t consider how he would operate a quantum computer built from his logical qubits. “We need some way to do computations on the encoded states, without losing that protection. And that’s not straightforward,” said Daniel Gottesman, a theoretical computer scientist at the University of Maryland. So in 1996, his third consecutive year of blazing trails, Shor came up with the notion of fault tolerance. A fault-tolerant code can deal with errors introduced by the environment, by imperfect operations on those qubits, and even by the error-correction steps themselves — provided the rate at which these errors occur is below a certain threshold. Last month, Monroe and his group announced that they had used a fault-protected version of Shor’s code called the Bacon-Shor code to demonstrate nearly all the tools necessary for a fully fault-tolerant quantum computer. They encoded a logical qubit into the quantum states of nine ions, then, using four ancilla qubits, they showed that they could fault-tolerantly perform all single-qubit operations necessary for quantum computing. The result shows that a fault-tolerant quantum computer is possible. This goal remains distant, though. Monroe thinks the advantage granted by error correction won’t be seen until quantum computers have reached about 100 logical qubits. Such a machine would require about 1,300 physical qubits, since each logical qubit needs nine physical qubits plus four ancillas. (The current largest quantum processor, IBM’s newly announced Eagle, has 127 physical qubits.) At that point, “we’re going to start making a qubit factory and then we’ll introduce error correction,” said Monroe. “But not before.”

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What is end-to-end encryption? End-to-end encryption (E2EE) is a method of secure communication that prevents third parties from accessing data while it's transferred from one end system or device to another. In E2EE, the data is encrypted on the sender's system or device, and only the intended recipient can decrypt it. As it travels to its destination, the message cannot be read or tampered with by an internet service provider (ISP), application service provider, hacker or any other entity or service. Many popular messaging service providers use end-to-end encryption, including Facebook, WhatsApp and Zoom. These providers have faced controversy around the decision to adopt E2EE. The technology makes it harder for providers to share user information from their services with authorities and potentially provides private messaging to people involved in illicit activities. How does end-to-end encryption work? The cryptographic keys used to encrypt and decrypt the messages are stored on the endpoints. This approach uses public key encryption. Public key, or asymmetric, encryption uses a public key that can be shared with others and a private key. Once shared, others can use the public key to encrypt a message and send it to the owner of the public key. The message can only be decrypted using the corresponding private key, also called the decryption key. In online communications, there is almost always an intermediary handing off messages between two parties involved in an exchange. That intermediary is usually a server belonging to an ISP, a telecommunications company or a variety of other organizations. The public key infrastructure E2EE uses ensures the intermediaries cannot eavesdrop on the messages that are being sent. The method for ensuring a public key is the legitimate key created by the intended recipient is to embed the public key in a certificate that has been digitally signed by a recognized certificate authority (CA). Because the CA's public key is widely distributed and known, its veracity can be counted on; a certificate signed by that public key can be presumed authentic. Since the certificate associates the recipient's name and public key, the CA would presumably not sign a certificate that associated a different public key with the same name. How does E2EE differ from other types of encryption? What makes end-to-end encryption unique compared to other encryption systems is that only the endpoints -- the sender and the receiver -- are capable of decrypting and reading the message. Symmetric key encryption, which is also known as single-key or secret key encryption, also provides an unbroken layer of encryption from sender to recipient, but it uses only one key to encrypt messages. The key used in single-key encryption can be a password, code or string of randomly generated numbers and is sent to the message recipient, enabling them to unencrypt the message. It may be complex and make the message look like gibberish to intermediaries passing it from sender to receiver. However, the message can be intercepted, decrypted and read, no matter how drastically the one key changes it if an intermediary gets ahold of the key. E2EE, with its two keys, keeps intermediaries from accessing the key and decrypting the message. Another standard encryption strategy is encryption in transit. In this strategy, messages are encrypted by the sender, decrypted intentionally at an intermediary point -- a third-party server owned by the messaging service provider -- and then reencrypted and sent to the recipient. The message is unreadable in transit and may use two-key encryption, but it is not using end-to-end encryption because the message has been decrypted before reaching its final recipient. Encryption in transit, like E2EE, keeps messages from being intercepted on their journey, but it does create potential vulnerabilities at that midpoint where they are decrypted. The Transport Layer Security encryption protocol is an example of encryption in transit. How is end-to-end encryption used? End-to-end encryption is used when data security is necessary, including in the finance, healthcare and communications industries. It is often used to help companies comply with data privacy and security regulations and laws. For example, an electronic point-of-sale (POS) system provider would include E2EE in its offering to protect sensitive information, such as customer credit card data. Including E2EE would also help a retailer comply with the Payment Card Industry Data Security Standard (PCI DSS), which mandates that card numbers, magnetic stripe data and security codes are not stored on client devices. What does end-to-end encryption protect against? E2EE protects against the following two threats: - Prying eyes. E2EE keeps anyone other than the sender and intended recipient from reading message information in transit because only the sender and recipient have the keys to decrypt the message. Although the message may be visible to an intermediary server that is helping move the message along, it won't be legible. - Tampering. E2EE also protects against tampering with encrypted messages. There is no way to predictably alter a message encrypted this way, so any attempts at altering would be obvious. What doesn't end-to-end encryption protect against? Although the E2EE key exchange is considered unbreakable using known algorithms and current computing power, there are several identified potential weaknesses of the encryption scheme, including the following three: - Metadata. While E2EE protects the information inside a message, it does not conceal information about the message, such as the date and time it was sent or the participants in the exchange. This metadata could give malicious actors with an interest in the encrypted information clues as to where they may be able to intercept the information once it has been unencrypted. - Compromised endpoints. If either endpoint has been compromised, an attacker may be able to see a message before it is encrypted or after it is decrypted. Attackers could also retrieve keys from compromised endpoints and execute a man-in-the-middle attack with a stolen public key. - Vulnerable intermediaries. Sometimes, providers claim to offer end-to-end encryption when what they really offer is closer to encryption in transit. The data may be stored on an intermediary server where it can be accessed. Advantages of end-to-end encryption The main advantage of end-to-end encryption is a high level of data privacy, provided by the following features: - Security in transit. End-to-end encryption uses public key cryptography, which stores private keys on the endpoint devices. Messages can only be decrypted using these keys, so only people with access to the endpoint devices are able to read the message. - Tamper-proof. With E2EE, the decryption key does not have to be transmitted; the recipient will already have it. If a message encrypted with a public key gets altered or tampered with in transit, the recipient will not be able to decrypt it, so the tampered contents will not be viewable. - Compliance. Many industries are bound by regulatory compliance laws that require encryption-level data security. End-to-end encryption can help organizations protect that data by making it unreadable. Disadvantages of end-to-end encryption Although E2EE generally does a good job of securing digital communications, it does not guarantee data security. Shortcomings of E2EE include the following: - Complexity in defining the endpoints. Some E2EE implementations allow the encrypted data to be decrypted and reencrypted at certain points during transmission. This makes it important to clearly define and distinguish the endpoints of the communication circuit. - Too much privacy. Government and law enforcement agencies express concern that end-to-end encryption can protect people sharing illicit content because service providers are unable to provide law enforcement with access to the content. - Visible metadata. Although messages in transit are encrypted and impossible to read, information about the message -- date sent and recipient, for instance -- is still visible, which may provide useful information to an interloper. - Endpoint security. If endpoints are compromised, encrypted data may be revealed. - Not future-proof. Although end-to-end encryption is a strong technology now, there is speculation that eventually quantum computing will render cryptography obsolete. Applications that use E2EE The first widely used E2EE messaging software was Pretty Good Privacy, which secured email and stored files and digital signatures. Text messaging applications frequently use end-to-end encryption, including Apple's iMessage, Jabber and Signal Protocol (formerly TextSecure Protocol). POS providers, like Square, also use E2EE protocols to help maintain PCI compliance. In 2019, Facebook announced that all three of its messaging services would begin using E2EE. However, law enforcement and intelligence agencies argue that encryption limits Facebook's ability to police illegal activity on its platforms. The debate often focuses on how E2EE can make it more difficult to identify and disrupt child abuse on private messaging platforms. Encryption is just one piece of data security in the enterprise. Learn more about all aspects of data security and compliance in our comprehensive guide. Continue Reading About end-to-end encryption (E2EE) Dig Deeper on Data security and privacy EU plans to police child abuse raise fresh fears over encryption and privacy rights IT professionals wary of government campaign to limit end-to-end encryption Tech companies risk being compelled by law to protect children, says online safety expert ICO criticises government-backed campaign to delay end-to-end encryption

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SANTA FE, N.M. Researchers at Los Alamos National Laboratories claim to have originated a blueprint for room-temperature quantum computers using such optical components as beam splitters, phase shifters and photodetectors. While some scientists contend that new kinds of nonlinear optical components must be invented before economical quantum computers can be realized, the Los Alamos team counters that artful use of feedback makes it possible to use existing optical components instead. The new approach, currently at the simulation stage, suggests that a more practical route can be followed to build effective quantum computers. Current methods use bulky and expensive equipment such as nuclear magnetic-resonance imaging systems, and the quantum states used to encode quantum bits, or "qubits,"are maintained at temperatures close to absolute zero. However, at room temperature, photons exhibit quantum behavior, and a lot of known technology can manipulate them. "The double-slit experiment, where a single photon goes through whichever parallel slit you put a photodetector behind, clearly demonstrates the quantum-mechanical aspects of photons," said Los Alamos National Laboratories researcher Emanuel Knill. "Others thought you needed a new kind of nonlinear optical component to make quantum computers with photons. We have shown that all you need is feedback." Knill's work was done with another Los Alamos researcher, Raymond Laflamme, and with professor Gerard Milburn of the University of Queensland, St. Lucia, Australia. Photons can act as the data in quantum computers by virtue of their dual wave/particle nature. The famous double-slit experiment sends a single photon toward two parallel slits and locates a single photodetector behind first one slit and then the other. No matter which slit the photodetector is put behind, it always detects the single photon. How does the photon "know"which slit to go through? The answer is that it is acting as a wave instead of a particle, and thus goes through both until it is measured by the photodetector. The act of measurement instantaneously localizes the "particle" aspect of the photon essentially causing it to "condense" behind whichever slit the measurement is made. For the optical quantum computer blueprint provided by the labs, the phase state as polarized either vertically or horizontally works off the ability of photons to represent 1s and 0s. With all quantum bits, the phase of a photon's wave can simultaneously represent both 1 and 0, since its phase can differ depending on the exact moment it is measured. Afterward that is no longer possible; the phase has become fixed as one or the other by the very act of measurement. "Until our work, it was thought that the only way to get photons to interact with each other was with nonlinear optics, which is very difficult to implement,"said Knill. "Nonlinear media work fine if you send laser beams through them, but if you only send single photons through, essentially nothing happens." To provide the necessary nonlinear coupling among qubits, using photons, the team of Knill, Laflamme and Milburn fell back on one of the most useful engineering techniques ever invented feedback. By employing feedback from the outputs of the photodetectors, they were able to simulate the effect of nonlinear media without the disadvantages of actually using them. Essentially, the optical components capable of handling single photons were bent to the service of nonlinear couplings through feedback. "People never thought to use feedback from the result of a photodetector, but that is where our nonlinearity comes from it was there all along," Knill explained. This technique was not tried because researchers assumed they could not reuse measurements in quantum computations. "We discovered that you can use feedback, and that you can replace a nonlinear component with it," said Laflamme. As in all quantum-mechanical systems, the most important principle has been to preserve "coherence" that is, to make sure that the qubits remain "unobserved" in their nebulous superposition of both 1 and 0 during a calculation. Once a measurement is made of a quantum-mechanical state, the system reverts to a normal digital system and the advantage of quantum computations is lost. That was why it was thought that feedback could not work because it would destroy the quantum coherence that forms the basis for quantum algorithms. However, Knill, Laflamme and Milburn have shown that systems that combine qubits with ordinary bits in the feedback loop can simulate nonlinear optical components. "What we do essentially is destroy coherence in one place and manage to indirectly reintroduce it elsewhere so that only the coherence we don't care about gets lost in the measurement," said Knill. The basic idea is that the original qubits to be used in a calculation can be prepared ahead of time by entangling them with what the researchers call "helper" qubits. Entangling ensures that the helper bits maintain the same state as the originals, even after they have gone through a quantum calculation. The helper qubits can then be independently processed with standard optical components, and after the calculation, they can be measured without destroying the coherence of the originals. The results of measuring the helper qubits are introduced into the feedback loop, which then simulates a nonlinear optical component for a single photon. There is a price for the destroyed coherence of the helper bits, however. According to the researchers, the labs' quantum computer blueprint will make more errors than the already error-prone quantum computers designed elsewhere. To compensate, the team carefully architected their design to use built-in error correction in two subsequent stages. "The most important discovery in quantum computing in the last five years has been quantum error correction," said Laflamme. "Using quantum error correction, we can mitigate the effect of the errors we introduce with our measurements." The resulting architecture uses three distinct stages. In stage one, helper photons are generated by entanglement and teleported to a circuit running in parallel with the main calculation. Measurement of the helper bits, after the main calculation, is then introduced into the feedback loop to simulate the effect of a nonlinear coupling between two photons. "We know when it succeeds by measuring the helper qubit. If the outcome is good, then we go on with whatever else we are going to do in the calculations, but if it fails then we forget about what we just did and start over," said Knill. But calculations made in this way are successful only with a quantum probability of 1/4, which necessitates the second stage of the architecture. In stage two, the success probability of stage one can be tuned arbitrarily close to 1. Unfortunately, however, the computing resources needed to achieve 100 percent accuracy can grow exponentially. To solve this problem, the researchers used a third error-correction stage drawing on the recent work of other scientists. By freely providing the blueprint to the research community, they hope to interest engineers in setting up real-world experiments.

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█ BRIAN HOYLE A supercomputer is a powerful computer that possesses the capacity to store and process far more information than is possible using a conventional personal computer. An illustrative comparison can be made between the hard drive capacity of a personal computer and a super-computer. Hard drive capacity is measured in terms of gigabytes. A gigabyte is one billion bytes. A byte is a unit of data that is eight binary digits (i.e., 0's and 1's) long; this is enough data to represent a number, letter, or a typographic symbol. Premium personal computers have a hard drive that is capable of storing on the order of 30 gigabytes of information. In contrast, a supercomputer has a capacity of 200 to 300 gigabytes or more. Another useful comparison between supercomputers and personal computers is in the number of processors in each machine. A processor is the circuitry responsible for handling the instructions that drive a computer. Personal computers have a single processor. The largest supercomputers have thousands of processors. This enormous computation power makes supercomputers capable of handling large amounts of data and processing information extremely quickly. For example, in April 2002, a Japanese supercomputer that contains 5,104 processors established a calculation speed record of 35,600 gigaflops (a gigaflop is one billion mathematical calculations per second). This exceeded the old record that was held by the ASCI White-Pacific supercomputer located at the Lawrence Livermore National Laboratory in Berkeley, California. The Livermore supercomputer, which is equipped with over 7,000 processors, achieves 7,226 gigaflops. These speeds are a far cry from the first successful supercomputer, the Sage System CDC 6600, which was designed by Seymour Cray (founder of the Cray Corporation) in 1964. His computer had a speed of 9 megaflops, thousands of times slower than the present day versions. Still, at that time, the CDC 6600 was an impressive advance in computer technology. Beginning around 1995, another approach to designing supercomputers appeared. In grid computing, thousands of individual computers are networked together, even via the Internet. The combined computational power can exceed that of the all-in-one supercomputer at far less cost. In the grid approach, a problem can be broken down into components, and the components can be parceled out to the various computers. As the component problems are solved, the solutions are pieced back together mathematically to generate the overall solution. The phenomenally fast calculation speeds of the present day supercomputers essentially corresponds to "real time," meaning an event can be monitored or analyzed as it occurs. For example, a detailed weather map, which would take a personal computer several days to compile, can be complied on a supercomputer in just a few minutes. Supercomputers like the Japanese version are built to model events such as climate change, global warming, and earthquake patterns. Increasingly, however, supercomputers are being used for security purposes such as the analysis of electronic transmissions (i.e., email, faxes, telephone calls) for codes. For example, a network of supercomputers and satellites that is called Echelon is used to monitor electronic communications in the United States, Canada, United Kingdom, Australia, and New Zealand. The stated purpose of Echelon is to combat terrorism and organized crime activities. The next generation of supercomputers is under development. Three particularly promising technologies are being explored. The first of these is optical computing. Light is used instead of using electrons to carry information. Light moves much faster than an electron can, therefore the speed of transmission is greater. The second technology is known as DNA computing. Here, recombining DNA in different sequences does calculations. The sequence(s) that are favored and persist represent the optimal solution. Solutions to problems can be deduced even before the problem has actually appeared. The third technology is called quantum computing. Properties of atoms or nuclei, designated as quantum bits, or qubits, would be the computer's processor and memory. A quantum computer would be capable of doing a computation by working on many aspects of the problem at the same time, on many different numbers at once, then using these partial results to arrive at a single answer. For example, deciphering the correct code from a 400-digit number would take a supercomputer millions of years. However, a quantum computer that is about the size of a teacup could do the job in about a year. █ FURTHER READING: Stork, David G. (ed) and Arthur C. Clarke. HAL's Legacy: 2001's Computer Dream and Reality. Boston: MIT Press, 1998. Cray Corporation. "What Is a Supercomputer?" Supercomputing. 2002. < http://www.cray.com/supercomputing >(15 December 2002). The History of Computing Foundation. "Introduction to Supercomputers." Supercomputers. October 13, 2002. < http://www.thocp.net/hardware/supercomputers.htm >(15 December 2002).

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In quantum mechanics, a singlet originally meant a linked set of particles whose net angular momentum is zero, that is, whose overall spin quantum number , though this meaning has since been generalized to include other situations. The link between the particles may be historical, such as two widely separated particles whose current angular momentum states originated in a single quantum event; or ongoing, such as two particles bound by charge. A set of linked particles that lacks net angular momentum is said to be in a singlet state. Singlets and the related spin concepts of doublets and triplets occur frequently in atomic physics and nuclear physics, where one often needs to determine the total spin of a collection of particles. Since the only observed fundamental particle with zero spin is the extremely inaccessible Higgs boson, singlets in everyday physics are necessarily composed of sets of particles whose individual spins are non-zero, e.g. 1/2 or 1. The origin of the term "singlet" is that bound quantum systems with zero net angular momentum emit photons within a single spectral line, as opposed to double lines (doublet state) or triple lines (triplet state). The number of spectral lines in this singlet-style terminology has a simple relationship to the spin quantum number: , and . Singlet-style terminology is also used for systems whose mathematical properties are similar or identical to angular momentum spin states, even when traditional spin is not involved. In particular, the concept of isospin was developed early in the history of particle physics to address the remarkable similarities of protons and neutrons. Within atomic nuclei, protons and neutrons behave in many ways as if they were a single type of particle, the nucleon, with two states. The proton-neutron pair thus by analogy was referred to as a doublet, and the hypothesized underlying nucleon was assigned a spin-like doublet quantum number to differentiate between those two states. Thus the neutron became a nucleon with isospin , and the proton a nucleon with . The isospin doublet notably shares the same SU(2) mathematical structure as the angular momentum doublet. It should be mentioned that this early particle physics focus on nucleons was subsequently replaced by the more fundamental quark model, in which a proton or neutron is interpreted as bound systems of three quarks. The isospin analogy also applies to quarks, and is the source of the names up (as in "isospin up") and down (as in "isospin down") for the quarks found in protons and neutrons. While for angular momentum states the singlet-style terminology is seldom used beyond triplets (spin 1), it has proven historically useful for describing much larger particle groups and subgroups that share certain features and are distinguished from each other by quantum numbers beyond spin. An example of this broader use of singlet-style terminology is the nine-member "nonet" of the pseudoscalar mesons. The simplest possible angular momentum singlet is a set (bound or unbound) of two spin 1/2 (fermion) particles that are oriented so that their spin directions ("up" and "down") oppose each other; that is, they are antiparallel. The simplest possible bound particle pair capable of exhibiting the singlet state is positronium, which consists of an electron and positron (antielectron) bound by their opposite electric charges. The electron and positron in positronium can also have identical or parallel spin orientations, which results in an experimentally distinct form of positronium with a spin 1 or triplet state. An unbound singlet consists of a pair of entities small enough to exhibit quantum behavior (e.g. particles, atoms, or small molecules), not necessarily of the same type, for which four conditions hold: 1) the spins of the two entities are of equal magnitude; 2) the current spin values of both entities originated within a single well-defined quantum event (wave function) at some earlier location in classical space and time; 3) the originating wave function relates the two entities such a way that their net angular momentum must be zero, which in turn means that if and when they are detected experimentally, conservation of angular momentum will require their spins to be in full opposition (antiparallel); and 4) their spin states have remained unperturbed since the originating quantum event, which is equivalent to asserting that there exists no classical information (observation) of their status anywhere within the universe. Any spin value can be used for the pair, but the entanglement effect will be strongest both mathematically and experimentally if the spin magnitude is as small as possible, with the maximum possible effect occurring for entities with spin 1/2 (e.g., electrons). Early thought experiments for unbound singlets usually assumed the use of two antiparallel spin 1/2 electrons. However, actual experiments have tended to focus instead on using pairs of spin 1 photons. While the entanglement effect is somewhat less pronounced with such spin 1 particles, photons are easier to generate in correlated pairs and (usually) easier to keep in an unperturbed quantum state. The ability of positronium to form both singlet and triplet states is described mathematically by saying that the product of two doublet representations (meaning the electron and positron, which are both spin 1/2 doublets) can be decomposed into the sum of an adjoint representation (the triplet or spin 1 state) and a trivial representation (the singlet or spin 0 state). While the particle interpretation of the positronium triplet and singlet states is arguably more intuitive, the mathematical description enables precise calculations of quantum states and probabilities. This greater mathematical precision for example makes it possible to assess how singlets and doublets behave under rotation operations. Since a spin 1/2 electron transforms as a doublet under rotation, its experimental response to rotation can be predicted by using the fundamental representation of that doublet, specifically the Lie group SU(2). Applying the operator to the spin state of the electron thus will always result in , or spin 1/2, since the spin-up and spin-down states are both eigenstates of the operator with the same eigenvalue. Similarly, for a system of two electrons it is possible to measure the total spin by applying , where acts on electron 1 and acts on electron 2. Since this system has two possible spins, it also has two possible eigenvalues and corresponding eigenstates for the total spin operator, corresponding to the spin 0 and spin 1 states. Singlets and Entangled States It is important to realize that particles in singlet states need not be locally bound to each other. For example, when the spin states of two electrons are correlated by their emission from a single quantum event that conserves angular momentum, the resulting electrons remain in a shared singlet state even as their separation in space increases indefinitely over time, provided only that their angular momentum states remain unperturbed. In Dirac notation this distance-indifferent singlet state is usually represented as: The possibility of spatially extended unbound singlet states has considerable historical and even philosophical importance, since considering such states eventually led to experimental exploration and verification of what is now called quantum entanglement. Quantum entanglement is the ability of quantum systems to maintain relationships that appear to violate the principle of locality, which Albert Einstein considered fundamental and defended throughout his life. Along with Podolsky and Rosen, Einstein proposed the EPR paradox thought experiment to help define his concerns with the non-locality of spatially distributed singlets, using it as a way to assert that quantum mechanics was incomplete. The difficulty captured by the EPR thought experiment was that by perturbing the angular momentum state of either of the two particles in a spatially distributed singlet state, the quantum state of the remaining particle appears to be "instantaneously" altered, even if the two particles have over time become separated by light years of distance. A critical insight made decades later by John Stewart Bell, who ironically was a strong advocate of Einstein's locality-first perspective, showed that his Bell's theorem could be used to assess the existence or non-existence of singlet entanglement experimentally. The irony was that instead of disproving entanglement, which was Bell's hope, subsequent experiments instead established the reality of entanglement. In fact, there now exist commercial quantum encryption devices whose operation depends fundamentally on the existence and behavior of spatially extended singlets. A weaker form of Einstein's locality principle remains intact, which is this: Classical, history-setting information cannot be transmitted faster than the speed of light c, not even by using quantum entanglement events. This weaker form of locality is less conceptually elegant than Einstein's absolute locality, but is sufficient to prevent the emergence of causality paradoxes.

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Scientists capture the speediest ever motion in a molecule The fastest ever observations of protons moving within a molecule open a new window on fundamental processes in chemistry and biology, researchers report today in the journal Science. Their capturing of the movements of the lightest and therefore speediest components of a molecule will allow scientists to study molecular behaviour previously too fast to be detected. It gives a new in-depth understanding of how molecules behave in chemical processes, providing opportunities for greater study and control of molecules, including the organic molecules that are the building blocks of life. The high speed at which protons can travel during chemical reactions means their motion needs to be measured in units of time called attoseconds, with one attosecond equating to one billion-billionth of a second. The teams observation of proton motion with an accuracy of 100 attoseconds in hydrogen and methane molecules is the fastest ever recorded. Dr John Tisch of Imperial College London says: "Slicing up a second into intervals as miniscule as 100 attoseconds, as our new technique enables us to do, is extremely hard to conceptualise. Its like chopping up the 630 million kilometres from here to Jupiter into pieces as wide as a human hair." Professor Jon Marangos, Director of the Blackett Laboratory Laser Consortium at Imperial, says this new technique means scientists will now be able to measure and control the ultra-fast dynamics of molecules. He says: "Control of this kind underpins an array of future technologies, such as control of chemical reactions, quantum computing and high brightness x-ray light sources for material processing. We now have a much clearer insight into what is happening within molecules and this allows us to carry out more stringent testing of theories of molecular structure and motion. This is likely to lead to improved methods of molecular synthesis and the nano-fabrication of a new generation of materials." Lead author Dr Sarah Baker of Imperial College believes that the technique is also exciting because of its experimental simplicity. She says: "We are very excited by these results, not only because we have watched motion occurring faster than was previously possible, but because we have achieved this using a compact and simple technique that will make such study accessible to scientists around the world." To make this breakthrough, scientists used a specially built laser system capable of producing extremely brief pulses of light. This pulsed light has an oscillating electrical field that exerts a powerful force on the electrons surrounding the protons, repeatedly tearing them from the molecule and driving them back into it. This process causes the electrons to carry a large amount of energy, which they release as an x-ray photon before returning to their original state. How bright this x-ray is depends on how far the protons move in the time between the electrons removal and return. The further the proton moves, the lower the intensity of the x-ray, allowing the team to measure how far a proton has moved during the electron oscillation period. Abigail Smith | EurekAlert! The most recent press releases about innovation >>> Die letzten 5 Focus-News des innovations-reports im Überblick: More and more automobile companies are focusing on body parts made of carbon fiber reinforced plastics (CFRP). However, manufacturing and repair costs must be further reduced in order to make CFRP more economical in use. Together with the Volkswagen AG and five other partners in the project HolQueSt 3D, the Laser Zentrum Hannover e.V. (LZH) has developed laser processes for the automatic trimming, drilling and repair of three-dimensional components. Automated manufacturing processes are the basis for ultimately establishing the series production of CFRP components. In the project HolQueSt 3D, the LZH has... Reflecting the structure of composites found in nature and the ancient world, researchers at the University of Illinois at Urbana-Champaign have synthesized thin carbon nanotube (CNT) textiles that exhibit both high electrical conductivity and a level of toughness that is about fifty times higher than copper films, currently used in electronics. "The structural robustness of thin metal films has significant importance for the reliable operation of smart skin and flexible electronics including... The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets. Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the... Physicists in Garching observe novel quantum effect that limits the number of emitted photons. The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called... Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project. Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a...

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Putting a hole in the center of the donut--a mid-nineteenth-century invention--allows the deep-fried pastry to cook evenly, inside and out. As it turns out, the hole in the center of the donut also holds answers for a type of more efficient and reliable quantum information teleportation, a critical goal for quantum information science. Quantum teleportation is a method of communicating information from one location to another without moving the physical matter to which the information is attached. Instead, the sender (Alice) and the receiver (Bob) share a pair of entangled elementary particles--in this experiment, photons, the smallest units of light--that transmit information through their shared quantum state. In superdense teleportation of quantum information, Alice (near) selects a particular set of states to send to Bob (far), using the hyperentangled pair of photons they share. The possible states Alice may send are represented as the points on a donut shape, here artistically depicted in sharp relief from the cloudy silhouette of general quantum state that surrounds them. To transmit a state, Alice makes a measurement on her half of the entangled state, which has four possible outcomes shown by red, green, blue, and yellow points. She then communicates the outcome of her measurement (in this case, yellow, represented by the orange streak connecting the two donuts) to Bob using a classical information channel. Bob then can make a corrective rotation on his state to recover the state that Alice sent. Image by Precision Graphics, copyright Paul Kwiat, University of Illinois at Urbana-Champaign In simplified terms, Alice encodes information in the form of the quantum state of her photon. She then sends a key to Bob over traditional communication channels, indicating what operation he must perform on his photon to prepare the same quantum state, thus teleporting the information. Quantum teleportation has been achieved by a number of research teams around the globe since it was first theorized in 1993, but current experimental methods require extensive resources and/or only work successfully a fraction of the time. Now, by taking advantage of the mathematical properties intrinsic to the shape of a donut--or torus, in mathematical terminology--a research team led by physicist Paul Kwiat of the University of Illinois at Urbana-Champaign has made great strides by realizing "superdense teleportation". This new protocol, developed by physicist and paper co-author Herbert Bernstein of Hampshire College in Amherst, MA, effectively reduces the resources and effort required to teleport quantum information, while at the same time improving the reliability of the information transfer. With this new protocol, the researchers have experimentally achieved 88 percent transmission fidelity, twice the classical upper limit of 44 percent. The protocol uses pairs of photons that are "hyperentangled"--simultaneously entangled in more than one state variable, in this case in polarization and in orbital angular momentum--with a restricted number of possible states in each variable. In this way, each photon can carry more information than in earlier quantum teleportation experiments. At the same time, this method makes Alice's measurements and Bob's transformations far more efficient than their corresponding operations in quantum teleportation: the number of possible operations being sent to Bob as the key has been reduced, hence the term "superdense." Kwiat explains, "In classical computing, a unit of information, called a bit, can have only one of two possible values--it's either a zero or a one. A quantum bit, or qubit, can simultaneously hold many values, arbitrary superpositions of 0 and 1 at the same time, which makes faster, more powerful computing systems possible. "So a qubit could be represented as a point on a sphere, and to specify what state it is, one would need longitude and latitude. That's a lot of information compared to just a 0 or a 1." "What makes our new scheme work is a restrictive set of states. The analog would be, instead of using a sphere, we are going to use a torus, or donut shape. A sphere can only rotate on an axis, and there is no way to get an opposite point for every point on a sphere by rotating it--because the axis points, the north and the south, don't move. With a donut, if you rotate it 180 degrees, every point becomes its opposite. Instead of axis points you have a donut hole. Another advantage, the donut shape actually has more surface area than the sphere, mathematically speaking--this means it has more distinct points that can be used as encoded information." Lead author, Illinois physics doctoral candidate Trent Graham, comments, "We are constrained to sending a certain class of quantum states called 'equimodular' states. We can deterministically perform operations on this constrained set of states, which are impossible to perfectly perform with completely general quantum states. Deterministic describes a definite outcome, as opposed to one that is probabilistic. With existing technologies, previous photonic quantum teleportation schemes either cannot work every time or require extensive experimental resources. Our new scheme could work every time with simple measurements." This research team is part of a broader collaboration that is working toward realizing quantum communication from a space platform, such as the International Space Station, to an optical telescope on Earth. The collaboration--Kwiat, Graham, Bernstein, physicist Jungsang Kim of Duke University in Durham, NC, and scientist Hamid Javadi of NASA's Jet Propulsion Laboratory in Pasadena, CA--recently received funding from NASA Headquarter's Space Communication and Navigation program (with project directors Badri Younes and Barry Geldzahler) to explore the possibility. "It would be a stepping stone toward building a quantum communications network, a system of nodes on Earth and in space that would enable communication from any node to any other node," Kwiat explains. "For this, we're experimenting with different quantum state properties that would be less susceptible to air turbulence disruptions." The team's recent experimental findings are published in the May 28, 2015 issue of Nature Communications, and represent the collaborative effort Kwiat, Graham, and Bernstein, as well as physicist Tzu-Chieh Wei of State University of New York at Stony Brook, and mathematician Marius Junge of the University of Illinois. Siv Schwink | EurekAlert! New quantum liquid crystals may play role in future of computers 21.04.2017 | California Institute of Technology Light rays from a supernova bent by the curvature of space-time around a galaxy 21.04.2017 | Stockholm University The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets. Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the... The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called... Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project. Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a... Two researchers at Heidelberg University have developed a model system that enables a better understanding of the processes in a quantum-physical experiment... Glaciers might seem rather inhospitable environments. However, they are home to a diverse and vibrant microbial community. It’s becoming increasingly clear that they play a bigger role in the carbon cycle than previously thought. A new study, now published in the journal Nature Geoscience, shows how microbial communities in melting glaciers contribute to the Earth’s carbon cycle, a... 20.04.2017 | Event News 18.04.2017 | Event News 03.04.2017 | Event News 21.04.2017 | Physics and Astronomy 21.04.2017 | Health and Medicine 21.04.2017 | Physics and Astronomy

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Simon Watson demystifies the complex world of quantum computing Quantum computers are regularly heralded as the future of computing, harnessing the power of atoms and elementary particles to perform calculations that today’s computers could only dream of. Quite how this remarkable feat is achieved is either complicated with jargon such as ‘qubits’, ‘superposition’ and ‘entanglement’ with no further description, or dismissed as too complicated for a layman. This article aims to explain how quantum computers work, why they’re faster than classical computers, and why they’re not a replacement for them. Before we can describe how a quantum computer works, we need to understand today’s classical computers. Currently, computers work by manipulating ‘bits’ of data. A bit is something that can take one of two values, commonly written as 0 or 1. For example, a coin can be either heads or tails, or a light-switch can be on or off. In the case of a computer, the values may be a charged or discharged capacitor in memory, or the presence or absence of a groove on a CD. Computers operate on strings of eight bits, termed a ‘byte’. These bytes form instructions sent to the computer’s processor, directing it to perform functions such as adding two numbers, printing to the screen, or writing to memory. Quantum computers work fundamentally differently. Rather than using a difference in electric voltage to encode the bit values, they use the physical properties of fundamental particles or atoms. As long as there are two different measurable states, a bit value can be stored. For example, electrons are sub-atomic particles that have a property called spin. This can be imagined similar to the rotation of a ball around its axis; just as a ball can rotate clockwise or anti-clockwise, electrons can have a spin value of 1/2 or -1/2 (termed ‘up’ and ‘down’). The bit value can therefore be assigned by measuring the spin state of the electron. Another example would be the polarization state of light. Light travels through space as a transverse wave, oscillating perpendicular to its direction of motion. A wave travelling along for example the z-axis can be oscillating about either the x- or y-axis. We could therefore store the bit value by whether the light is horizontally- or vertically-linearly polarized. Were the analogy to end there, quantum computing would be no more complicated than classical computing. However, in 1930 theoretical physicist Paul Dirac published the first edition of his book The Principles of Quantum Mechanics. In it, he introduced the world to the revolutionary concept of ‘quantum superposition’ that now forms the backbone of quantum mechanics. Dirac was trying to explain the baffling evidence that light acts as both a wave and a particle–the so-called ‘wave-particle duality’ theory. On the one hand, Thomas Young’s double-slit experiment showed that when a laser was shone through two parallel slits, it diffracted like a classical wave, with the two waves interfering with each other where they were out of phase. In contrast, alternative evidence, such as the emission of photoelectrons by atoms when they absorb light of a particular frequency, showed that light was composed of discrete particles with definite energy and momentum, termed photons. Dirac considered the problem of a beam of light containing a single photon passing through a double-slit. For the light to pass through the apparatus and cause an interfering diffraction pattern the single photon must partly go through both slits, with each part interacting with the other–it is in a superposition! This idea of wave-particle duality and superposition was later shown to not be restricted to light, but universally extended to all particles. Quantum superposition therefore holds that a physical system exists at some probability in all possible states simultaneously. This phenomenon of superposition becomes even more bizarre when you extend it to particles that originate from the same source or are brought to interact together. Such particles do not exist in superposition independent of each other, but rather their quantum states become ‘entangled’ so that their physical properties can only be described relative to each other. For example, a particle with spin 0 may decay into two particles, each with spin 1/2. These particles, because they are entangled, exist in superposition where in addition to both being spin up or down at some probability, they have a probability of being in their anti-correlated spin states up/down and down/up simultaneously. They therefore do not individually have a spin direction of their own, but rather their state is defined as being opposite each other. A system with 10 qubits holds the same information as 1024 classical bits, while 300 qubits holds more information than there are particles in the universe! If we return now to the quantum computer, quantum mechanics dictates that in addition to the quantum bit (termed a ‘qubit’) having two measurable states (for example, spin up or down), it exists in a superposition where it has both states at the same time. It therefore has some probability of being both 0 and 1 at the same time. This means that the amount of information each qubit can hold is significantly larger than its equivalent bit. Consider a computer consisting of two classical bits. To fully describe the possible states of the system (00, 01, 10, 11), only the values of each bit are required. So the computer has an information capacity of two. The corresponding quantum computer has its entangled qubits in a superposition of all states, with a separate probability of being in each state: the probability of being in state |00> (for example, both electrons have spin down) is a; the probability of them being in state |11> (for example both electrons have spin up) is b; and the probability of being in states |01+10> and |01-10> (that is in an entangled superposition) is c and d respectively. To fully describe this quantum system, four probability values (a, b, c, d) are required. This two-qubit system therefore holds double the amount of information as the classical two-bit system, with each additional qubit exponentially increasing the amount of information it can contain. A system with 10 qubits holds the same information as 1024 classical bits, while 300 qubits holds more information than there are particles in the universe! However, accessing the information stored in these qubits is not a simple matter, as superposition doesn’t automatically make any computation faster than a classical computer. The quantum computer must be designed, and quantum algorithms written specially, to utilise the probabilities in the entangled qubits’ superimposed states and get the desired speedup. Otherwise it is nothing more than a very fancy, but expensive, classical computer containing only a few bits. This severely constrains its applicability to solving specific problems, such as using Shor’s quantum algorithm for factorising integers. While this doesn’t sound very exciting, the widely-used public-key cryptography relies on the intractable time it takes to factorize very large numbers in keeping messages encrypted. If quantum computers can factorise quickly, these encrypted messages can be easily read. However, to date the largest integer that has been successfully factorised by a quantum computer is 143! Much money and research is therefore being invested in this field; in 2013 Canadian company D-Wave claimed to have a 512-qubit computer that solved a Travelling Salesman problem over 3,600 times faster than a classical computer. So, while quantum computers will probably not replace personal computers, they are also not just a proof of concept. Simon Watson is a postdoctoral researcher at the Wellcome Trust Sanger Institute. Featured image: jurvetson

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Researchers are hoping to improve high-precision clocks by entangling their atoms. by Patrick L Barry and Dr Tony Einstein called it "spooky action at a distance." Now researchers are using an astonishing property of quantum mechanics called "entanglement" to improve atomic clocks - humanity's most precise way to measure time. Entangled clocks could be as much as 1000 times more stable than their non-entangled This improvement would benefit pilots, farmers, hikers - in short, anyone who uses the Global Positioning System (GPS). Each of the 24+ GPS satellites carries four atomic clocks on board. By triangulating time signals broadcast from orbit, GPS receivers on the ground can pinpoint their own location NASA uses atomic clocks for spacecraft navigation. Geologists use them to monitor continental drift and the slowly changing spin of our planet. Physicists use them to check theories of gravity. An entangled atomic clock might keep time precisely enough to test the value of the Fine Structure Constant, one of the fundamental constants of physics. "The ability to measure time with very high precision is an invaluable tool for scientific research and for technology," says Alex Kuzmich, a physicist at the Georgia Institute of Technology. Through its office of Biological and Physical Research, NASA recently awarded a grant to Kuzmich and his colleagues to support their research. Kuzmich has studied quantum entanglement for the last 10 years and has recently turned to exploring how it can be applied to atomic clocks. Einstein never liked entanglement. It seemed to run counter to a central tenet of his theory of relativity: nothing, not even information, can travel faster than the speed of light. In quantum mechanics, all the forces of nature are mediated by the exchange of particles such as photons, and these particles must obey this cosmic speed limit. So an action "here" can cause no effect "over there" any sooner than it would take light to travel there in a vacuum. Image by Patrick L. Barry a measurement on one entangled particle affects the properties of the other instantaneously. But two entangled particles can appear to influence one another instantaneously, whether they're in the same room or at opposite ends of the Universe. Pretty spooky occurs when two or more particles interact in a way that causes their fates to become linked: It becomes impossible to consider (or mathematically describe) each particle's condition independently of the others'. Collectively they constitute a single quantum state. Two entangled particles often must have opposite values for a property - for example, if one is spinning in "up" direction, the other must be spinning in the "down" direction. Suppose you measure one of the entangled particles and, by doing so, you nudge it "up." This causes the entangled partner to spin "down." Making the measurement "here" affected the other particle "over there" instantaneously, even if the other particle was a million miles away. While physicists and philosophers grapple with the implications for the nature of causation and the structure of the Universe, some physicists are busy putting entanglement to work in applications such as "teleporting" atoms and producing uncrackable encryption. Atomic clocks also stand to benefit. "Entangling the atoms in an atomic clock reduces the inherent uncertainties in the system," Kuzmich explains. At the heart of every atomic clock lies a cloud of atoms, usually cesium or rubidium. The natural resonance's of these atoms serve the same purpose as the pendulum in a grandfather clock. Tick-tock-tick-tock. A laser beam piercing the cloud can count the oscillations and use them to keep time. This is how an atomic clock works. "The best atomic clocks on Earth today are stable to about one part in 1015," notes Kuzmich. That means an observer would have to watch the clock for 1015 seconds or 30 million years to see it gain or lose a single second. Click on the image to learn more. Lasers are a key ingredient of atomic clocks - both the ordinary and The precision of an atomic clock depends on a few things, including the number of atoms being used. The more atoms, the better. In a normal atomic clock, the precision is proportional to the square-root of the number of atoms. So having, say, 4 times as many atoms would only double the precision. In an entangled atomic clock, however, the improvement is directly proportional to the number of atoms. Four times more atoms makes a 4-times better clock. Using plenty of atoms, it might be possible to build a "maximally entangled clock stable to about one part in 1018," says Kuzmich. You would have to watch that clock for 1018 seconds or 30 billion years to catch it losing a single second. Kuzmich plans to use the lasers already built-in to atomic clocks to create the entanglement. "We will measure the phase of the laser light passing through the cloud of atoms," he explains. Measuring the phase "tweaks the laser beam," and if the frequency of the laser has been chosen properly, tweaking the beam causes the atoms to become entangled. Or, as one quantum physicist might say to another, "such a procedure amounts to a quantum non-demolition (QND) measurement on the atoms, and results in preparation of a Squeezed Spin State." How soon an entangled clock could be built - much less launched into space aboard a hypothetical new generation of GPS satellites - is difficult to predict, cautions Kuzmich. The research is still at the stage of just demonstrating the principle. Building a working prototype is probably several But thanks to research such as this, having still-better atomic clocks available to benefit science and technology is only a matter

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Wonders of Bird Migration - and Threatened Asian Wetlands Birds may undertake marathon flights using astonishing navigation skills – yet are threatened by careless habitat destruction As you read this, flocks of bar-tailed godwits may be departing the south coast of Alaska. These brown wading birds appear fairly nondescript – like smaller cousins of curlew – but are embarking on one of the greatest migratory journeys known. The godwits are will fly non-stop for some 11,700 km (7270 miles), the distance from Hong Kong to Los Angeles, and the longest known flight by any creature. After perhaps eight days, they will arrive at their destination, coastal mudflats in New Zealand. Church bells will ring to welcome flocks touching down in Christchurch, where they are regarded as harbingers of spring. Bird migration is among the wonders of the natural world. It has evolved in response to the great rewards of being able to exploit food in a place like Arctic tundra, whilst also fleeing winter conditions that make life impossible for a many birds. Yet there are also immense risks, and countless birds die en route. Amazing adaptations of migratory birds Migratory birds have a host of remarkable adaptations that enable them to undertake such journeys. To prepare for their autumn marathon, the bar-tailed godwits store energy, until fat comprises up to 55 percent of their body weight. Their livers, kidneys and intestines shrink, becoming almost useless, as the birds’ bodies become focused on flying. The godwits have innate weather forecasting skills: “All the departures we’ve observed to date were associated with low pressure systems,” noted Bob Gill, a wildlife biologist with the US Geological Survey’s Alaska Science Center, who was in a team studying godwits. “The birds get on the back side of these lows and get 900 to 1,200 kilometres (558 to 744 miles) of pretty strong tailwinds.” Built-in weather knowledge could also help the godwits avoid being buffeted by Pacific typhoons. No one knows for sure, just as no one fully understands how birds navigate. Navigation may involve quantum mechanics! Experiments have revealed that birds can use a range of methods to help with navigation. The most obvious of these is following familiar landmarks, such as rivers, coastlines and even highways that feature in their mental maps. At least some migrants have mental star maps, for orientation on clear nights, and indicating arrival at their destinations. The height and position of the sun can help birds judge the direction in which they’re headed. Yet this is no help in cloudy weather, nor can just observing the sun fix position when there are no landmarks in view – which proved so challenging for humans that it was not until the late 18th century that mariners could determine longitude while at sea. For precisely determining their position and direction, birds need a sense we don’t consciously possess: gauging the earth’s magnetic field. Experiments have shown that birds can orient using magnetic fields, and a change as small as a thousandth of the earth’s magnetic field can affect the navigation ability of European robins. Yet it is unclear how they sense magnetism. There were notions that iron-rich cells near pigeons’ beaks serve as tiny compasses. But researchers have found only white blood cells that do not produce electrical signals. The true answer could be far more bizarre. Robins’ magnetic sense requires them to see clearly – in turn leading to notions that it depends on a weird property of matter known as quantum entanglement. Possibly, light excites two electrons on a molecule of a suitable chemical, leading to one electron departing for another molecule of the same chemical. Though the electrons are now separate, their “spins” would be inextricably linked for a short time, during which they would be affected by the earth’s magnetic field. The magnetism could affect the chemical’s properties, resulting in subtle changes across the eye that lead to a bird “seeing” the earth’s magnetic field. A candidate for the chemical responsible for sensing magnetism is a protein known as cryptochrome. Intriguingly, fruit flies with cryptochrome receptors can navigate using magnetism; those without fly as if oblivious to the magnetic field. Bar-tailed Godwit: the marathon bird While the ways birds navigate remain mysterious, we have far more knowledge of the routes they take. This is partly thanks to satellite tracking of birds including the bar-tailed godwit given the code E7. E7 is a female bar-tailed godwit that was captured, tagged and fitted with a satellite transmitter in New Zealand in February 2007. No one was then certain that these godwits really flew the length of the Pacific in one flight, yet in autumn that year scientists monitored as she left Alaska on 29 August, passed near Fiji, and on 7 September landed at an estuary eight miles from where she had been captured. In March, E7 had made two other huge flights: taking her to coastal mudflats in north China, 10,300km (6400 miles) away, and then another 6500km (4500 miles) to Alaska. Threatened Yellow Sea wetlands While in north China, E7 spent five weeks refuelling in readiness for the breeding season ahead. Tens of thousands of other godwits were likewise refuelling at this and other wetlands around the Yellow Sea – which is among the world’s greatest areas for intertidal mudflats and the wildlife that depends on them. According to WWF China, the Yellow Sea is the most important site for migratory birds in the East Asian-Australasian Flyway, which encompasses the routes of a myriad species. Millions of birds pass through each year. Red Knot and Great Knot at Happy Island, southeast of Tianjin The godwits are among an outstanding variety of shorebirds that rely on the Yellow Sea wetlands as stopovers on their journeys. There are also geese, ducks, cranes, cormorants and other wetland birds. Some species are unique to east Asia, some face extinction; one, spoon-billed sandpiper, probably numbers less than 500 in all. Yet while the Yellow Sea should be a key region for conservation efforts, wetlands are being casually destroyed. Within the last decade, South Korea reclaimed an estuary eight times larger than Hong Kong Island. There are ongoing massive reclamation projects along the Chinese shore – notably in Tianjin which, ironically, is also building an artificial “eco city”. Water pollution is severe. Wetlands are also threatened elsewhere along the flyway, including in Hong Kong. As habitats dwindle, populations of the migratory birds depending on them will continue to decline. Year by year, there will be less wonder in the world. Perhaps a season will come when the church bells in Christchurch remain silent, as godwit flocks no longer arrive. Published in Sunday Morning Post, Hong Kong, on 2 September 2012. Useful links include: Bar-tailed Godwit (Limosa lapponica) on US Geological Survey website; includes the astonishing flights of godwit E7. On this site, there's Reclamations slaughtering Bohai Bay birds.

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Bohr and beyond: a century of quantum physics Our understanding of the quantum world began with Niels Bohr's discovery of the quantum atom in 1913. Bohr would be astounded by where his theory has since led, says Professor David Jamieson. Bohr's discovery of the quantum nature of the atom, published when he was a young man of 28, was an important pioneering contribution to the earliest days of quantum physics. This field emerged to explain the common sense-defying behaviour of atoms, molecules and light at the smallest scales, forming the foundations on which we have built one of the greatest and most successful theories of all time — quantum mechanics. What is quite remarkable to modern eyes was that Bohr had very little to go on. The true nature of the atom as an incredibly tiny nucleus surrounded by a cloud of orbiting electrons had only been discovered a few years earlier, in the separate work of physicists Thomson and Rutherford. Bohr's genius was to recognise that these electrons had many roles in a range of apparently different scenarios. He saw that electrons were behind the electric currents flowing in wires, the red hot glow of molten iron, and the production of light from electric discharges in gas-filled tubes. Bohr took the important elements of the emerging theories to explain all these different things, invented some new quantum mechanical principles and made it all work. In so doing he also managed to solve an important and troubling problem: that any electron moving in an orbit would have to spontaneously radiate away energy until it spiralled down and slowed to a stop — a view from classical physics that meant no atom could be stable. Bohr's quantum atom: nature is digital Like others, Bohr was keen to draw on our understanding of the orbit of planets around the sun in understanding the orbit of electrons in atoms. The planets are attracted by the powerful gravity of the sun, but their speed lets them settle into stable orbits rather than spiralling into the sun's gravitational field. In the case of the positively charged nucleus and the negatively charged electron, the mutual pull is the electric force. Classical physics dictates that an accelerating charge (like an electron in orbit) must give off electromagnetic radiation. The energy lost through radiation should make an electron slow in its orbit and quickly crash into the nucleus, which means no atom could be stable. This was clearly not true, and Bohr's solution to this conundrum was the first of two powerful ideas with which he introduced us to the quantum atom. He proposed that electrons in atoms are only stable in certain allowed orbits, which he called stationary states. This idea is an attribute of the wave-like nature of all matter at the nanoscale, and it is now understood as a fundamental principle of quantum mechanics. Bohr's second idea was that electrons dropping down from one stable orbit to another would radiate a single discrete packet of radiation, in the form of a photon of light. This shows the deep connection between light and matter, and that photons are all or nothing — there is no such thing as half a photon. Together these ideas tell us that nature is fundamentally digital at the atomic level, and they provide the basis for quantum mechanics. From theory to evidence Bohr was able to use his new theory to successfully explain the regularities in the pattern of light emitted from hot hydrogen gas, both in the laboratory and in the atmospheres of stars near and far. Heated hydrogen emits characteristic blue, red and violet light. Bohr showed that the light was given off by excited electrons as they settle into allowed stable orbits at lower energies. A photon of each of those colours of light corresponds to the energy difference between different allowed orbits. The radiation emerging from the atom as the electrons settle into stable orbits can tell us a lot about the nucleus. Shortly after Bohr's discovery, Henry Moseley discovered that energetic photons emitted from electrons settling into close orbits around the nucleus, typically in the x-ray part of the spectrum, could be used to discover gaps in the periodic table of the elements where new elements would later be found. Later, Bohr's theory was further developed to explain molecules and the basis of chemistry. One year after Bohr's theory appeared in the scientific journals, the British Association for the Advancement of Science held its 1914 meeting in Australia. In the old physics building at the University of Melbourne, Sir Ernest Rutherford presented a report on the new and controversial theory to delegates from the United Kingdom, Australia and New Zealand. This was one of the first public outings for the theory. Reports from the conference give a strong sense of the excitement created by Bohr's radical ideas — one delegate remarked "... I should like to say that although I have criticised certain parts of Bohr's theory adversely, no one can admire more its ingenuity and great suggestiveness." These were prophetic words! Today, Bohr's theory is applied to a range of scenarios that would have astounded the young Niels. He could never have imagined that his work would lead to PET (positron emission tomography) scanners that look inside our bodies, showing us the effect of diseases like cancer on the way our organs function. Bohr's theory explains the mutual orbit of electrons and positrons just before they annihilate each other, transforming into gamma rays that give rise to the PET scan image. And recent breakthroughs have led to some exciting new applications built on Bohr's theory, including our work in nanodiamonds and quantum computing at the Australian Centre of Excellence for Quantum Computation and Communication Technology. Bohr in today's science: nanodiamonds, quantum computers ... Bohr's theory can be adapted to explain the peculiar orbits of electrons around a single nitrogen atom inserted into a diamond crystal. The light photons emitted when these electrons change between their stationary states is incredibly bright, and signals the internal quantum state even at room temperature. These stationary states are susceptible to even the tiniest magnetic fields, affecting the colour of light given off. When a living cell is seeded with nanoscale diamond crystals containing single nitrogen atoms, the way the cellular electromagnetic machinery affects the emitted light tells us what is going on at these tiny scales. This could help us learn about the dynamics of biological neural networks, which is fundamental to gaining insight into information processing in the brain. We have also shown how modern nanotechnology allows us to program digital information into the quantum atom. Recognising that both the electrons and the nucleus in the quantum atom possess angular momentum, called spin, we have discovered how to amplify one billion-fold the subtle difference in energy between the two stable spin states of the nucleus of an engineered phosphorus atom in a silicon device. This could lead to a raft of new technologies built on the quantum atom. For example, instead of seeking information from the photons emerging from quantum atoms, we use photons in our single atom device as a means of artificially encoding information in the nuclear spin orientation. This could be the foundational component of a large-scale silicon quantum computer. In this device the electron spin is used for information processing and read-out, with the nuclear spin used as long lived memory for quantum information. A quantum computer could have revolutionary applications to the storage, processing and transmission of information. This would exploit the best characteristics of the quantum domain and the most important material for microelectronics, silicon, to build the proposed quantum internet of the mid-21st century. ... and the Higgs boson After Bohr published his ideas in 1913 he went on to found an important institute for theoretical physics in Copenhagen, and his great discovery was recognised by a Nobel Prize in 1922. He pledged support for founding the CERN laboratory in 1952 and then hosted the CERN theorists in his institute until they were ready to move to Geneva. Australian involvement in CERN led to the announcement in 2012 in Melbourne and Geneva of the discovery of the Higgs boson, the latest discovery in the deep journey into the quantum atom that Bohr helped start one hundred years ago! About the author:Prof David Jamieson is Head of the School of Physics of the University of Melbourne, and a Program Manager with the Centre of Excellence for Quantum Computation and Communication Technology. His research expertise is in the field of ion beam physics, particularly in the use of focused ion beams for materials modification and analysis. Published 18 July 2013

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Scientists demonstrate versatile, noise-tolerant quantum operations on a single electron While a classical bit found in conventional electronics exists only in binary 1 or 0 states, the more resourceful quantum bit, or 'qubit' is represented by a vector, pointing to a simultaneous combination of the 1 and 0 states. To fully implement a qubit, it is necessary to control the direction of this qubit's vector, which is generally done using fine-tuned and noise-isolated procedures. Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly -- is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. Their findings were published online Feb. 15, 2016, in Nature Photonics and will appear in the March print issue. "We tend to view quantum operations as very fragile and susceptible to noise, especially when compared to conventional electronics," remarked David Awschalom, the Liew Family Professor of Molecular Engineering and senior scientist at Argonne National Laboratory, who led the research. "In contrast, our approach shows incredible resilience to external influences and fulfills a key requirement for any practical quantum technology." When a quantum mechanical object, such as an electron, is cycled along some loop, it retains a memory of the path that it travelled, the Berry phase. To better understand this concept, the Foucault pendulum, a common staple of science museums helps to give some intuition. A pendulum, like those in a grandfather clock, typically oscillates back and forth within a fixed plane. However, a Foucault pendulum oscillates along a plane that gradually rotates over the course of a day due to Earth's rotation, and in turn knocks over a series of pins encircling the pendulum. The number of knocked-over pins is a direct measure of the total angular shift of the pendulum's oscillation plane, its acquired geometric phase. Essentially, this shift is directly related to the location of the pendulum on Earth's surface as the rotation of Earth transports the pendulum along a specific closed path, its circle of latitude. While this angular shift depends on the particular path traveled, Awschalom said, it remarkably does not depend on the rotational speed of Earth or the oscillation frequency of the pendulum. "Likewise, the Berry phase is a similar path-dependent rotation of the internal state of a quantum system, and it shows promise in quantum information processing as a robust means to manipulate qubit states," he said. A light touch In this experiment, the researchers manipulated the Berry phase of a quantum state within a nitrogen-vacancy (NV) center, an atomic-scale defect in diamond. Over the past decade and a half, its electronic spin state has garnered great interest as a potential qubit. In their experiments, the team members developed a method with which to draw paths for this defect's spin by varying the applied laser light. To demonstrate Berry phase, they traced loops similar to that of a tangerine slice within the quantum space of all of the potential combinations of spin states. "Essentially, the area of the tangerine slice's peel that we drew dictated the amount of Berry phase that we were able to accumulate," said Christopher Yale, a postdoctoral scholar in Awschalom's laboratory, and one of the co-lead authors of the project. This approach using laser light to fully control the path of the electronic spin is in contrast to more common techniques that control the NV center spin, through the application of microwave fields. Such an approach may one day be useful in developing photonic networks of these defects, linked and controlled entirely by light, as a way to both process and transmit quantum information. A noisy path A key feature of Berry phase that makes it a robust quantum logic operation is its resilience to noise sources. To test the robustness of their Berry phase operations, the researchers intentionally added noise to the laser light controlling the path. As a result, the spin state would travel along its intended path in an erratic fashion. However, as long as the total area of the path remained the same, so did the Berry phase that they measured. "In particular, we found the Berry phase to be insensitive to fluctuations in the intensity of the laser. Noise like this is normally a bane for quantum control," said Brian Zhou, a postdoctoral scholar in the group, and co-lead author. "Imagine you're hiking along the shore of a lake, and even though you continually leave the path to go take pictures, you eventually finish hiking around the lake," said F. Joseph Heremans, co-lead author, and now a staff scientist at Argonne National Laboratory. "You've still hiked the entire loop regardless of the bizarre path you took, and so the area enclosed remains virtually the same." These optically controlled Berry phases within diamond suggest a route toward robust and fault-tolerant quantum information processing, noted Guido Burkard, professor of physics at the University of Konstanz and theory collaborator on the project. "Though its technological applications are still nascent, Berry phases have a rich underlying mathematical framework that makes them a fascinating area of study," Burkard said. Steve Koppes | EurekAlert! Study offers new theoretical approach to describing non-equilibrium phase transitions 27.04.2017 | DOE/Argonne National Laboratory SwRI-led team discovers lull in Mars' giant impact history 26.04.2017 | Southwest Research Institute More and more automobile companies are focusing on body parts made of carbon fiber reinforced plastics (CFRP). However, manufacturing and repair costs must be further reduced in order to make CFRP more economical in use. Together with the Volkswagen AG and five other partners in the project HolQueSt 3D, the Laser Zentrum Hannover e.V. (LZH) has developed laser processes for the automatic trimming, drilling and repair of three-dimensional components. Automated manufacturing processes are the basis for ultimately establishing the series production of CFRP components. In the project HolQueSt 3D, the LZH has... Reflecting the structure of composites found in nature and the ancient world, researchers at the University of Illinois at Urbana-Champaign have synthesized thin carbon nanotube (CNT) textiles that exhibit both high electrical conductivity and a level of toughness that is about fifty times higher than copper films, currently used in electronics. "The structural robustness of thin metal films has significant importance for the reliable operation of smart skin and flexible electronics including... The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets. Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the... The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called... Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project. Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a... 20.04.2017 | Event News 18.04.2017 | Event News 03.04.2017 | Event News 27.04.2017 | Life Sciences 27.04.2017 | Physics and Astronomy 27.04.2017 | Earth Sciences

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Previous section: Encryption standards and Bullrun In the foreseeable future, some standard encryption methods could become obsolete thanks to a brand-new technology. Quantum computing takes place on the atomic and sub-atomic scale and is still at the experimental stage. It aims to take advantage of some frankly mind-blowing properties of the particles that form the building blocks of matter and light and works in a completely different way from the classical electronics-based computing with which we are all familiar. A bit in a classical computer is set to either 0 or 1 at any given moment in time. A classical computer program might be written to add two whole numbers, each of which has to be between zero and fifteen. Storing a number between zero and fifteen requires four bits (two to the power of four is sixteen), so storing both numbers would require eight bits. The values of each of the eight bits at the point in time when the program carried out the addition would determine the result. Contrary to everything common sense tells us, a bit in a quantum computer, which is called a qubit, can have both values – 0 and 1 – simultaneously. A quantum program might take eight qubits as its input. Because each qubit has two values at once, eight qubits together have 256 concurrent values (two to the power of eight). The quantum program would effectively perform 256 calculations at the same time. Unfortunately, the fact that each qubit in a quantum computation has both possible values at once does not mean that the results of a huge number of mathematical calculations can all be obtained using a single quantum computation. Rather than saying that each qubit has both values, it would perhaps be more accurate to say that each qubit has either value with a given probability. As long as a computation is taking place, each qubit really is set to 0 and to 1 at the same time. However, as soon as each of the qubits that makes up the result of the computation is read, the act of observing the qubit makes it stick in one of these two values. Retrieving the result of a quantum computation yields the result of only one of the many calculations that were performed. None of the other results remains accessible. You may well ask what the use of the answer to a mathematical calculation is if there is no way of choosing the question. The crucial point is that some of the operations used in quantum computing can skew the probability with which each qubit has one or the other value. Such operations can be cleverly combined to make it likely that the result retrieved from a computation will be the answer to a specific question chosen by the programmer. It may be helpful to compare the eight-qubit quantum computer with its classical counterpart and imagine it adding in parallel each of the members of a complete range of whole numbers between zero and fifteen to each of the members of a second, identical range of numbers. However, this analogy misrepresents the way quantum computing works. In quantum computing, it is not just the storage of information that is revolutionary. The simplest building blocks that a classical computer uses when it runs a program are based on interactions between flows of electric current. A quantum computer, on the other hand, makes individual physical particles interact with one another in ways that are themselves unlike anything in our everyday experience. While it is certainly possible to write a quantum program to add two numbers, the steps that would be used to do so are completely different from the ones somebody programming a classical computer would have at their disposal. In short, a quantum program is not just lots of classical programs operating in parallel. Because quantum computing and classical computing operate in totally dissimilar fashions, they tend to be good at different things. A quantum computer would not be an appropriate tool to solve the simple arithmetic at which classical computers excel, while the new mechanisms it offers can be exploited to achieve quick fixes for some mathematical problems that classical computers can only solve using brute-force methods. In many cases, these are the very mathematical problems on which today’s encryption standards are based. It turns out that quantum computing would make cracking contemporary symmetric encryption methods easier, but that the advantage could be counterbalanced by doubling the number of bits used in each key to increase the number of possible values. For the asymmetric methods that use private / public keys, on the other hand, quantum computing would pose a much more serious problem. It would provide easy solutions to the mathematical problems on which all the current asymmetric standards are based. In the right circumstances, a quantum computer could allow its owner to find out other people’s private keys. The private / public encryption system would no longer serve its purpose. Although practical research has certainly confirmed the theory behind quantum computing, none of the experimental quantum computers built so far have been able to use more than a very small number of qubits, nor have they worked well enough to be able to solve any mathematical problems more rapidly than the fastest classical computers. Nonetheless, it is probably only a matter of time until the remaining engineering problems are satisfactorily solved and the technology becomes mature enough for practical use. New methods of encryption and decryption will probably emerge that can only be carried out using quantum technology. For the time being, however, the race is on to develop and standardise quantum-resistant asymmetric encryption techniques. These will be performed on classical computers just like the methods that are in use today. At the same time, they will rely on mathematical problems that a quantum computer would not be able to solve in a trivial fashion, which will provide assurance that the encodings they provide will not be open to analysis by quantum computers at some point in the future. |Tweet about quantum encryption| Next section: Government restrictions on encryption

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But if we continue to follow the trend that has been in place since computers were introduced, by 2040 we will not have the capability to power all of the machines around the globe, according to a recent report by the Semiconductor Industry Association. To prevent this, the industry is focused on finding ways to make computing more energy efficient, but classical computers are limited by the minimum amount of energy it takes them to perform one operation. This energy limit is named after IBM Research Lab's Rolf Landauer, who in 1961 found that in any computer, each single bit operation must use an absolute minimum amount of energy. Landauer's formula calculated the lowest limit of energy required for a computer operation, and in March this year researchers demonstrated it could be possible to make a chip that operates with this lowest energy. It was called a "breakthrough for energy-efficient computing" and could cut the amount of energy used in computers by a factor of one million. However, it will take a long time before we see the technology used in our laptops; and even when it is, the energy will still be above the Landauer limit. This is why, in the long term, people are turning to radically different ways of computing, such as quantum computing, to find ways to cut energy use. What is quantum computing? Quantum computing takes advantage of the strange ability of subatomic particles to exist in more than one state at any time. Due to the way the tiniest of particles behave, operations can be done much more quickly and use less energy than classical computers. In classical computing, a bit is a single piece of information that can exist in two states – 1 or 0. Quantum computing uses quantum bits, or 'qubits' instead. These are quantum systems with two states. However, unlike a usual bit, they can store much more information than just 1 or 0, because they can exist in any superposition of these values. "Traditionally qubits are treated as separated physical objects with two possible distinguishable states, 0 and 1," Alexey Fedorov, physicist at the Moscow Institute of Physics and Technology told WIRED. "The difference between classical bits and qubits is that we can also prepare qubits in a quantum superposition of 0 and 1 and create nontrivial correlated states of a number of qubits, so-called 'entangled states'." A qubit can be thought of like an imaginary sphere. Whereas a classical bit can be in two states - at either of the two poles of the sphere - a qubit can be any point on the sphere. This means a computer using these bits can store a huge amount more information using less energy than a classical computer. Advances in quantum computing Last year, a team of Google and Nasa scientists found a D-wave quantum computer was 100 million times faster than a conventional computer. But moving quantum computing to an industrial scale is difficult. IBM recently announced its Q division is developing quantum computers that can be sold commercially within the coming years. Commercial quantum computer systems "with ~50 qubits" will be created "in the next few years," IBM claims. While researchers at Google, in Nature comment piece, say companies could start to make returns on elements of quantum computer technology within the next five years. Computations occur when qubits interact with each other, therefore for a computer to function it needs to have many qubits. The main reason why quantum computers are so hard to manufacture is that scientists still have not found a simple way to control complex systems of qubits. Now, scientists from Moscow Institute of Physics and Technology and Russian Quantum Centre are looking into an alternative way of quantum computing. Not content with single qubits, the researchers decided to tackle the problem of quantum computing another way. "In our approach, we observed that physical nature allows us to employ quantum objects with several distinguishable states for quantum computation," Fedorov, one of the authors of the study, told WIRED. The team created qubits with various different energy "levels", that they have named qudits. The "d" stands for the number of different energy levels the qudit can take. The term "level" comes from the fact that typically each logic state of a qubit corresponds to the state with a certain value of energy - and these values of possible energies are called levels. "In some sense, we can say that one qudit, quantum object with d possible states, may consist of several 'virtual' qubits, and operating qudit corresponds to manipulation with the 'virtual' qubits including their interaction," continued Federov. "From the viewpoint of abstract quantum information theory everything remains the same but in concrete physical implementation many-level system represent potentially useful resource." Quantum computers are already in use, in the sense that logic gates have been made using two qubits, but getting quantum computers to work on an industrial scale is the problem. "The progress in that field is rather rapid but no one can promise when we come to wide use of quantum computation," Fedorov told WIRED. Elsewhere, in a step towards quantum computing, researchers have guided electrons through semiconductors using incredibly short pulses of light. Inside the weird world of quantum computers These extremely short, configurable pulses of light could lead to computers that operate 100,000 times faster than they do today. Researchers, including engineers at the University of Michigan, can now control peaks within laser pulses of just a few femtoseconds (one quadrillionth of a second) long. The result is a step towards "lightwave electronics" which could eventually lead to a breakthrough in quantum computing. Quantum computing and space A bizarre discovery recently revealed that cold helium atoms in lab conditions on Earth abide by the same law of entropy that governs the behaviour of black holes. What are black holes? WIRED explains The law, first developed by Professor Stephen Hawking and Jacob Bekenstein in the 1970s, describes how the entropy, or the amount of disorder, increases in a black hole when matter falls into it. It now seems this behaviour appears at both the huge scales of outer space and at the tiny scale of atoms, specifically those that make up superfluid helium. "It's called an entanglement area law,” explained Adrian Del Maestro, physicist at the University of Vermont. "It points to a deeper understanding of reality” and could be a significant step toward a long-sought quantum theory of gravity and new advances in quantum computing.

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Single field shapes quantum Technology Research News computers, which tap the properties of particles like atoms, photons and electrons to carry out computations, could potentially use a variety of schemes: individual photons controlled by optical networks, clouds of atoms linked by laser beams, and electrons trapped in quantum dots embedded in Due to the strange nature of quantum particles, quantum computers are theoretically much faster than ordinary computers at solving certain large problems, like cracking secret codes. Chip-based quantum computers would have a distinct advantage: the potential to leverage the extensive experience and manufacturing infrastructure of the semiconductor industry. Controlling individual electrons, however, is extremely challenging. Researchers have recently realized that it may be possible to control the electrons in a quantum computer using a single magnetic field rather than having to produce extremely small, precisely focused magnetic fields for each electron. Researchers from the University of Toronto and the University of Wisconsin at Madison have advanced this idea with a scheme that allows individual electrons to serve as the quantum bits that store and process computer information. The scheme is an improvement over existing global magnetic field schemes, which require each qubit to consist of two or more electrons. Electrons have two magnetic orientations, spin up and spin down, which can represent the 1s and 0s of computing. The logic of quantum computing is based on one-qubit gates and two-qubit gates. One-qubit gates flip individual spins, changing a 1 to a 0 and vice versa. Two-qubit gates cause two spins to become linked, or entangled. The researchers' scheme relies on the interactions of pairs of electrons to create both types of gates. Tiny electrodes positioned near quantum dots -- bits of semiconductor material that can trap single electrons -- can draw neighboring electrons near enough that they exchange energy. If the electrons interact long enough, they swap spin orientations. The challenge is finding a way to use the interaction to flip the spin of one electron without flipping the spin of the other. The scheme does so by taking a pair of electrons through eleven incremental steps using the electron interaction and the global magnetic field. "We first turn on the exchange interactions... through small electrodes to generate a swap gate, then turn on the global magnetic field," said Lian-Ao Wu, a research associate at the University of Toronto. The eleven steps -- four electron interactions and seven pulses of the magnetic field -- alter the spins. Because the magnetic field diminishes in strength over distance each electron is exposed to a different strength. By tuning the field, the researchers can make the process cancel out the changes to one spin while flipping the other, according to Wu. The researchers' scheme could be implemented using a pair of square, 100-nanometer-diameter aluminum nanowires separated by a thin insulating layer. A row of quantum dots in a zigzag pattern would be positioned parallel to the wires, with half of the dots 200 nanometers from the wires and the other half 300 nanometers away. A nanometer is one millionth of a millimeter, or the span of 10 hydrogen atoms. The ability to build such a quantum computer depends on developments in nanotechnology, said Wu. "It is still hard to design a complete control scheme of the exchange interactions," he said. "Once such obstacles are overcome, our scheme should offer significant simplifications and flexibility." The on-chip conducting wires called for in the researchers' scheme have been used in physics experiments involving controlling beams of atoms and Bose-Einstein condensates, which are small clusters of atoms induced to behave as one quantum entity, according to Wu. The researchers are working on reducing the number of steps required for their quantum logic circuit, combining their scheme with quantum error correction techniques, and reducing the engineering challenge of implementing the design, said Wu. The scheme would require making the aluminum wires with a precision of a single layer of atoms, but optimizing the scheme should make it possible to loosen the requirements to several atomic layers, which is technologically feasible, according to Wu. "The main challenge is [achieving a] high degree of control of the exchange interactions," he said. The technique could be used practically in 10 to 20 years, said Wu's research colleague was Daniel A. Lidar at the University of Toronto and Mark Friesen at the University of Wisconsin at Madison. The work appeared in the July 15, 2004 issue of Physical Review Letters. The research was funded by the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), and the Army Research Office/Advanced Research and Development Activity (ARO/ARDA). Timeline: 10-20 years TRN Categories: Quantum Computing and Communications Story Type: News Related Elements: Technical paper, "One-Spin Quantum Logic Gates from Exchange Interactions and a Global Magnetic Field," Physical Review Letters, July 15, 2004 November 3/10, 2004 DNA machines take a walk DNA in nanotubes Single field shapes lengthen to centimeters Lasers move droplets promise reliable MRAM Research News Roundup Research Watch blog View from the High Ground Q&A How It Works News | Blog Buy an ad link

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Taking a Practical Step Forward in Optical Computing Using Slow Light: Photonic Crystals Offer a Slow Light Solution for Optical Computing Previously published on Apr 13, 2011 Quantum computing is the Mount Everest of the information technology revolution. What approach succeeds will almost assuredly utilize optical components. With the limits of traditional electronics threatening to halt progress, alternatives, such as optical computing, will be needed in the not so distant future. One major hurdle for the development of such optical systems has been the need to convert between optical and electronic signals. Because time spent converting optical data into an electronic format takes longer than simply using the traditional medium, the concept is impractical in many respects. On the other hand, an almost paradoxical concept known as slow light offers a way around this barrier with a very practical solution. It is a fundamental law of the universe that light can only exist at the speed of light. That is, photons must always move at approximately 300 million meters per second. Looking closely at this law reveals a rather obvious loophole. Light waves passing through almost any given medium usually take longer to propagate through said medium than they would free space, because the light is bent along a lengthier path due to the internal properties of the medium. In other words, photons will continue to move at light speed, but it takes them longer to navigate through an object rather than simply moving within a vacuum at light speed, i.e. light goes slower. Consequently, given the proper medium, light could be slowed to a crawl, or even stopped. It is how much a medium bends light that determines the "speed" of light and this property classically depends upon a material's index of refraction. A material with a high enough index of refraction, therefore, could be used to slow light. While the first demonstration of slow light in 1999, which yielded a speed around 17 meters per second, utilized a Bose-Einstein Condensate, which is a low-temperature state of matter where the atoms lose their individual characteristics and act almost as a single particle, one alternative approach is to utilize the many emerging manmade meta-materials that exhibit extreme properties, including super high indexes of refraction. On the other hand, researchers at the University of Sydney in New South Wales looked at advances in photonic crystals to suggest an even easier, more dynamic alternative. Photonic crystals are a rapidly advancing technology first developed in the 1990's. By engineering regular structures in an optical material, light will respond to the pattern as though it is passing through a crystal. Giving researchers far greater control over light, photonic crystals can be used to slow light to variable speeds at continually shrinking costs with greater precision and less bulk. In fact, Professor Benjamin Eggleton's research group has already demonstrated an approach using a photonic crystal structure engineered by a University of St. Andrews team led by Professor Thomas F. Krauss for use over a broad bandwidth yields a sixteen fold increase in processing speeds over a traditional silicon chip, or 640 gigabits a second. As such, it is obvious the next step forward is hybrid systems using photonic crystal chips. The key to processing and transmitting data stems from the ability to control how information flows. Light can get information to where it needs to go rather quickly, but the information must be stored until it can be used. Optical buffering as the "old fashion" approach relies on costly conversions between optical and electronic signals, so slowing light is a better option. If light is slowed or stopped until it is needed, a hybrid optical-electronic system would be extremely practical with results instantly surpassing the capacity of electronic devices. Consequently, we may soon see a major advancement in the telecommunications industry, followed by a renewed revolution in all computing technologies. Thanks to initiatives for promoting civil investments in solar energy, LED lighting, national security and so on, technologies based on research from the fields of optics have known great progress in recent years. Just as the fruits of this research finally start to ripen, however, public support is drying up due to budget battles in Europe and the United States. Meanwhile, private funding can often be very selective to our civilization's detriment as entrepreneurs only want to invest in products that guarantee them a return, especially in the current environment where high return, low cost business deals can be exploited by the investment community. The US was already significantly behind in providing funds for research while even less funding is certain to retard progress just as we are the verge of major advances on a number of fronts. With relatively low-cost experimental needs, the optical sciences offer solutions for everything from national and energy security to pharmaceutical and agricultural applications. Breakthroughs like slow light, meta-materials, photonic crystals, and quantum dots, which are essentially "traps" for photons and other particles, came about due to somewhat basic theories of some very complex subjects and scientists simply questioning. Not only do these discoveries and more have a myriad of potential applications, the costs associated with these technologies fall as we see progress while the benefits and profits begin to amass. Pursuing related research has already revealed some very meaningful discoveries and opportunities, but our society must be more aggressive in our pursuit of the basic research required to realize current and future gains.

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Graphene is a honeycomb-like lattice made of a one-atom-thick layer of carbon atoms. It is the thinnest, lightest, and strongest known material and offers extremely high electrical and thermal conductivity. Recently, researchers are trying to add superconductivity to its unique set of properties. How Does Superconductivity Happen? A superconductor achieves zero electrical resistance below a certain temperature which may be as low as -269 degrees Celsius. Such superconductors, called low-temperature superconductors [PDF], were discovered nearly 100 years ago. On the other hand, high-temperature superconductors, which has a transition temperature of about -135 degree Celsius, were not discovered until about 30 years ago. A low-temperature superconductor using liquid nitrogen. Photo courtesy of Camilla Hoel [CC BY-SA 2.0] In a metal, electrons move on their own, repelling and colliding with each other. However, in a superconductor, they travel in pairs and move more smoothly. Suchitra Sebastian, an applied physicist at the University of Cambridge, envisions that in a superconductor electrons travel in lanes. Today, scientists have a deeper understanding of low-temperature superconductors. They know that the crystal structure of these materials forces the electrons to travel in pairs. Applications of Superconductors Magnetic levitation is one of the most well-known applications of superconducting where the strong superconducting magnets are used to make a vehicle, such as a train, float in the air and travel at extremely high speeds. In April 2015, the MLX01 test vehicle achieved an incredible speed of 603 kph. The medical applications of superconductors are magnetic resonance imaging (MRI) and the SQUIDs. The latter can be used to examine certain depths of the body without applying a strong magnetic field like that of MRI. Another interesting application of this technology is superconductor-based electric generators which are estimated to have a worldwide market of $20-30 billion in the next decade. Petaflop computers, ultra-high-performance filters, very low-frequency antennas, and E-bombs are just a few of other applications of this technology which would be impossible otherwise. Superconductivity was observed in graphite years ago and, even before experimental verifications, scientists believed that incorporating the right additives must lead to superconductivity in graphene. Superconductivity of Lithium-Coated Graphene Less than two years ago, researchers incorporated lithium atoms to make the world’s first graphene superconductor. The international research team created graphene sheets and coated them with lithium atoms. Andrea Damascelli, director of the University of British Columbia's Quantum Matter Institute in Vancouver who was involved in this research, noted that the way samples are prepared is a key factor. Before this, several other groups had been trying to create superconducting lithium-coated graphene; however, they always faced sources of instability which made success elusive. Damascelli and his colleagues experimented in ultra-high-vacuum conditions at about minus 268 degrees Celsius. Calcium Atoms Sandwiched with Graphene Sheets Nearly one year ago, researchers from Tohoku University and the University of Tokyo placed calcium atoms between sheets of graphene which were grown on a silicon carbide crystal. They achieved superconductivity at -269 degrees Celsius. A representation of the developed material. Image courtesy of Tohoku University. Obviously, these super cold temperatures are not suitable for applications such as superconductor-based power lines. However, according to Tohoku University, these studies pave the way for ultra high-speed superconducting nanodevices which can be used in quantum computing. PCCO Unleashes the Superconductivity of Graphene While the above experiments relied on doping graphene to achieve a superconductor, researchers from the University of Cambridge have recently developed a graphene-based superconductor without altering the material. Jason Robinson, involved in the project, notes that, apparently, the study has achieved a rare type of superconductivity called p-wave state. However, he adds that further experiments are required to confirm this. According to Angelo di Bernardo, methods which place graphene on other materials change its properties. On the other hand, although they achieve superconductivity, it is not necessarily from graphene but simply that of the underlying superconductor being passed on. The Cambridge team incorporates a material called praseodymium cerium copper oxide (PCCO) to awaken graphene’s dormant superconductivity. While the experiment may look like previous ones where a second material was required to achieve superconductivity, the new method is quite different from previous techniques. In this recent experiment, the achieved superconductivity is clearly distinguished from that of the added material, i.e. PCCO. In a PCCO, electron pairs are in a state called d-wave; however, the spin state of electron pairs in the new superconductor was observed to be p-wave which is a rare and still unverified type of superconductivity first proposed by Japanese researchers in 1994. According to Robinson, the superconductivity was not from PCCO and PCCO was simply required to unleash the intrinsic superconductivity of graphene. The experiment is a big deal because it can prove that the elusive p-wave superconductivity really exists and, consequently, give the researchers the chance to properly investigate this type of superconductivity. With a better understanding of p-wave superconductivity, researchers may find a whole new spectrum of superconductors. Uses for Superconducting Graphene Superconducting graphene may not be a good choice to develop more efficient power lines, but researchers believe that it suits applications such as SQUIDs (superconducting quantum interference devices). SQUIDs, which are capable of sensing a change in a magnetic field over a billion times weaker than the force that moves the needle on a compass, can scan brain activities with a great precision. Damascelli believes that graphene-based superconductors could lead to a 100-fold increase in the sensitivities which are currently achievable. Unfortunately, there are many mysterious unknowns about how superconductivity is achieved in general, especially in graphene-based materials. However, all these endeavors seem to be quite rewarding and many research groups are intrigued to discover this territory. The details of this research are published in Nature Communications.

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A series of reports from the annual meeting of the American Association for the Advancement of Science kicks off with new developments in quantum computing Feb 25th 2012 | vancouver | from the print edition QUANTUM effects are vital to modern electronics. They can also be a damnable nuisance. Make a transistor too small, for example, and electrons within it can simply vanish from one place and reappear in another because their location is quantumly indeterminate. Currents thus leak away, and signals are degraded. Other people, though, see opportunity instead. Some of the weird things that go on at the quantum scale afford the possibility of doing computing in a new and faster way, and of sending messages that—in theory at least—cannot be intercepted. Several groups of such enthusiasts hope to build quantum computers capable of solving some of the problems which stump today’s machines, such as finding prime factors of numbers with hundreds of digits or trawling through large databases. They gave a progress report to the annual meeting of the American Association for the Advancement of Science (AAAS) in Vancouver. At the core of their efforts lie the quantum-mechanical phenomena of superposition and entanglement. An ordinary digital computer manipulates information in the form of bits, which take the value of either 0 or 1. These are represented within the computer as different voltages of electric current, itself the result of the electron’s charge. This charge is a fixed feature of all electrons; each has the same amount of it as any other. But electrons possess other, less rigid properties like spin, which can be either “up”, “down” or a fuzzy, imprecisely defined combination of the two. Such combinations, known as superpositions, can be used to construct a quantum analogue of the traditional bit—the qubit. Entanglement, meanwhile, is the roping together of particles in order to add more qubits. Each extra qubit in a quantum machine doubles the number of simultaneous operations it can perform. It is this which gives quantum computing its power. Two entangled qubits permit four operations; three permit eight; and so on. A 300-qubit computer could perform more concurrent operations than there are atoms in the visible universe. A coherent idea Unfortunately, such a machine is not in the offing. Entanglement and superposition are delicate things. Even the slightest disturbance causes qubits to “decohere”, shedding their magical properties. To build a working quantum computer, qubits will have to become more resilient, and progress so far has been slow. The first quantum computations were done in the lab in 1995. Since then various teams have managed to entangle as many as 14 qubits. The record holders, a group in Innsbruck, use a device called an ion trap in which each qubit exists as a superposition of a rubidium atom at different energies. Raymond Laflamme and his colleagues at the University of Waterloo, in Canada, have managed to entangle 12 qubits by performing a similar trick, entangling certain atoms within a single molecule of an amino acid called histidine, the properties of which make it particularly suited to such experiments. The problem with these approaches is that they will not be easy to scale up. Ion traps reside inside big vacuum chambers, which cannot easily be shrunk. And a molecule of histidine contains only so many suitable atoms. So the search is on for more practical qubits. One promising approach is to etch qubits in semiconductors. Charles Marcus, previously of Harvard University and now at the University of Copenhagen, has been using electrons’ spins to do this. Single-electron qubits decohere quickly, so his team decided instead to create a qubit out of two electrons, which they trapped in “quantum dots”, tiny semiconducting crystals (of gallium arsenide, in this case). When two such dots are close together, it is possible to get an electron trapped in one to pop over and join its neighbour in the other. The superposition of the two electrons’ spins produces the qubit. Dr Marcus’s team have so far managed to stitch four such qubits together. An array of clever tricks has extended their life to about ten microseconds—enough to perform the simple algebraic operations that are the lifeblood of computing. They hope to extend their life further by using silicon or carbon, the atomic nuclei of which interfere less with the entangled electrons than do those of gallium arsenide. John Martinis and his colleagues at the University of California, Santa Barbara (UCSB), meanwhile, have been trying to forge qubits from superconducting circuits. In a superconductor, electrons do not travel solo. Instead, for complicated quantum-mechanical reasons, they pair up (for the same reasons, the pairs feel no electrical resistance). When they do so, the pairs start behaving like a single particle, superposing proclivities and all. This superparticle can, for instance, in effect be moving in two directions at once. As electrons move, they create a magnetic field. Make a closed loop of superconducting wire, then, and you get a magnetic field which can be facing up and down at the same time. You have yourself a superconducting qubit—or five, the number Dr Martinis has so far managed to entangle. He has another clever trick up his sleeve. Using a device called a resonator he has been able to transfer information from the circuit to a single photon and trap it in a cavity for a few microseconds. He has, in other words, created a quantum memory. A few microseconds may not sound much, but it is just about enough to perform some basic operations. The problem with all these approaches is that the quantum states they rely on are fragile, which allows errors to creep in. One way to ensure that they do not scupper the calculation is to encode the same information in several qubits instead of just one. Drs Marcus, Martinis and Laflamme have therefore had to build redundant qubits into their systems. For every “logical” qubit needed to do a calculation, there is a handful of physical ones, all of which need to be entangled. Michael Freedman is trying to address this problem by taking a different tack. Together with his colleagues at Microsoft’s Station Q research centre, also at UCSB, he is trying to build what he calls a topological quantum computer. This uses a superconductor on top of a layer of an exotic material called indium antimony. When a voltage is applied to this sandwich, the whole lot becomes a quantum system capable of existing in superposed states. Where Dr Freedman’s qubits differ from Dr Martinis’s is in the way they react to interference. Nudge any electron in a superconducting circuit and the whole lot decoheres. Dr Freedman’s design, however, is invulnerable to such local disruptions thanks to the peculiar way in which energy is distributed throughout indium antimony. The Microsoft team has yet to create a functioning qubit, but hopes to do so soon, and is searching for other materials in which to repeat the same trick. All of this work is pretty fundamental. Researchers are a long way from creating quantum mainframes, which is how most of them see the future of their fiddly devices, let alone quantum desktops. Dr Martinis thinks that a viable quantum processor is still ten years away. Yet even this is progress of a sort. When he entered the field two decades ago, he thought that building a quantum processor was “insanely difficult”. Now he says it is merely “very, very hard”. Leave a comment(if you having troubles, try posting your comment on this page or send an email to chronicle @ itbhuglobal.org) Institute of Technology, Banaras Hindu University Varanasi 221005, UP

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It's a machine that could calculate solutions to problems so impossibly time-consuming that even the most powerful supercomputers could never handle them. And it would do so in an instant. This is the quantum computer, made possible by the bizarre nature of quantum mechanics. And though the idea is still in its infancy, it's no fantasy. Two research teams, at Harvard University and the Max Planck Institute of Quantum Optics in Germany, have just announced that they have independently forged the building blocks for tomorrow's quantum computers. As they published today in the journal Nature (1, 2), the scientists discovered a way to hook up atoms and particles of light to create a new type of switch and logic-gate‚ quantum versions of the connecting structures that link bits of data in modern computers. When you dive down into the circuits, all modern computers are basically the same: a huge collection of data arranged with simple rules. Each piece of data is called a bit and shows just one fragment of information‚ a 0 or a 1. You can think of a bit as a lightbulb that's either shining or not. But quantum theory‚ the physics that rules the tiny world of atoms and particles‚ tells us that there are certain circumstances in which a piece of matter can be two things at the same time. It's possible to have an atom that's spinning in two opposite directions at once, or even to have your lightbulb both shining and not shining. Items with this wacky dual state are said to be in "superposition." (Physicist Niels Bohr once said, "Those who are not shocked when they first come across quantum theory cannot possibly have understood it." So don't worry if you're confused‚ Bohr was one of the founders of quantum theory.) The most important catch (there are plenty) is that this superposition state is fragile and possible only for incredibly tiny bits of matter. But for computers, this very idea poses an interesting prospect. If you could somehow harness this odd state of matter to put individual bits of information into superposition, then suddenly you've packed more data into the tiniest package possible. Your bits can now show a 0, a 1, or a combo of both. This is called a quantum bit, or a qubit. And if qubits were linked together like normal bits are linked in a computer, then you'd have a machine could calculate at insane speeds. "At this point, very small-scale quantum computers already exist," says Mikhail Lukin, the head of the Harvard research team. "We're able to link, roughly, up to a dozen qubits together. But a major challenge facing this community is scaling these systems up to include more and more qubits." The problem of adding more qubits, Lukin explains, is tied to the fragility of the superposition state. Unless the entire quantum computer is kept at extremely cold temperatures and free of any interfering particles or other noise, the superposition state will entirely collapse for all the qubits, ruining the computer. What makes this even harder is that today's qubits must be close to one another to be connected, and it takes a massive apparatus of machinery, lab equipment, and lasers to support the superposition state of just a single fleck of matter. That dumps an increasing amount of grit into the system, increasing the chance that the entire quantum computer will fail. "It's just very difficult to address one qubit without interfering with all the rest of them; to take a laser beam and shine it one particular qubit and not another," says Gerhard Rempe, the head of the Max Planck Institute of Quantum Optics research team. "And if, for example, you want to use 10,000 qubits, well, that's 10,000 lasers you have to worry about." The Ol' Gate and Switch The new quantum logic gate and switch unveiled today promise to ameliorate some of these problems. Both use a new method: They harness trapped atoms (in both cases, rubidium) that can transfer information through photons, the particles that make up light. Photons, which can be directed through fiber-optic cable, are the prime candidate for sending information at great distances and keeping qubits apart. Here is how it works: The scientists trap a heavy rubidium atom between two mirror-like sheets using a laser technique that keeps the atom relatively immobile. The scientists then send a photon straight at this atom sandwich. Normally, the photon would hit the first mirror and bounce right back where it came from. But if the atom is put in a specific energetic state, the photon will go straight through that first mirror, hang out with the atom for a moment, and then exit where it came from. As a going-away present, the photon also has a slight change in polarization. This is pretty much how any switch in a computer works. If something is "on," then one thing happens. If it's "off," then another thing happens. But here's the tricky part. The scientists can put the rubidium atom in superposition, so that it is simultaneously in that energetic state and not in the energetic state. It's on and off. Because of this, the photon both does and does not enter the mirror, mingle, and gain its polarization change. And the photon, by virtue of having both changed and not changed, carries that superposition information and can bring it to a different atom-based qubit. A similar process happens with the quantum logic gate. A normal logic gate is just a series of switches set up in a way that together, they perform a logical operation when given multiple inputs. The German team created a quantum version by having multiple photons repeatedly bounce off the mirror-trapped and superpositioned rubidium atom. Then, using another funky attribute of quantum physics called entanglement swapping, the scientists made it so that the photons share the same information. These entangled photons can become the multiple inputs required for any logic gate. Even with this new advancement, we're still a long way from building large-scale quantum computers, with thousands of qubits linked together. "We're not going to see quantum computers being built for the average American consumer in ten years, or anything like that," says Jeff Thompson, a physicist with the Harvard research team. Rempe says that while this technology seems promising for solving the qubit-closeness issue, neither team is actually attempting to link multiple qubits. And that endeavor will probably open up a new world of unknowns. Nonetheless, "It's exciting to see this [photon-based] technology is coming into its own," says Jacob Taylor, a physicist at the University of Maryland who was not involved with the projects. Whatever future difficulties arise, he says, scientists are learning valuable information about one of the most fundamental aspects of physics. Everything we know about quantum mechanics would lead us to believe that large-scale quantum computers should be theoretically possible. But even if "you couldn't build a large-scale quantum computer," he says, "that's somewhat exciting, too. That tells us that our theory of quantum mechanics might be breaking down somewhere, that we still have much to learn."

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Some people want to move mountains. Kunal Das, Ph.D., assistant professor of physics, wants to move electrons. Das is a theoretical physicist researching an area where the classical rules of physics no longer apply—the nanoscale universe of quantum physics, a submicroscopic world where particles defy common sense. In that mysterious world of the ultra-small, Das is searching for new ways to move the currents that power computers. “When the first computers came along in the 1960s, they were huge objects which filled up an entire room and had miniscule computing power,” Das says, as he gestures to his computer in his Freeman Hall office. “How is it that today we have something this compact and with this much more power? Today, every two years computers become twice as fast and half as big.” Computers are powered by electronic circuitry in which currents move large clusters of electrons at a time to feed a tiny computer chip. The number of electrons needed for each operation has gotten smaller with time. But within 20 years, Das says, computers will reach a point where each operation could be done by just one electron, and thus won’t be able to get any faster or any smaller. What then? Where will technology go? Already, scientists are experimenting with storing information not in bits, but in qubits (or quantum bits), which can potentially store much larger amount of information than traditional bits. Can a “quantumchip” be in the offing? That’s where quantum mechanics come in. Das has focused his research on adiabatic electron pumps, which can be used to control the flow of individual or entangled pairs of electrons in order to power quantum computers. Quantum computers, which are still in their infancy, have the potential to perform certain calculations significantly faster than any silicon-based computer. Quantum mechanics have become very important partly because, at the qubit level, individual particles of matter play essential roles. The current that powers the computer no longer flows as a cluster of electrons, but as one electron at a time; and such motion is governed by quantum mechanics. “In classical physics, we talk about currents flowing continuously, like water,” Das says. “At the nanoscale, your current is comprised of individual electrons, and it is discrete as opposed to continuous.” In other words, if you were to look at water flowing through a pipe, you would discover that at the submicroscopic level it is made of molecules that are discrete from one another, like individual grains of sand. The problem is that the super-small world of quantum mechanics is notoriously unpredictable. In fact, an electron at the quantum level has a few weird characteristics that stem from the fact that quantum mechanics is all about probabilities, not absolutes. “An electron, from a quantum mechanical perspective, does not behave like it does in classic physics, where it always acts like a particle,” Das says. “Here, it acts like a particle some of the time and like a wave some of the time. It has wave-particle duality, and it becomes probabilistic, meaning you cannot say for sure that the electron is definitely here. It might have some probability of it being here, or some probability of it being there. That’s what makes quantum mechanics strange and confusing to the layperson.” An adiabatic electron pumping system is complex, but Das describes it as a mechanism that manipulates the shape of the “quantum wavefunction” of an electron, by varying such things as voltage or a magnetic field at the nanoscale. Das is researching how to apply the pumping system to single electrons and also to pairs of “entangled” electrons in which one electron can affect another even when separated by vast distances. He hopes that his research will ultimately lead to a dependable system of moving currents of electrons in a precisely controlled way without destroying their fragile quantum state, which is essential to powering quantum computers. “Once we start using the wave nature of electrons and the probabilistic nature of quantum mechanics, we can potentially do certain computations tremendously faster,” he says. At this point, quantum computers have not yet been built, although some experiments have been carried out. Research is being done at a frantic pace, however, as such systems would be invaluable to national security, Das says. “All existing encryption systems are based upon the fact that we cannot crack them with the computers that we have available now,” says Das. “With a quantum mechanical algorithm, you could crack encryption methods very fast.” There are also potential applications to teleportation, Das says, but not of the Star Trek variety—at least not yet. What you could teleport is the state of an electron,” he says. “We could transfer those properties to a location which is far away, but not the physical object itself. So, in a sense, in quantum mechanics, you can be in two places at the same time.”

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Quantum computers should be much easier to build than previously thought, because they can still work with a large number of faulty or even missing components, according to a study published today in Physical Review Letters. This surprising discovery brings scientists one step closer to designing and building real-life quantum computing systems - devices that could have enormous potential across a wide range of fields, from drug design, electronics, and even code-breaking. Scientists have long been fascinated with building computers that work at a quantum level - so small that the parts are made of just single atoms or electrons. Instead of 'bits', the building blocks normally used to store electronic information, quantum systems use quantum bits or 'qubits', made up of an arrangement of entangled atoms. Materials behave very differently at this tiny scale compared to what we are used to in our everyday lives - quantum particles, for example, can exist in two places at the same time. "Quantum computers can exploit this weirdness to perform powerful calculations, and in theory, they could be designed to break public key encryption or simulate complex systems much faster than conventional computers," said Dr Sean Barrett, the lead author of the study, who is a Royal Society University Research Fellow in the Department of Physics at Imperial College London. The machines have been notoriously hard to build, however, and were thought to be very fragile to errors. In spite of considerable buzz in the field in the last 20 years, useful quantum computers remain elusive. Barrett and his colleague Dr. Thomas Stace, from the University of Queensland in Brisbane, Australia, have now found a way to correct for a particular sort of error, in which the qubits are lost from the computer altogether. They used a system of 'error-correcting' code, which involved looking at the context provided by the remaining qubits to decipher the missing information correctly. "Just as you can often tell what a word says when there are a few missing letters, or you can get the gist of a conversation on a badly-connected phone line, we used this idea in our design for a quantum computer," said Dr Barrett. They discovered that the computers have a much higher threshold for error than previously thought - up to a quarter of the qubits can be lost - but the computer can still be made to work. "It's surprising, because you wouldn't expect that if you lost a quarter of the beads from an abacus that it would still be useful," he added. The findings indicate that quantum computers may be much easier to build than previously thought, but as the results are still based on theoretical calculations, the next step is to actually demonstrate these ideas in the lab. Scientists will need to devise a way for scaling the computers to a sufficiently large number of qubits to be viable, says Barrett. At the moment the biggest quantum computers scientists have built are limited to just two or three qubits. "We are still some way off from knowing what the true potential of a quantum computer might be, says Barrett. "At the moment quantum computers are good at particular tasks, but we have no idea what these systems could be used for in the future," he said. "They may not necessarily be better for everything, but we just don't know. They may be better for very specific things that we find impossible now." For further information please contact: Research Media Relations Manager Imperial College London Telephone: +44 (0)207 594 8432 or ext. 48432 Out of hours duty Press Officer: +44 (0)7803 886 248 Notes to editors: 1. All are welcome to attend the lecture by Professor Alain Aspect of CNRS at Imperial College London from 17.30 - 18.30 on Thursday 11 November, "From Einstein's intuition to quantum bits: a new quantum age?" The lecture will be held in the Great Hall in the Sherfield Building on Imperial College London's South Kensington campus. Please email email@example.com for further information or to register to attend. 2 "Fault tolerant quantum computation with very high threshold for loss errors" Physical Review Letters 09 November 2010, to be published online at: 1500 London time (GMT) / 1000 US Eastern time Tuesday 9th November (no embargo) Link to paper on pre-print server: http://arxiv. Corresponding author: Sean Barrett, Institute for Mathematical Sciences, Imperial College London. 3. Contact for Australian media: Dr Thomas Stace, Co-author (University of Queensland, Brisbane, Australia) Tel: +61 40 441 3069 4. Images are available for the media at: Credit: Sean Barrett and Thomas Stace. Caption: Illustration of the error correcting code used to demonstrate robustness to loss errors. Each dot represents a single qubit. The qubits are arranged on a lattice in such a way that the encoded information is robust to losing up to 25 percent of the qubits 5. The Royal Society is an independent academy promoting the natural and applied sciences. Founded in 1660, the Society has three roles, as the UK academy of science, as a learned Society, and as a funding agency. It responds to individual demand with selection by merit, not by field. As we celebrate our 350th anniversary in 2010, we are working to achieve five strategic priorities, to: - Invest in future scientific leaders and in innovation - Influence policymaking with the best scientific advice - Invigorate science and mathematics education - Increase access to the best science internationally - Inspire an interest in the joy, wonder and excitement of scientific discovery 6. About Imperial College London: Consistently rated amongst the world's best universities, Imperial College London is a science-based institution with a reputation for excellence in teaching and research that attracts 14,000 students and 6,000 staff of the highest international quality. Innovative research at the College explores the interface between science, medicine, engineering and business, delivering practical solutions that improve quality of life and the environment - underpinned by a dynamic enterprise culture. Since its foundation in 1907, Imperial's contributions to society have included the discovery of penicillin, the development of holography and the foundations of fibre optics. This commitment to the application of research for the benefit of all continues today, with current focuses including interdisciplinary collaborations to improve global health, tackle climate change, develop sustainable sources of energy and address security challenges. In 2007, Imperial College London and Imperial College Healthcare NHS Trust formed the UK's first Academic Health Science Centre. This unique partnership aims to improve the quality of life of patients and populations by taking new discoveries and translating them into new therapies as quickly as possible. Website: www.imperial.ac.uk

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Quantum Computation with Trapped Ions Today computers are indispensable even in our daily life. Each year engineers create more powerful computers simply by making them smaller. Can this continue for ever? With the current rate of miniaturization in about 20 years single atoms will be used for storage and manipulation of information. For such small objects, however, our usual intuition fails, since they do not follow classical, but quantum mechanical rules. Is it still possible to build a computer based on these strange, new quantum laws? Already in 1982, Richard Feynman pronounced the idea that certain calculations could be performed much more efficiently with quantum mechanical than with classical computers. In 1994, the first computational problem was proved to be solvable substantially faster with a "quantum algorithm" (the Shor algorithm) compared to a classical one. Nevertheless, the physics and mathematics behind this is little known to most people, and experimental exploration of this fascinating subject has just started. Our approach is based on well controlled laser beams and a series of calcium ions, confined to a space of less than a hair wide. Linear ion trap: By applying voltages to the trap electrodes, a string of ions can be held in the trap for several days. The lower picture shows a string of 6 Calcium 40 ions taken with a ordinary CCD camera. Our group has demonstrated the basic principles of such a quantum computer. Currently, we are working with up to eight ionized Calcium atoms suspended in free space by electromagnetic forces. Each atom represents one quantum bit (qubit). In contrast to classical bits, a qubit can take any value between 0 and 1, so that it contains partially both values at the same time. Due to this property it is possible to calculate an algorithm for both values in parallel. Thus loosely speaking, quantum computers can solve different tasks simultaneously. For certain tasks - like simulation of complicated quantum processes - even a 40-bit quantum computer would be much more powerful than any existing classical computer. Sketch of the experimental setup. The quantum state of the trapped ions is manipulated by laser pulses and finally detected by measuring the ion's fluorescence on a CCD camera. Absence and presence of fluorescence signal the qubit's "0" and "1" states, respectively. In our prototype quantum computer, we use lasers to manipulate quantum information encoded in the atoms. The atomic states evolve according to the chosen strength and frequency of the laser pulse. Also, lasers serve to read out the qubits: depending on their state, the atoms either emit light or remain dark which can be readily detected with a CCD-camera. One of the biggest challenges is to control the interaction between these tiny quantum bits. Similarly to classical computing, for quantum computers there exists a small set of (quantum) gates with which every quantum algorithm can be realized. Using two trapped ions, we have demonstrated an important quantum gate, the controlled-NOT operation (F. Schmidt-Kaler et al.) which - together with single qubit gates - constitutes such a set of gates. We have realized the quantum mechanical equivalent to the Toffoli gate - a controlled-controlled-NOT gate (T. Monz et al.). This gate could become an essential element for implementing quantum error correction (QEC). Exploring quantum physics Quantum computing techniques are also very useful tools for exploring the strange rules of quantum physics. We have created entangled states of up to eight ions (H. Häffner et al.). Here, the state of a single particle is completely undetermined even though the state of the whole system is well-defined. These states are used to investigate fundamental properties of quantum physics like, for example, the collapse of the wave function induced by measurements. Also, we can demonstrate the non-local nature of quantum theory, i.e. the fact that the quantum state of an object can be inextricably linked to the quantum state of another (distant) object. This property plays a key role in quantum state teleportation. A closer look at the quantum computing setup showing a box of mu-metal for magnetic shielding, inside the vacuum vessel housing the ion trap and laser beam steering optics around. Quantum teleportation with ions Quantum state teleportation is a scheme that solves the task of transferring an unknown quantum state from one location to another. First achieved with entangled photons, it is also applicable to atomic quantum states. In our implementation (M. Riebe et al.) based on three ions, we show that the quantum information encoded in one ion is deterministically transferred to another ion at any time. Although the teleportation distance is currently limited to 10 micrometers, the development of segmented ion traps with complex electrode structures will overcome this limitation and increase the distance over which quantum information can be communicated. Schematic of the teleportation of a quantum state. Entanglement swapping with ions A similar protocol as for quantum teleportation can be used to entangle two ions that have never interacted before. Such deterministic entanglement swapping (M. Riebe et al.) was recently show by our group (C. F. Roos et al. and H. Häffner et al.). Entanglement swapping is of particular significance for the next generation of quantum computers where it could be used to entangle and link qubits in distant regions of the quantum processor. Quantum computation with logical qubits A quantum computer can encode logical information in superpositions of quantum states. The information is contained in the relative probabilities of the two states of the qubits, but also their respective phase. Environmental effects like magnetic field fluctuations or laser instabilities can result in dephasing, and therefore loss, of quantum information. However, special states - the so called decoherence free subspace (DFS) - are insensitive to dephasing. We have shown encoding of qubits within that subspace (M. Chwalla et al.), storing information in a way that is only limited by the lifetime of the qubit states. Currently, we are working on techniques to use such robust encoding for calculating arbitrary algorithms View into the vacuum chamber with the ion trap inside. (Lintrap Group picture) - Roman Stricker (Master's student) - Alexander Erhard (PhD student) - Esteban Martinez (PhD student) - Daniel Nigg (PhD student) - Thomas Monz (Postdoc) - Philipp Schindler (project leader) - Rainer Blatt (group leader) Former members: Julio Barreiro, Michael Chwalla, Stefan Quint

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Study of electron movement on helium may impact the future of quantum computing Images of the electron trap architecture. Top: Schematic representation of the experiment. Current of surface electrons, induced by ac voltage applied to the electrode underneath Reservoir 1, flows between Reservoirs 1 and 4, as shown by the red arrow. Middle: Cross section of the central microchannel around the gate area. Bottom: Photograph of the microchannel device on a copper sample cell, with subsequent close-up photographs of the central channel and surrounding reservoirs.Credit: Denis Konstantinov The future of quantum computing is a hot topic not only for experts but also in many commercial and governmental agencies. Rather than processing and storing information as bits in transistors or memories, which limit information to the binary ‘1’ or ‘0’, quantum computers would instead use quantum systems, such as atoms, ions, or electrons, as ‘qubits’ to process and store “quantum information” in, which can be in an infinite number of combinations of ‘1 and 0’. Large technology corporations, such as Google, Microsoft, Intel, and IBM are investing heavily in related projects that may lead to realize the quantum computer and technologies. At the same time, universities and research institutes around the world are researching novel quantum systems, adoptable for quantum computing. The Quantum Dynamics Unit at the Okinawa Institute of Science and Technology Graduate University (OIST), has recently made novel findings about electrons floating on the surface of liquid helium, a quantum system which may be a new candidate for quantum computing into reality. These results were published in Physical Review B. One of the common problems in quantum computing research using solids is that it is very difficult to make perfectly identical qubits because intrinsic defects or impurities in the materials used randomly affect each individual qubit performance. “Our motivation for pursuing a liquid helium system is that it is intrinsically pure and free of defects, which theoretically allows for the creation of perfectly identical qubits. Additionally, we can move electrons in this liquid helium system, which is difficult or nearly impossible in other quantum systems,” explained Prof. Denis Konstantinov, head of the Quantum Dynamics Unit. Therefore, it is believed that adopting this system for quantum computing might bring the whole field to the next level. Utilizing electrons on a liquid helium surface for quantum computing requires isolating individual electrons on a helium surface and controlling their quantum degrees of freedom, either motional or spin. It may also require the movement of electrons to different locations, thus it is also important to understand the physics of the interaction between electrons and the helium surface. It was previously discovered that electrons on helium can form a two-dimensional crystal, and some unique phenomena occur when this crystal moves along the helium surface, due to the interaction between electrons and surface waves. The OIST scientists, however, are the first to probe how these phenomena depend on the size of the electron crystal. To test this, Dr. Alexander Badrutdinov, Dr. Oleksandr Smorodin and OIST PhD student Jui-Yin Lin, built a microscopic channel device that contained an electron trap within to isolate a crystal of a relatively small number of electrons. This crystal would then be moved across the liquid helium surface by altering electrostatic potential of one of the device electrodes. This motion would be detected by measuring image charges, which are induced by the moving electrons, flowing through another electrode using a commercially available current amplifier and lock-in detector. “This research gave us some insights into the physics of the interaction between electrons and the helium surface, as well as expanded our micro-engineering capabilities” states Dr. Alexander Badrutdinov, a former member of the Quantum Dynamics Unit and the first author of the paper. “We successfully adopted a technology to confine electrons into microscopic devices, on the scale of few microns. With this technology we studied the motion of microscopic two-dimensional electron crystals along a liquid helium surface and saw no difference between the movement of large electron crystals, on the scale of millions to billions of electrons, and crystals as small as a few thousands of electrons, when theoretically, differences should exist.” This research is the first step at OIST in the prospect of using this system for quantum computing. According to Konstantinov, “the next step in this research is to isolate an even smaller electron crystal, and ultimately, a single electron, and to move them in this system. Unlike other systems, this system has the potential to be a pure, scalable system with mobile qubits.” In theory, this type of system would have the potential to revolutionize the quantum computing research field. A.O. Badrutdinov, A. V. Smorodin, D. G. Rees, J. Y. Lin, D. Konstantinov. Nonlinear transport of the inhomogeneous Wigner solid in a channel geometry. Physical Review B, 2016; 94 (19) DOI: 10.1103/PhysRevB.94.195311

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First Generation (1941-1956) World War gave rise to numerous developments and started off the computer age. Electronic Numerical Integrator and Computer (ENIAC) was produced by a partnership between University of Pennsylvania and the US government. It consisted of 18,000 vacuum tubes and 7000 resistors. It was developed by John Presper Eckert and John W. Mauchly and was a general purpose computer. "Von Neumann designed the Electronic Discrete Variable Automatic Computer (EDVAC) in 1945 with a memory to hold both a stored program as well as data." Von Neumann's computer allowed for all the computer functions to be controlled by a single source. Then in 1951 came the Universal Automatic Computer (UNIVAC I), designed by Remington rand and collectively owned by US census bureau and General Electric. UNIVAC amazingly predicted the winner of 1952, presidential elections, Dwight D. Eisenhower. In first generation computers, the operating instructions or programs were specifically built for the task for which computer was manufactured. The Machine language was the only way to tell these machines to perform the operations. There was great difficulty to program these computers and more when there were some malfunctions. First Generation computers used Vacuum tubes and magnetic drums (for data storage). The IBM 650 Magnetic Drum Calculator Second Generation Computers (1956-1963) The invention of Transistors marked the start of the second generation. These transistors took place of the vacuum tubes used in the first generation computers. First large scale machines were made using these technologies to meet the requirements of atomic energy laboratories. One of the other benefits to the programming group was that the second generation replaced Machine language with the assembly language. Even though complex in itself Assembly language was much easier than the binary code. Second generation computers also started showing the characteristics of modern day computers with utilities such as printers, disk storage and operating systems. Many financial information was processed using these computers. In Second Generation computers, the instructions (program) could be stored inside the computer's memory. High-level languages such as COBOL (Common Business-Oriented Language) and FORTRAN (Formula Translator) were used, and they are still used for some applications nowadays. The IBM 7090 Console in the Columbia Computer Center machine room, 1966. Pictured: A group of particle physicists who discovered the violation of charge-conjugation invariance in interactions of intermediate strength: Charles Baltay and Lawrence Kirsch of Nevis Lab (back row); Juliet Lee-Franzini of SUNY Stony Brook and team leader Paulo Franzini of Nevis Lab [V1#7]. Photo: Columbia Computer Center Newsletter, V1#7, Aug 1966, Columbiana Archive. Although transistors were great deal of improvement over the vacuum tubes, they generated heat and damaged the sensitive areas of the computer. The Integrated Circuit(IC) was invented in 1958 by Jack Kilby. It combined electronic components onto a small silicon disc, made from quartz. More advancement made possible the fittings of even more components on a small chip or a semi conductor. Also in third generation computers, the operating systems allowed the machines to run many different applications. These applications were monitored and coordinated by the computer's memory. The IBM 360/91 Fourth Generation (1971-Present) Fourth Generation computers are the modern day computers. The Size started to go down with the improvement in the integrated circuits. Very Large Scale (VLSI) and Ultra Large scale (ULSI) ensured that millions of components could be fit into a small chip. It reduced the size and price of the computers at the same time increasing power, efficiency and reliability. "The Intel 4004 chip, developed in 1971, took the integrated circuit one step further by locating all the components of a computer (central processing unit, memory, and input and output controls) on a minuscule chip." Due to the reduction of cost and the availability of the computers power at a small place allowed everyday user to benefit. First, the minicomputers which offered users different applications, most famous of these are the word processors and spreadsheets, which could be used by non-technical users. Video game systems like Atari 2600 generated the interest of general populace in the computers. In 1981, IBM introduced personal computers for home and office use. "The number of personal computers in use more than doubled from 2 million in 1981 to 5.5 million in 1982. Ten years later, 65 million PCs were being used." Computer size kept getting reduced during the years. It went down from Desktop to laptops to Palmtops. Mackintosh introduced Graphic User Interface in which the users don’t have to type instructions but could use Mouse for the purpose. The continued improvement allowed the networking of computers for the sharing of data. Local Area Networks (LAN) and Wide Area Network (WAN) were potential benefits, in that they could be implemented in corporations and everybody could share data over it. Soon the internet and World Wide Web appeared on the computer scene and fomented the Hi-Tech revolution of 90's. Fifth generation computers Fifth generation computers are mainly future computers. Of course some modern computers also belong to this generation. The aim of these computers is to develop devices that respond to natural language input and are capable of learning and self-organization. In these computers massive numbers of CPUs are used for more efficient performance. Voice recognition is a special feature in these computers. By using superconductors and parallel processing computer geeks are trying to make artificial intelligence a reality. Quantum computing, molecular and nanotechnology will change the face of computers in the coming years.Fifth generation computer.

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Researchers at Princeton have developed a method to cause perovskite particles to self-assemble. They say this produces more efficient, stable and durable perovskite LEDs that would be easier to manufacture than current LEDs, while emitting very strong light, that is easily tuned to display different colours. The crystal and diamond structure of perovskite exhibits either superconductivity or semi-conductivity depending on structure. The researchers’ advance was to add long-chain ammonium halide to the perovskite solution during processing, which constrained the formation of crystal in the film. Instead, what were formed were 5-10 nanometre crystallites which made the halide perovskite films far thinner and smoother. This meant that the LEDs emitted more photons per number of electrons entering the device than using alternative production methods. Kyoto University, working with Osaka Gas, has built a proof-of-concept nanoscale semiconductor that narrows wavelength bandwidth to concentrate light energy in solar cells. Kyoto researchers claim that current solar cells are not good at converting visible light into electrical power, having just 20% efficiency. The scientists wanted to capture and convert light produced by gas flames, so they chose silicon as it can withstand temperatures of up to 1000 degrees Celsius. They etched silicon plates to create a grid of identical, equidistant rods the structure of which could be altered to catch different wavelength bandwidths. Using this material, the scientists showed that they could raise the conversion efficiency of light to electricity by 40%. Scientists believe that misshapen proteins, called amyloids, can clump together and form masses in the brain which block normal cell function, leading to neurodegenerative disorders such as Alzheimer’s. A team of researchers from the University of Michigan and the University of Fribourg have developed a technique to measure amyloids’ shapes, volume, electrical charge, rotation speed and binding propensity. They call this information a ‘5D fingerprint’. Having more measurement categories could enable doctors to better understand, treat and predict problems associated with amyloids. The researchers created a nanopore (holes of 10-30 nanometres diameter, small enough that only one protein molecule can pass through at a time) on a substrate. The nanopore was sandwiched between saline solution layers to which an electric current was applied. By reading fluctuations in the current as the molecule passes through the pore researchers were able to determine the molecule’s ‘5D fingerprint’. Scientists from the Daegu Gyeongbuk Institute of Science and Technology in Korea have discovered a way to control colour changes by adding a coating of nanometres thick semiconducting materials to a metal substrate. Through the addition of a thin germanium film of 5-25 nanometres to a gold substrate the team could control the colour produced (through thin-film interference) – such as yellow, orange, blue and purple. The scientists hope that in the future a similar method could be used to create patterns or symbols on the substrate. Researchers at Northwest University have created a new type of nanomaterial called a COF colloid. Covalent organic frameworks (COFs) are strong polymers with many tiny pores which can be used to store for example energy, drugs or other cargo. These COFs usually come as a powdery substance which is, according to NWU, almost useless. However, the NWU team suspended the COF in a liquid ink which allows the material to be engineered to arbitrary sizes and thicknesses – opening up their potential use as designed carriers of drugs or other cargo to specific locations within the body. Moreover, the team discovered that they could watch the process of how the molecules come together to create COF colloids by using a transmission electron microscope. Researchers at Aalto University in Finland have created a nanoscale laser using nanoparticles. The device uses silver nanoparticles arranged in a periodic array. The optical feedback needed to generate the laser light is provided by radiative coupling (bouncing the captured light back and forth) between silver nanoparticles which effectively act as antennas. To produce a strong laser light the distance between particles was matched with the lasing wavelength to that they all radiate in unison. To provide the input energy for the laser organic fluorescent molecules were added. The benefits of such devices are that the laser can be made very small and very fast, which will be of use for chip-scale light sources in optical components. Scientists at the University of Massachusetts Amherst have discovered a type of conductive natural nanowire produced by bacteria. The wires, known as microbial nanowires, are protein filaments which bacteria use to make electrical connections with other microbes or minerals. The team has been looking at several species of geobacter bacteria for their potential use in electronics. In the most recent study the scientists used genetically modified G. Sulfurreducens which produces more filaments and expresses filament genes from many different types of bacteria, and discovered that the microbial nanowires are highly conductive (around 5 mS/cm) which the scientists claim is comparable to that of metallic nanostructures. The scientists attribute the conductivity to a large amount of aromatic amino acid allowing for improved conductivity along the filament. As a result they believe these have good potential for use in electronics. A microscopic mechanical drum – a vibrating aluminium membrane – has been cooled to less than one-fifth of a single quantum (packet of energy), which is lower than quantum physics would predict. The work from the National Institute of Standards and Technology (NIST) provides the possibility of cooling an object to absolute zero which would make it more sensitive as a sensor, store information for longer, or even be used in quantum computing according to NIST scientists. To achieve this effect the scientists manipulated the resonance of the cavity through the application of a microwave tone at a frequency below the cavity’s resonance. The beating of the drum’s surface releases photons; with each photon that leaves the drum as a result of the microwave excitation the drum loses heat. Scientists at The University of Manchester have braided multiple molecular strands to enable the tying of a very tight knot. The knot has eight crossings in a 192-atom closed loop which is about 20 nanometres long. The knot was created by a technique known as self-assembly, in which molecular strands are woven around metal ions causing crossing points. The ends of the strands were then fused by a chemical catalyst to close the loop and create the knot. The scientists think that this will enable further study into how molecular knotting affects strength and elasticity of materials which could lead to the knowledge of how to weave polymer strands to create new materials. The Rosetta Disk’s goal is to make a catalogue of languages and important documents to be preserved for the long term. On the small wearable pendant can be found microscopic pages. With the help of a microscope the preamble to the universal declaration of human rights can be read in 327 languages, a Swadesh Vocabulary List by PanLex Project (a phrasebook listing identical words and phrases in 719 languages), The Clock of The Long Now by Steward Brand and diagrams for the 10,000-year clock. This ‘wearable’ is made using a process similar to microchip lithography, which uses a laser bean to write onto a photosensitive material coated on a glass plate. These recorded features are then developed like a film, after which the plate is electroformed, which results in a disk made of solid nickel. The text is slightly raised from the surface, and requires optical magnification to read.

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When we make the move to quantum computers, we’ll need a quantum internet. And that’s why a team of researchers at Tsinghua University in China have built what they call the world’s first quantum router. Often called the holy grail of the tech world, a quantum computer uses the seemingly magical principles of quantum mechanics to achieve speeds well beyond today’s machines. At the moment, these counterintuitive contraptions are little more than lab experiments, but eventually, they’ll instantly handle calculations that would take years on today’s machines. The trick is that whereas the bits of a classical computer can only hold one value at any given time, a quantum bit — or qubit — can hold multiple simultaneous values, thanks to the superposition principle of quantum mechanics. But if we build a world of quantum computers, we’ll also need a way of transporting quantum data — the multiple values so delicately held in those qubits — from machine to machine. Led by post doctoral researcher Xiuying Chang, the Tsinghua University team seeks to provide such transportation, and though their work is still largely theoretical, they’ve taken an important step in the right direction. “Their router isn’t practical right now,” says Ari Dyckovsky, a researcher with National Institute of Standards and Technology (NIST) who specializes in quantum entanglement, “but it adds another reason that people should keep researching in this area.” Yes, there are already ways of moving quantum data between two places. Thanks to quantum entanglement — another mind-bending principle of quantum mechanics — you can move data between two quantum systems without a physical connection between them. And you can send quantum data across a single fiber-optic cable using individual photons. “Their router isn’t practical right now. But it adds another reason that people should keep researching in this area.” But for a true quantum internet, you need a way of routing quantum data between disparate networks — i.e., from one fiber-optic cable to another — and at the moment, that’s not completely possible. The problem is that if you look at a qubit, it’s no longer a qubit. In a classic computer, a transistor stores a single “bit” of information. If the transistor is “on,” for instance, it holds a “1.” If it’s “off,” it holds a “0.” But with quantum computer, information is represented by a system that can an exist in two states at the same time. Thanks to the superposition principle, such a qubit can store a “0” and “1” simultaneously. But if you try to read those values, the qubit “decoheres.” It turns into a classical bit capable of storing only one value. To build a viable quantum computer, researchers must work around this problem — and they must solve similar problems in building a quantum internet. The internet is all about routing data between disparate networks. A router uses a “control signal” to route a “data signal” from network to network. The trouble with a quantum router is that if you read the control signal, you break it. But in a paper recently published to the net, Xiuying Chang and her team describe an experiment in which they build a quantum router — complete with a quantum control signal — using two entangled photons. “This leads to more freedom to control the route of quantum data,” Luming Duan, who worked on the paper, tells Wired, “and I believe it is a useful device for future quantum internet.” As described by Technology Review, the team begins the experiment with a photon that exists in two quantum states at the same time: both a horizontal and a vertical polarization. Then they convert this single photon into two entangled protons — which means they’re linked together even though they’re physically separate — and both of these are also in a superposition of two quantum states. One photon serves as the control signal, and it routes the other photon — the data signal. The rub is that the method isn’t suited to large-scale quantum routing. You can’t expand it beyond the photons. “It is a nice check that coherence is maintained while converting between polarization and path entanglement, which will be an important operation for a large-scale quantum network,” says Steven Olmschenk, an assistant professor of physics and astronomy at Denison University. “But as the authors are careful to point out, the implementation that they have demonstrated cannot be scaled up, and is missing some of the key — and hard — features that will be necessary in a more general implementation.” In other words, the experiment only transmits one qubit at a time — and the quantum internet needs a bit more bandwidth than that. But this will come.Go Back to Top. Skip To: Start of Article.

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Trapping light means either stopping the light temporally or confining the light in space. Scientists have also been able to trap a light pulse in a tiny enclosure bounded by metamaterials; the light pulse retains its form but is kept from moving away. Previously only light of a short frequency interval could be trapped in this way. Now a group of scientists at Nanjing University in China have shown how a rather wide spectrum of light -- a rainbow of radiation -- can be trapped in a single structure. They propose to do this by sending the light rays into a self-similar-structured dielectric waveguide (SDW) -- essentially a light pipe with a cladding of many layers. Light of different colors propagates separately in (or is contained within) different layers, the layers each being tailored by color. They replace the conventional periodically-spaced, identical cladding layers with a non-periodic, self-similar pattern of successive layers made from two materials, A and B, with slightly different thicknesses and indices of refraction. Self similarity, in this case, means that the pattern of layers successively outwards would be as follows: A, AB, ABBA, ABBABAAB, and so forth. "The effect might be applied for on-chip spectroscopy or on-chip 'color-sorters,'" says Ruwen Peng, one of the Nanjing researchers. "It might also be used for photon processing and information transport in optical communications and quantum computing." Peng and her associates, who published their results in the American Institute of Physics' journal Applied Physics Letters, expect that they can create trapped "rainbows" for light in many portions of the electromagnetic spectrum, including microwave, terahertz, infrared, and even visible. The article "'Rainbow' trapped in a self-similar coaxial optical waveguide" by Qing Hu, Jin-Zhu Zhao, Ru-Wen Peng, Feng Gao, Rui-Li Zhang, and Mu Wang was published online in the journal Applied Physics Letters in April, 2010. See: http://link.aip.org/link/APPLAB/v96/i16/p161101/s1 Journalists may request a free PDF of this article by contacting email@example.comABOUT APPLIED PHYSICS LETTERS Jason Socrates Bardi | Newswise Science News Study offers new theoretical approach to describing non-equilibrium phase transitions 27.04.2017 | DOE/Argonne National Laboratory SwRI-led team discovers lull in Mars' giant impact history 26.04.2017 | Southwest Research Institute More and more automobile companies are focusing on body parts made of carbon fiber reinforced plastics (CFRP). However, manufacturing and repair costs must be further reduced in order to make CFRP more economical in use. Together with the Volkswagen AG and five other partners in the project HolQueSt 3D, the Laser Zentrum Hannover e.V. (LZH) has developed laser processes for the automatic trimming, drilling and repair of three-dimensional components. Automated manufacturing processes are the basis for ultimately establishing the series production of CFRP components. In the project HolQueSt 3D, the LZH has... Reflecting the structure of composites found in nature and the ancient world, researchers at the University of Illinois at Urbana-Champaign have synthesized thin carbon nanotube (CNT) textiles that exhibit both high electrical conductivity and a level of toughness that is about fifty times higher than copper films, currently used in electronics. "The structural robustness of thin metal films has significant importance for the reliable operation of smart skin and flexible electronics including... The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets. Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the... The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called... Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project. Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a... 28.04.2017 | Event News 20.04.2017 | Event News 18.04.2017 | Event News 28.04.2017 | Medical Engineering 28.04.2017 | Earth Sciences 28.04.2017 | Life Sciences

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What is a ruby and why is it red? Why is the friction so low on a sled? On a plot of P versus T, plot out the the phases of helium-three.... A favorite professor loved to craft simply stated preliminary exam questions that happened rhyme. Parts were often difficult - here the second question is still not fully answered. The ruby question is good as there are layers of depth to show how much you have thought about it. Sapphire, ruby and corundum are all very stable crystalline forms of Al2O3 - aluminum oxide.1 In ruby and sapphire a percent or so of the aluminum atoms are replaced by something else - chromium in the case of ruby. Chromium is a bit larger and has a different shape than aluminum. The result is a small distortion of the arrangement of oxygens around the chromiums. The most obvious visual difference is how the two crystals absorb light. The distortions change how visual light is absorbed. Pure corundum doesn't strongly absorb, ruby absorbs in the violet and yellow-green making it appear red.2 Structure is one of the features used to categorize solids. A crystal is a solid with a structure of atoms or molecules that repeat in three dimensions with a regular pattern called a lattice - single crystal snowflakes, table salt, diamonds... Groupings of smaller crystals form polycrystals - most metals, large snowflakes, ice and ceramics are polycrystals. Amorphous solids lack such order .. glass and almost anything organic for example. Impurities in crystals lead to changes in physical properties like the colors of gem stones. But there are many ways to disrupt a pure crystal lattice. Some locations can be empty, filled with some impurity or dislocations to the pattern of the crystal can lead to strong steel. Impurities create different electrical properties that allow semiconductors to exist - without ongoing theoretical and experimental work from the 40s, the integrated circuit wouldn't exist. Steel is largely iron with a few other things thrown in. Carbon turned out to be a big winner, but you have to get the recipe right and there are tens of thousands of variations giving a wide range of properties. Successful recipes were carefully guarded secrets .. the special steels used in Japanese swords, steels from Toledo and Damascus became legendary. Sometimes there was a bit of odd baggage - one recipe involved adding the urine from red-haired boys. Serious progress had to wait for real science. It is possible to have two dimensional structures with regular repeating patterns - graphene is a single layer of hexagonally packed carbon atoms. Amazing properties that lead in a recent Nobel prize in physics. You can easily make some yourself with a pencil and a bit of scotch tape.3 But it gets stranger... It is natural for a physicist to think in more than three dimensions. Space-time, the structure of space and time we live in, has four dimensions. Other more abstract hypotheses have eight, nine, eleven, twenty six. Calculations can have large numbers of dimensions. Sometimes this is practical in the real world - cryptology and cellphone modulation schemes both make use of higher dimensionalities. So it is natural to think of a four dimensional crystal -- one in space and time. And usually, for a variety of reasons, you quickly rule it out as non-physical. Afew years ago Frank Wilczek didn't throw it away and presented the idea of a time crystal that exists in four dimensions. The physical pattern is be stable, but a property of it repeats in time. Instead of regularly repeating atoms there would be a regularly repeating internal motion. It was very controversial - it looked like a perpetual motion machine, but loophole was spotted. Properties like electron spin might bunch up in direction at regular intervals in time .. a repeating lattice in time. As crazy as it sounds a few groups tried to make their own time crystals and two appear to have succeeded with papers passing peer review and due out in the near future. Here's one of the early papers and a high level description. Much more is likely to emerge in the next few months. Apart from being beautiful it has the potential to be incredibly useful. Quantum computing has the fundamental challenge of maintaining entanglement over time in a macroscopic object. It could be that time crystals are a mechanism to successfully address the problem and who knows what else... perhaps years, perhaps decades, perhaps never... ah the frontier 1 In pure corundum three electrons from each aluminum join with six O2- ions in an octahedral group. The aluminums are left without unpaired electrons and their energy levels are filled. This configuration is exceptionally stable and strong and is also colorless. 2 There is also a fluorescent emission in the red making the crystals beautifully red. This emission property is central to making lasers out of rubies. 3 rather than tell you, just watch this:) Not a recipe, but a bit of technique. It's Winter and that means reasoning with hard Winter squashes. I find it's much easier if you par-cook (not boil) them for a few minutes. Find a big enough pot and let it simmer for two or three minutes. If the squash is larger than the pot, just turn it over. That's it. Now it should slice easily. Something frustrating becomes easy.

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The problem comes in finding the dividing line between the two worlds -- or even in establishing that such a line exists. To that end, Keith Schwab, associate professor of physics who moved to Cornell this year from the National Security Agency, and colleagues have created a device that approaches this quantum mechanical limit at the largest length-scale to date. And surprisingly, the research also has shown how researchers can lower the temperature of an object -- just by watching it. The results, which could have applications in quantum computing, cooling engineering and more, appear in the Sept. 14 issue of the journal Nature. The device is actually a tiny (8.7 microns, or millionths of a meter, long; 200 nanometers, or billionths of a meter, wide) sliver of aluminum on silicon nitride, pinned down at both ends and allowed to vibrate in the middle. Nearby, Schwab positioned a superconducting single electron transistor (SSET) to detect minuscule changes in the sliver's position. According to the Heisenberg uncertainty principle, the precision of simultaneous measurements of position and velocity of a particle is limited by a quantifiable amount. Schwab and his colleagues were able to get closer than ever to that theoretical limit with their measurements, demonstrating as well a phenomenon called back-action, by which the act of observing something actually gives it a nudge of momentum. "We made measurements of position that are so intense -- so strongly coupled -- that by looking at it we can make it move," said Schwab. "Quantum mechanics requires that you cannot make a measurement of something and not perturb it. We're doing measurements that are very close to the uncertainty principle; and we can couple so strongly that by measuring the position we can see the thing move." The device, while undeniably small, is -- at about ten thousand billion atoms -- vastly bigger than the typical quantum world of elementary particles. Still, while that result was unprecedented, it had been predicted by theory. But the second observation was a surprise: By applying certain voltages to the transistor, the researchers saw the system's temperature decrease. "By looking at it you cannot only make it move; you can pull energy out of it," said Schwab. "And the numbers suggest, if we were to keep going on with this work, we would be able to cool this thing very cold. Much colder than we could if we just had this big refrigerator." The mechanism behind the cooling is analogous to a process called optical or Doppler cooling, which allows atomic physicists to cool atomic vapor with a red laser. This is the first time the phenomenon has been observed in a condensed matter context. Schwab hasn't decided if he'll pursue the cooling project. More interesting, he says, is the task of figuring out the bigger problem of quantum mechanics: whether it holds true in the macroscopic world; and if not, where the system breaks down. For that he's focusing on another principle of quantum mechanics -- the superposition principle -- which holds that a particle can simultaneously be in two places. "We're trying to make a mechanical device be in two places at one time. What's really neat is it looks like we should be able to do it," he said. "The hope, the dream, the fantasy is that we get that superposition and start making bigger devices and find the breakdown." Press Relations Office | EurekAlert! New quantum liquid crystals may play role in future of computers 21.04.2017 | California Institute of Technology Light rays from a supernova bent by the curvature of space-time around a galaxy 21.04.2017 | Stockholm University The nearby, giant radio galaxy M87 hosts a supermassive black hole (BH) and is well-known for its bright jet dominating the spectrum over ten orders of magnitude in frequency. Due to its proximity, jet prominence, and the large black hole mass, M87 is the best laboratory for investigating the formation, acceleration, and collimation of relativistic jets. A research team led by Silke Britzen from the Max Planck Institute for Radio Astronomy in Bonn, Germany, has found strong indication for turbulent processes connecting the accretion disk and the jet of that galaxy providing insights into the longstanding problem of the origin of astrophysical jets. Supermassive black holes form some of the most enigmatic phenomena in astrophysics. Their enormous energy output is supposed to be generated by the... The probability to find a certain number of photons inside a laser pulse usually corresponds to a classical distribution of independent events, the so-called... Microprocessors based on atomically thin materials hold the promise of the evolution of traditional processors as well as new applications in the field of flexible electronics. Now, a TU Wien research team led by Thomas Müller has made a breakthrough in this field as part of an ongoing research project. Two-dimensional materials, or 2D materials for short, are extremely versatile, although – or often more precisely because – they are made up of just one or a... Two researchers at Heidelberg University have developed a model system that enables a better understanding of the processes in a quantum-physical experiment... Glaciers might seem rather inhospitable environments. However, they are home to a diverse and vibrant microbial community. It’s becoming increasingly clear that they play a bigger role in the carbon cycle than previously thought. A new study, now published in the journal Nature Geoscience, shows how microbial communities in melting glaciers contribute to the Earth’s carbon cycle, a... 20.04.2017 | Event News 18.04.2017 | Event News 03.04.2017 | Event News 21.04.2017 | Physics and Astronomy 21.04.2017 | Health and Medicine 21.04.2017 | Physics and Astronomy

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Quantum mechanical phenomena . Quantum Mechanics. The study between quanta and elementary particles. Quanta – an indivisible entity of a quantity that has the same value as Planck’s constant which is related to energy and momentum of elementary particles. Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. Quantum mechanical phenomena The study between quanta and elementary particles. Quanta – an indivisible entity of a quantity that has the same value as Planck’s constant which is related to energy and momentum of elementary particles. Elementary Particle – a particle not known to have substructure or be composed of smaller particles. Quantum Mechanics (cont.) It generalizes all classical theories (excluding general relativity), results are typically only observable on the atomic and subatomic scales. The foundations of quantum mechanics were established during the first half of the twentieth century by Werner Heisenburg, Max Planck, Albert Einstein, Neils Bohr, Erwin Schrodinger, and Wolfgang Pauli The modern world of physics is notably founded on two tested and demonstrably sound theories of general relativity and quantum mechanics; theories which appear to contradict one another. However, while they do not directly contradict each other theoretically, they are resistant to being incorporated within one model. Quantum Mechanics (cont.) Einstein himself is well known for rejecting some of the claims of quantum mechanics. While clearly inventive in this field, he did not accept the more philosophical consequences and interpretations of quantum mechanics …these consequences are know as Quantum Mechanical Phenomena. Quantum Mechanical Phenomena Quantum mechanical phenomena include things such as: --the EPR paradox Quantum Teleportation is a quantum protocol where quantum information can be transmitted using an entangled pair of qubits. Qubit - a two dimensional vector that measures quantum information. Quantum Teleportation cannot teleport matter, energy, or information at a speed faster than light, but it is useful for quantum communication and calculations. Quantum Teleportation (cont.) Assume that A and B share an entangled qubit AB. Let C denote the qubit A wishes to transmit to B. A applies a unitary operation on the qubits AC and measures the result to obtain two classical bits. In this process, the two qubits are destroyed. B's qubit, B now contains information about C however the information is somewhat randomized. More specifically, B's qubit is in one of four states uniformly chosen at random and B cannot obtain any information about C from his qubit. A provides her two measured qubits, which indicate which of the four states B possesses. B applies a unitary transformation which depends on the qubits he obtains from A, transforming his qubit into an identical copy of the qubit C. The EPR Paradox The EPR paradox is a dichotomy, which means it yields two results but they’re coincide with each other. EPR stands for Einstein, Podolsky, and Rosen; who are the people that introduced the thought to show that quantum mechanics isn’t totally physical. The EPR paradox draws on a phenomenon predicted by quantum mechanics to show that measurements performed on spatially separated parts of a quantum system can apparently have an instantaneous influence on one another. This result is known as “nonlocal behavior” or as Einstein put “a spooky action at a distance”. The EPR Paradox (cont.) The EPR paradox relates to the concept of locality. Locality states that a physical process at one location should have no immediate effect on something at a different location. Usually information cannot be transferred faster than the speed of light without contradicting causality, however if you combine quantum mechanics with classical views of physics you can contradict locality without contradicting locality, thus resulting in The EPR Paradox! Quantum Entanglement is a quantum mechanical phenomena where the quantum states of two or more objects are linked so one object can’t be completely described without mentioning the other(s) even thought they may be spatially separated. In theory this results in correlations between physical properties of remote systems. The distance between the two particles is irrelevant. Some physicists have theorized that there are hidden variables that are determined when the pair of particles are entangled. The rules of quantum mechanics curiously appear to prevent an outsider from using these methods to actually transmit information, and therefore do not appear to allow for time travel or Faster Than Light communication. This misunderstanding seems to be widespread in popular press coverage of quantum teleportation experiments. The ideas are commonly used in science fiction literature without the complicated explanation of course. The assumption that time travel or superluminal communications is impossible allows one to derive interesting results such as the no cloning theorem, and how the rules of quantum mechanics work to preserve causality is an active area of research.

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Higher Education Education All Higher Education Education Resources - Air Pollution Model (aerial) Explore the connections between point-source pollution, geography, and wind on regional air quality. - Air Pollution Model (cross-section) Explore the connections between pollution sources, weather, geography, and air quality. - Atomic Structure Explore ion formation, isotopes, and electron orbital placement using interactive models of atomic structure. - Boiling Point This model allows you to explore why polar and non-polar substances have very different boiling points. - Can We Feed the Growing Population? Explore the interconnected resources that make up our agricultural system as you consider food production. Explore the effects of homogeneous catalysts. - Cellular Respiration Explore how your body converts the chemical energy of glucose into the chemical energy of ATP. - Ceramic Forces Explore what happens when a force is exerted on a ceramic material. - Charged and Neutral Atoms Explore the role of charge in interatomic interactions. - Comparing Attractive Forces Explore different attractive forces between various molecules. - Comparing Dipole-Dipole to London Dispersion Investigate the difference in the attractive force between polar and non-polar molecules. - Concentrating Charge and Electric Fields Take the same amount of charge and try spreading it out or concentrating it. What effect does that have on other moving charged particles? - Crookes Tube Experiment with a simulated Crookes tube for qualitative results similar to Thomson's experiments in which the electron was discovered. - DC Circuits: Parallel Resistances Learn about parallel circuits by interacting with a virtual breadboard. - DC Circuits: Series Resistances Learn about series circuits by interacting with a virtual breadboard. - DC Circuits: Series-Parallel Resistances Learn about series-parallel circuits by interacting with a virtual breadboard. - Diffusion Across a Semipermeable Membrane Explore the role of pore size in the diffusion of a substance across a membrane. - Diffusion and Molecular Mass Explore the role of a molecule's mass with respect to its diffusion rate. - Diffusion and Temperature Explore the role of temperature in the rate of diffusion of a substance. - Diffusion of a Drop Explore the random molecular motion of a dye in water. - Diffusion, Osmosis and Active Transport Explore how water and ions can diffuse both passively and actively through cell membranes. - DNA to Protein (HTML5 Model) Explore how the code embedded in DNA is translated into a protein. DNA transcription and mRNA translation are modeled. - DNA to Protein (Java-based Activity) Explore what DNA is and how proteins are synthesized from the genetic information stored in it. - Electrons in Atoms and Molecules The interactions of electrons with matter are central to many technologies from transistors to sophisticated quantum computing. Discover how atoms can be charged, and manipulate charge and distance to examine Coulomb's Law. - Excited States and Photons Investigate how atoms can be excited to give off radiation. - Exploring Electron Properties Compare the behavior of electrons to that of other charged particles to discover properties of electrons such as charge and mass. - Factors Affecting London Dispersion Attractions Explore the role of size and shape in the strength of London dispersion attractions. - Global Climate Change Model: Making Predictions About Future Climate Explore how changing human emissions may affect Earth's temperature in the future. - How Electrons Move Discover the forces affecting the movement of electrons, including electric and magnetic fields. - Hydraulic Fracturing Model Explore how hydraulic fracturing is used to extract oil and natural gas and how the process may affect local aquifers. - Hydrogen Bonds: A Special Type of Attraction Explore the polar molecule interactions known as hydrogen bonds. - Intermolecular Attractions and States of Matter Explore how states of matter are related to the strength of intermolecular attractions. - Introduction to Quantum Mechanics Discover the quantum nature of electrons including their wave nature, tunneling abilities, and their bound and excited states. - Land Management Model Explore the effects of different land management strategies, terrain, and climate on erosion rate and soil quality. - Metal Forces Explore what happens when a force is exerted on a metallic material. - Modeling Transcription Explore how an mRNA copy is made of DNA. - Modeling Translation Explore how a protein is made from an mRNA sequence. - Molecular View of a Gas Explore the structure of a gas at the molecular level. - Molecular View of a Liquid Explore the structure of a liquid at the molecular level. - Molecular View of a Solid Explore the structure of a solid at the molecular level. Explore how changing the DNA sequence can change the amino acid sequence of a protein. - Oil and Water Explore the interactions that cause water and oil to separate from a mixture. Explore the factors that affect a pendulum's motion. - Pendulum and Spring Explore the motion of a pendulum suspended by a spring. - Phase Change Explore what happens at the molecular level during a phase change. - Planet Hunting Model Explore how a star's movement and light intensity are affected by an orbiting planet. Explore some characteristics of stars and planets that are important to habitability potential. - Plastic Forces Explore what happens when a force is exerted on a polymeric plastic material. - Polarity and Attractive Strength Explore the role of polarity in the strength of intermolecular attractions. - Protein Folding Explore how hydrophobic and hydrophilic interactions cause proteins to fold into specific shapes. - Quantum Tunneling Explore the unique concept of quantum tunneling and its importance to modern technology. - Scanning Tunneling Microscopy Use a virtual scanning tunneling microscope to explore the quantum tunneling effect. - Seeing Intermolecular Attractions Explore different types of attractions between molecules. - Seismic Explorer Explore the pattern of earthquakes on Earth, including magnitude, depth, location, and frequency. Explore the structure and behavior of natural and doped semiconductors. Explore why excited atoms emit different wavelengths of radiation and learn how to identify atoms based on their unique atomic spectra. - Spring Model Explore the factors that affect a spring's motion. - Sunlight, Infrared, CO2 and the Ground Explore how solar radiation interacts with Earth’s surface and atmosphere. - The Temperature-Pressure Relationship Explore the relationship between the temperature of a gas and the pressure it exerts on its container. - The Temperature-Volume Relationship Explore the relationship between the temperature of a gas and its volume. - The Volume-Pressure Relationship Investigate the relationship between the volume of a gas and the pressure it exerts on its container. - Tire Forces Explore what happens when a force is exerted on a rubber tire. - Transistors: The Field Effect The field effect transistor is the most common type of transistor. - Troubleshooting DC Circuits Find the faulted resistor in a simulated circuit. - Water Model Explore how water moves through Earth’s layers and determine whether wells can produce sustainable amounts of water while maintaining the health of the underlying aquifer. - What Are Our Energy Choices? Explore the advantages and disadvantages of using renewable and non-renewable sources to generate electricity. - What is Pressure? Explore pressure at the atomic level. - What Is the Future of Earth's Climate? Examine climate data and models to predict Earth's future climate. - Will the Air Be Clean Enough to Breathe? With more of the world becoming industrialized, will the air be clean enough to breathe? - Will There Be Enough Fresh Water? As the human population has grown, water use has increased. Explore water movement and predict water availability.

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Will we ever realize the sci-fi dream of human teleportation? Physicists have already successfully teleported tiny objects. (See Beam Me Up, Schrödinger for more on the mechanics of quantum teleportation.) What will it take to extend the technique to a living, breathing human being? Quantum teleportation is possible because of two quantum phenomena that are utterly foreign to our everyday experience: entanglement and superposition. Entanglement is the connection that links the quantum states of two particles, even when they are separated: The two particles can be described only by their joint properties. Though there is no classical analogue for entanglement, in his book Dance of the Photons Zeilinger imagined how entanglement might work if it could be applied to a pair of ordinary dice instead of a pair of subatomic particles: “The science fiction Quantum Entanglement Generator produces pairs of entangled dice. These dice do not show any number before they are observed.” In other words, they are in a superposition of states where there is an equal chance of producing any number between one and six. “When one die is observed, it randomly chooses to show a number of dots. Then, the other distant die instantly shows the same number.” This works no matter how far apart the dice are. They can be sitting beside each other or on opposite ends of the universe. In either case, when the particle over here is measured to be in one of many possible states, then we can infer the state of the particle over there, even though no energy, no mass, and no information travels between A and B when the first one is observed. The state of particle B simply is what it is. The difficult concept is that B’s state corresponds with the state of the measured particle A. Entanglement is so confounding that in the early days of quantum theory, when entanglement was supported only by thought experiments and math on paper, Einstein famously derided it as “spooky action at a distance.” Today, though, entanglement has been thoroughly tested and verified. In fact, entangling particles isn’t even the hard part: For physicists, the most difficult task is maintaining the entanglement. An unexpected particle from the surrounding environment—something as insubstantial as a photon—can jostle one of the entangled particles, changing its quantum state. These interactions must be carefully controlled or else this fragile connection will be broken. If entanglement is one gear in the quantum machinery of teleportation, the second critical gear is superposition. Remember the thought experiment about Schrödinger’s cat? A cat, a flask of poison, and a radioactive source are all placed in a sealed box. If the source decays and emits a particle, then the flask breaks and the cat dies. While the box is closed, we can’t know whether the cat is living or dead. Moreover, the cat can be considered both alive and dead until the box is opened: The cat will stay in a superposition of the two states until a “measurement is made—that is, until we look in the box and observe that the cat is either alive or dead. Schrödinger never tried this on a real cat—in fact, he drew up the thought experiment just to demonstrate the apparently preposterous implications of quantum theory, and to force theorists to examine what constitutes a “measurement”—but today scientists have demonstrated that superposition is real using systems that are increasingly large (albeit still much smaller than a cat). In 2010, a group of researchers at the University of California, Santa Barbara demonstrated superposition in a tiny mechanical resonator—like a tuning fork, it vibrates at a characteristic frequency, but just like the cat it doesn’t exist in a single position until measured. Last year, another group of researchers demonstrated quantum superposition in systems of as many as 430 atoms. Before superposition and entanglement appear in a human-scale teleporter, if ever, they will be harnessed for multiple applications in computing. Quantum cryptography uses entanglement to encode messages and detect eavesdropping. Because observation perturbs entanglement, eavesdropping destroys information carried by entangled particles. And if two people each receive entangled particles, they can generate an entirely secure key. Quantum cryptography is an active area of research and some systems are already on the market. Quantum mechanical superposition and entanglement could also be exploited to make faster and more powerful computers that store information in quantum states, known as “qubits,” instead of traditional electronic bits. Quantum computers could solve problems that are intractable for today’s computers. Whether it’s possible to make a working quantum computer is still in question, but roughly two dozen research groups around the world are avidly investigating methods and architectures. So we know how to teleport one particle. But what if we want to make like Captain Kirk and teleport an entire human being? Remember that we wouldn’t be moving Kirk’s molecules from one place to another. He would interact with a suite of previously-entangled particles, and when we read the quantum state we would destroy the complex quantum information that makes his molecules into him while instantly providing the information required to recreate his quantum state from other atoms in a distant location. Quantum mechanics doesn’t forbid it. The rules of quantum mechanics still apply whether you’re talking about a system of two particles or human being made of 1027 atoms. “The size doesn’t matter in and of itself,” says Andrew Cleland, a physicist at the University of California, Santa Barbara. Macroscopic systems like superconductors and Bose-Einstein condensates show quantum effects while arbitrarily large. From an engineering standpoint, though, teleporting larger objects becomes an increasingly tough problem. Cleland comments, “Taking any object and putting it in a quantum state is hard. Two is multiply hard.” Maintaining entanglement between particle requires isolating them from interactions that would break their entanglement. We don’t want Captain Kirk to end up like The Fly, so we need to keep the particles absolutely isolated. What if we start with something simpler: Instead of teleporting a person, can we teleport a much smaller living thing—like a virus? In 2009, Oriol Romero-Isart of the Max-Planck-Institut fur Quantenoptik in Germany and his colleagues proposed just such an experiment. Using current technology, it should be possible to demonstrate superposition in a virus, they argued. They didn’t try it, but laid out a procedure: First, store the virus in a vacuum to reduce interactions with the environment, and then cool it to its quantum ground state before pumping it with enough laser light to create a superposition of two different energy states. This is possible in theory because some viruses can survive cold and vacuum. But humans are hot, and that thermal energy is a problem. “We have quadrillions of quantum states superimposed at the same time, dynamically changing,” says Cleland. Not only are we hot, but we interact strongly with our environment: We touch the ground, we breathe. Ironically, our need to interact with our environment, our sheer physicality, could come between us and the dream of human teleportation.

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Quantum Computing and the Cryptography Conundrum By leveraging existing networking infrastructure and adding suitable post-quantum key distribution techniques, it is possible to take a “quantum leap” in securing your data. By: Anand Patil On October 23, 2019, researchers from Google made an official announcement of a major breakthrough – one that scientists compared to the Wright Brothers’ first flight, or even man’s first moon landing. They said to have achieved Quantum Supremacy, meaning that they had created a Quantum Computer that could perform a calculation that is considered impossible by the classical computers of today. The announcement was a landmark, highlighting the possibilities of Quantum Computing. The concept of Quantum Computing itself isn’t new. It is a field that has been a point of interest of physicists and computer researchers since the 1980s. Google’s announcement, however, has brought it to the mainstream, and shone a spotlight on the promise that this niche field of innovation holds. Of course, like someone once said, with great power comes with great responsibility, so this field isn’t without complexities. The Possibilities of Quantum Computing Quantum Computing is a branch of computer science that is focused on leveraging the principles of quantum physics to develop computer technology. Quantum Computers hold the promise to power major advances in various fields that require complex calculations – from materials science and pharmaceuticals to aerospace and artificial intelligence (AI). So far, Quantum Computers have been nothing more than fancy laboratory experiments – large and expensive – but they have successfully demonstrated that the underlying principles are sound and have the potential to transform industries and accelerate innovation like never before. This has spurred scientific and industrial interest in this nascent field, giving rise to multiple projects across the world in pursuit of creating a viable, general-use Quantum Computer. That said, it may still be many years before Quantum Computers are commercially and generally available. So Why Does It Matter Today? The possibility of Quantum Computers poses a serious challenge to cryptographic algorithms deployed widely today. Today’s key-exchange algorithms, like RSA, Diffie-Hellman, and others, rely on very difficult mathematical problems such as prime factorization for their security, which a Quantum computer would be able to solve much faster than a classical computer. For example, it would take a classical computer centuries or even longer, to break modern algorithms like DH, RSA-2048 etc. by using brute-force methods. However, given the power and efficiency of quantum machines in calculations such as finding prime factors of large numbers – it may be possible for a quantum computer to break current asymmetric algorithms in a matter of days So, while the encrypted internet is not at risk at the moment, all that a bad actor has to do is capture the encrypted data today including the initial key exchange, and then wait until a powerful enough quantum computer is available – to decrypt it. This is particularly a problem for organizations that have large amounts of sensitive data that they need to protect over the long term – such as Banks, Governments and Defense agencies. What Can I Do Now? For organizations that could be at risk in the future, this is the best time to start evaluating “post-quantum” cryptography. Simply put, this means moving to algorithms and/or keys that are a lot more robust and can withstand a brute-force attack by a quantum computer –i.e. quantum resistant. The National Institute of Standards and Technology (NIST) in the US is leading the effort towards the standardization of post-quantum secure algorithms. However, given the lengthy process involved, this may take many years to fructify. An alternative is to use “Quantum Key Distribution” (QKD) techniques with existing algorithms that are considered quantum-safe. This involves using a dedicated optical channel to exchange keys using the quantum properties of photons. Any attempt to “tap” this secure channel will lead to a change in the quantum state of the photon and can be immediately detected – and therefore the key is unhackable. One of the limitations of QKD in this method is the need for a dedicated optical channel that cannot span more than 50km between the two terminals. Of course, this also means that the existing encryption devices or routers should be capable of ingesting such “Quantum-Generated” keys. Post-Quantum Cryptography and Cisco Cisco is an active contributor to the efforts to standardize post-quantum algorithms. However, recognizing that an implementable standard may be some years away, there is work ongoing to ensure that organizations are able to implement quantum-resistant encryption techniques in the interim, that leverage existing network devices like routers – which are most commonly used as encryptors. To start with, a team of veteran technical leaders and cryptography experts from Cisco US – David McGrew, Scott Fluhrer, Lionel Florit and the engineering team in Cisco India lead by Amjad Inamdar and Ramas Rangaswamy developed an API interface called the “Secure Key Import Protocol” – or SKIP – through which Cisco routers can securely ingest keys from an external post-quantum key source. This allows existing Cisco routers to be quantum-ready, with just the addition of an external QKD system. Going forward, this team is working on a way to deliver quantum-safe encryption keys without the need for short-range point-to-point connections. The advantage of this method is that organizations can integrate post-quantum key sources with existing networking gear in a modular fashion – without the need to replace anything already installed. In this manner, you could create a quantum-ready network for all traffic with minimal effort. Getting Ready for the Post-Quantum World Quantum Supremacy is an event which demonstrates that a quantum machine is able to solve a problem that no classical computer can solve in a feasible amount of time. This race has gathered momentum in the recent past with several companies joining the bandwagon, and some even claiming to have achieved it. There is an unprecedented amount of attention focused on making a commercially viable quantum computer. Many believe it is inevitable, and only a question of time. When it does happen, the currently used cryptography techniques will become vulnerable, and therefore be limited in their security. The good news is, there are methods available to adopt strong encryption techniques that will remain secure even after quantum computers are generally available. If you are an organization that wants to protect its sensitive data over the long term, you should start to evaluate post-quantum secure encryption techniques today. By leveraging existing networking infrastructure and adding suitable post-quantum key distribution techniques, it is possible to take a “quantum leap” in securing your data. (The author is Director, Systems Engineering, Cisco India and SAARC and the views expressed in this article are his own)

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The story so far: The allure of quantum computers (QC) is their ability to take advantage of quantum physics to solve problems too complex for computers that use classical physics. The 2022 Nobel Prize for physics was awarded for work that rigorously tested one such ‘experience’ and paved the way for its applications in computing – which speaks to the contemporary importance of QCs. Several institutes, companies and governments have invested in developing quantum-computing systems, from software to solve various problems to the electromagnetic and materials science that goes into expanding their hardware capabilities. In 2021 alone, the Indian government launched a National Mission to study quantum technologies with an allocation of ₹8,000 crore; the army opened a quantum research facility in Madhya Pradesh; and the Department of Science and Technology co-launched another facility in Pune. Given the wide range of applications, understanding what QCs really are is crucial to sidestep the misinformation surrounding it and develop expectations that are closer to reality. How does a computer use physics? A macroscopic object – like a ball, a chair or a person – can be at only one location at a time; this location can be predicted accurately; and the object’s effects on its surroundings can’t be transmitted faster than at the speed of light. This is the classical ‘experience’ of reality. For example, you can observe a ball flying through the air and plot its trajectory according to Newton’s laws. You can predict exactly where the ball will be at a given time. If the ball strikes the ground, you will see it doing so in the time it takes light to travel through the atmosphere to you. Quantum physics describes reality at the subatomic scale, where the objects are particles like electrons. In this realm, you can’t pinpoint the location of an electron. You can only know that it will be present in a given volume of space, with a probability attached to each point in the volume – like 10% at point A and 5% at point B. When you probe this volume in a stronger way, you might find the electron at point B. If you repeatedly probe this volume, you will find the electron at point B 5% of the time. There are many interpretations of the laws of quantum physics. One is the ‘Copenhagen interpretation’, which Erwin Schrödinger popularised using a thought-experiment he devised in 1935. There is a cat in a closed box with a bowl of poison. There is no way to know whether the cat is alive or dead without opening the box. In this time, the cat is said to exist in a superposition of two states: alive and dead. When you open the box, you force the superposition to collapse to a single state. The state to which it collapses depends on the probability of each state. Similarly, when you probe the volume, you force the superposition of the electrons’ states to collapse to one depending on the probability of each state. (Note: This is a simplistic example to illustrate a concept.) The other ‘experience’ relevant to quantum-computing is entanglement. When two particles are entangled and then separated by an arbitrary distance (even more than 1,000 km), making an observation on one particle, and thus causing its superposition to collapse, will instantaneously cause the superposition of the other particle to collapse as well. This phenomenon seems to violate the notion that the speed of light is the universe’s ultimate speed limit. That is, the second particle’s superposition will collapse to a single state in less than three hundredths of a second, which is the time light takes to travel 1,000 km. (Note: The ‘many worlds’ interpretation has been gaining favour over the Copenhagen interpretation. Here, there is no ‘collapse’, automatically removing some of these puzzling problems.) How would a computer use superposition? The bit is the fundamental unit of a classical computer. Its value is 1 if a corresponding transistor is on and 0 if the transistor is off. The transistor can be in one of two states at a time – on or off – so a bit can have one of two values at a time, 0 or 1. The qubit is the fundamental unit of a QC. It’s typically a particle like an electron. (Google and IBM have been known to use transmons, where pairs of bound electrons oscillate between two superconductors to designate the two states.) Some information is directly encoded on the qubit: if the spin of an electron is pointing up, it means 1; when the spin is pointing down, it means 0. But instead of being either 1 or 0, the information is encoded in a superposition: say, 45% 0 plus 55% 1. This is entirely unlike the two separate states of 0 and 1 and is a third kind of state. The qubits are entangled to ensure they work together. If one qubit is probed to reveal its state, so will some of or all the other qubits, depending on the calculation being performed. The computer’s final output is the state to which all the qubits have collapsed. One qubit can encode two states. Five qubits can encode 32 states. A computer with N qubits can encode 2N states – whereas a computer with N transistors can only encode 2 × N states. So a qubit-based computer can access more states than a transistor-based computer, and thus access more computational pathways and solutions to more complex problems. How come we’re not using them? Researchers have figured out the basics and used QCs to model the binding energy of hydrogen bonds and simulate a wormhole model. But to solve most practical problems, like finding the shape of an undiscovered drug, autonomously exploring space or factoring large numbers, they face some fractious challenges. A practical QC needs at least 1,000 qubits. The current biggest quantum processor has 433 qubits. There are no theoretical limits on larger processors; the barrier is engineering-related. Qubits exist in superposition in specific conditions, including very low temperature (~0.01 K), with radiation-shielding and protection against physical shock. Tap your finger on the table and the states of the qubit sitting on it could collapse. Material or electromagnetic defects in the circuitry between qubits could also ‘corrupt’ their states and bias the eventual result. Researchers are yet to build QCs that completely eliminate these disturbances in systems with a few dozen qubits. Error-correction is also tricky. The no-cloning theorem states that it’s impossible to perfectly clone the states of a qubit, which means engineers can’t create a copy of a qubit’s states in a classical system to sidestep the problem. One way out is to entangle each qubit with a group of physical qubits that correct errors. A physical qubit is a system that mimics a qubit. But reliable error-correction requires each qubit to be attached to thousands of physical qubits. Researchers are also yet to build QCs that don’t amplify errors when more qubits are added. This challenge is related to a fundamental problem: unless the rate of errors is kept under a certain threshold, more qubits will only increase the informational noise. Practical QCs will require at least lakhs of qubits, operating with superconducting circuits that we’re yet to build – apart from other components like the firmware, circuit optimisation, compilers and algorithms that make use of quantum-physics possibilities. Quantum supremacy itself – a QC doing something a classical computer can’t – is thus at least decades away. The billions being invested in this technology today are based on speculative profits, while companies that promise developers access to quantum circuits on the cloud often offer physical qubits with noticeable error rates. The interested reader can build and simulate rudimentary quantum circuits using IBM’s ‘Quantum Composer’ in the browser.

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Silicon is a material widely used in computing: It is used in computer chips, circuits, displays and other modern computing devices. Silicon is also used as the substrate, or the foundation of quantum computing chips. Researchers at the Superconducting Quantum Materials and Systems Center, hosted by the U.S. Department of Energy’s Fermi National Accelerator Laboratory, demonstrated that silicon substrates could be detrimental to the performance of quantum processors. SQMS Center scientists have measured silicon’s effect on the lifespan of qubits with parts-per-billion precision. These findings have been published in Physical Review Applied. New approaches to computing Calculations once performed on pen and paper have since been handed to computers. Classical computers rely on bits, 1 or 0, which have limitations. Quantum computers offer a new approach to computing that relies on quantum mechanics. These novel devices could perform calculations that would take years or be practically impossible for a classical computer to perform. Using the power of quantum mechanics, qubits—the basic unit of quantum information held within a quantum computing chip—can be both a 1 and a 0 at the same time. Processing and storing information in qubits is challenging and requires a well-controlled environment. Small environmental disturbances or flaws in the qubit’s materials can destroy the information. Qubits require near-perfect conditions to maintain the integrity of their quantum state, and certain material properties can decrease the qubit lifespan. This phenomenon, called quantum decoherence, is a critical obstacle to overcome to operate quantum processors. Disentangling the architecture The first step to reduce or eliminate quantum decoherence is to understand its root causes. SQMS Center scientists are studying a broadly used type of qubit called the transmon qubit. It is made of several layers of different materials with unique properties. Each layer, and each interface between these layers, play an important role in contributing to quantum decoherence. They create “traps” where microwave photons—key in storing and processing quantum information—can be absorbed and disappear. Researchers cannot unequivocally distinguish where the traps are located or which of the various materials or interfaces are driving decoherence based on the measurement of the qubit alone. Scientists at the SQMS Center use uniquely sensitive tools to study these effects from the materials that make up the transmon qubits. “We are disentangling the system to see how individual sub-components contribute to the decoherence of the qubits,” said Alexander Romanenko, Fermilab’s chief technology officer, head of the Applied Physics and Superconducting Technology Division and SQMS Center quantum technology thrust leader. “A few years ago, we realized that our [superconducting radio frequency] cavities could be tools to assess microwave losses of these materials with a preciseness of parts-per-billion and above.” Measurements at cold temperatures SQMS Center researchers have directly measured the loss tangent—a material’s ability to absorb electromagnetic energy—of high-resistivity silicon. These measurements were performed at temperatures only hundreds of a degree above absolute zero. These cold temperatures offer the right conditions for superconducting transmon qubits to operate. “The main motivation for why we did this experiment was that there were no direct measurements on this loss tangent at such low temperatures,” said Mattia Checchin, SQMS Center scientist and the lead researcher on this project. No material is perfect. Through rigorous testing and studies, researchers are building a more comprehensive understanding of the materials and properties best suited for quantum computing. Checchin cooled a metallic niobium SRF cavity in a dilution refrigerator and filled it with a standing electromagnetic wave. After placing a sample of silicon inside the cavity, Checchin compared the time the wave dissipated without the silicon present to the time with it present. He found that the waves dissipated more than 100 times faster with the silicon present—from 100 milliseconds without silicon to less than a millisecond with it. “The silicon dissipation we measured was an order of magnitude worse than the number widely reported in the [quantum information science] field,” said Anna Grassellino, director of the SQMS Center. “Our approach of disentangling the problem by studying each qubit sub-component with uniquely sensitive tools has shown that the contribution of the silicon substrate to decoherence of the transmon qubit is substantial.” Companies developing quantum computers based on quantum computing chips often use silicon as a substrate. SQMS Center studies highlight the importance of understanding which of silicon’s properties have negative effects. This research also helps define specifications for silicon that would ensure that substrates are useful. Another option is to substitute the silicon with sapphire or another less lossy material. “Sapphire, in principle, is like a perfect insulator—so much better than silicon,” said Checchin. “Even sapphire has some losses at really low temperatures. In general, you would like to have a substrate that is lossless.” Researchers often use the same techniques for fabricating silicon-based microelectronic devices to place qubits on silicon substrate. So sapphire has rarely been used for quantum computing. “It has taken years of material science and device physics studies to develop the niobium material specifications that would ensure consistently high-performances in SRF cavities,” said Romanenko. “Similar studies need to be done for materials that comprise superconducting qubits. This effort includes researchers working together with the material industry vendors.” Regardless of which material is used for qubits, eliminating losses and increasing coherence time is crucial to the success of quantum computing. No material is perfect. Through rigorous testing and studies, researchers are building a more comprehensive understanding of the materials and properties best suited for quantum computing. This loss tangent measurement is a substantial step forward in the search for the best materials for quantum computing. SQMS Center scientists have isolated a problem and can now explore whether a more refined version of silicon or sapphire will harness the computational power of a qubit. The Superconducting Quantum Materials and Systems Center is one of the five U.S. Department of Energy National Quantum Information Science Research Centers. Led by Fermi National Accelerator Laboratory, SQMS is a collaboration of 23 partner institutions—national labs, academia and industry—working together to bring transformational advances in the field of quantum information science. The center leverages Fermilab’s expertise in building complex particle accelerators to engineer multiqubit quantum processor platforms based on state-of-the-art qubits and superconducting technologies. Working hand in hand with embedded industry partners, SQMS will build a quantum computer and new quantum sensors at Fermilab, which will open unprecedented computational opportunities. For more information, please visit sqms.fnal.gov. Fermi National Accelerator Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

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The world of technology is fast. We see innovations happening every single day and trying to keep up with all of the new buzzwords is far from easy. Our aim simplifies learning some of the latest or maybe most confusing technology. This is the second installment in Frequently Asked Questions in Technology. If you want to see the ideas or concepts, we discussed in the first part, check it out here: Frequently Asked Questions in Technology (Part 1). This month we will be discussing net neutrality, big data, quantum computing, and more. Net neutrality is the idea and principle that Internet Service Providers treat all content and all data the same. It’s the concept that all Internet Service Providers should charge the same prices for all users, all content, and all websites. Old net-neutrality laws didn’t let ISPs play favorites. They weren’t allowed to slow down certain websites that didn’t align with their beliefs or charge more for sites that used more data. In 2018, the Federal Communications Commission, which handles the law, voted to repeal net neutrality. Net neutrality lasted from 2015 until 2018. And while we may not see the repercussions of not having net neutrality in a blatant form. It’s important to understand this concept. This continues to be argued over in court and in within politics. Net neutrality supports the concept of free speech. It believes in uncensored voices. Certain states are looking to reintroduce some form of net neutrality in 2021. The latest buzzword or buzz phrase in technology is quantum computing. Before we get into what quantum computing is, we should first define what quantum mechanics is. Quantum mechanics is also referred to as quantum physics or quantum theory. It explains how the particles that make up atoms work. Quantum computing employs the ideas of quantum mechanics to enhance the processing power of computers. Using quantum computing can help data analysts solve computational problems faster. For some equations, quantum computing can be more beneficial than even supercomputers. Quantum computers use the properties of quantum physics to store and compute data. A quantum computer also uses different units of memory called a quibit (short for a quantum bit). These computers calculate based on the probability of an object’s state before it’s measured instead of using 1s and 0s. This means it can theoretically process more data than traditional supercomputers. While still in their early stages, quantum computers are all the buzz and could potentially be the future of computational power. The term big data is used to describe extremely large amounts of data collected by businesses, companies, and institutions. But the amount of data isn’t what is important to these entities. It is the analysis and the insights that come from the data collected which help improve an organization’s business. There are several different examples of big data that we can see from the real world. The first is figuring out a consumer’s shopping habits. This can help strategists understand how to market to a regular customer and also how to market to potential consumers Streaming services are also using big data as a way to predict what shows could be profitable or even the next big hit. The data they find from how their subscribers stream shows help them make the decisions. If you’re a Netflix subscriber, you will receive a curated list of recommended movies that will very different than the next subscriber. They are taking into account your viewing preferences and history and uses this data to inform them of what to market to you. Big data is also used in TV advertisements, social media marketing, navigation applications, and more. The analysis of data helps companies predict what could be the next big thing. It informs them of the next steps for their business. If your business looking to store its data in a trusted data center, connect with us today. Virtual Reality and Augmented Reality have been around for quite some time now, but the two different technologies are still confusing. The main thing to remember when it comes to VR and AR is the way it alters your vision. VR is a computer simulation that makes users feel like they are somewhere else. Today, virtual reality is used in video games and 3D movies. Augmented reality on the other hand combines the real world you are currently and adds virtual elements. Using a phone or a tablet, these computer augmentations are projected as a layer on top of your real-world setting. Some examples of AR include Google Sky Map, Layar, and PokemonGO. It’s also to help users find what’s in and around them when they are visiting a new city. Some examples of VR include the Oculus Quest and the PlayStation VR. The resurgence of these two industries could be an indicator of where technology is headed. The virtual reality and augmented reality markets reached over 18.8 billion dollars in the US in 2020. Machine learning is an integral part of Artificial Intelligence, which is a simulation of human intelligence processed by computer systems and machines. These machines are programmed to think like humans and simulate our actions. Machine learning is an application within Artificial Intelligence that gives the system the capability of automatically learning and improving what it’s doing without that specific aspect being programmed. Machine learning technology is currently being applied to personal assistants like Google Home and Amazon Alexa. It’s also being used by various applications to help improve marketing and performance. As mentioned earlier, streaming services are using data to push certain content to their users. Streaming service recommendation engines are an example of machine learning that we may see more frequently. As the world gets closer to smart cities and self-driving cars, artificial intelligence and machine learning will continue to play a vital role in these innovations. The most exciting aspect of technology is the number of new ideas being applied every single day. And even before these ideas make it into mainstream consumption, these concepts can be quite intriguing. Keeping up with all of this information can be difficult. If you have any buzzwords, topics, or concepts that you want to know more about, leave us a message and we’ll include it in the next installment of Frequently Asked Questions in Technology.

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Phonons radiated by artificial atoms Science news with bad titles usually attract a lot of attention. A recent example is “The sound of an atom has been captured” 1]. Laypeople must know that atoms do not emit sound. Quantum acoustics study the propagation and the interaction of phonons, the analogues in sound to photons in light. An atom cannot emit phonons, but an artificial atom (a quantum dot or a superconducting qubit) can. Martin V. Gustafsson (Chalmers University of Technology, Göteborg, Sweden) and colleagues 2 have studied the free propagation of quantum information by phonons (specifically surface acoustic waves) strongly coupled to a superconducting qubit. In their experiments, phonons have a role similar to that of photons in quantum optics. A beautiful result in quantum acoustics that deserves our attention. Surface Acoustic Waves (SAWs), also referred to as Rayleigh waves, where theoretically predicted in 1885 by Lord Rayleigh 3. He showed that an elastic medium can support surface vibration modes that are wavelike. They are surface waves because their amplitudes decay exponentially with increasing distance into the solid from the surface and their energy is peaked within a depth of approximately one wavelength 4. By using piezoelectric materials the electric energy in electronic circuits can be transduced to mechanical energy in the form of SAWs. The so-called InterDigital Transducers (IDTs) are capable of converting acoustic waves to electrical signals and vice versa. Today, SAW devices are extensively used in commercial mobile phones instead of the traditional quartz crystals (based on bulk waves) because they can operate at higher frequency. From the quantum point of view, SAWs are made of phonons so they can be coupled to an artificial atom (a qubit) via piezoelectricity (see Fig. 1 for a circuit model). Thanks to SAWs a bidirectional communication with the qubit can be achieved. The great advantage is the low speed of sound, which allows the observation of the emission of phonons from the qubit in the time domain, i.e., to listen the sound of the artificial atom. Gustafsson and colleagues use an IDT with a GaAs substrate as piezoelectric material and two electrodes made of aluminium capped with palladium (see Fig. 2, left micrographs). The resulting SAWs propagate in the crystal at a speed of about 2900 m/s with a narrow bandwidth of ~1 MHz around an IDT carrier frequency of 4.8066 GHz. The IDT can both launch a SAW beam toward the artificial atom and pick up leftward-propagating SAW phonons produced by it. The device operates at a low temperature of about 20 mK to avoid the influence of spurious thermal phonons from the environment. The artificial atom (see Fig. 2, right micrographs) is a superconducting qubit of the transmon type. A transmon consists of a Superconducting Quantum Interference Device (SQUID) shunted by a large geometric capacitance so the Josephson inductance forms a resonant circuit. This nonlinear inductance gives rise to the anharmonic energy spectrum characteristic for an (artificial) atom, i.e., a set of discrete energy levels. The transmon is well suited for coupling to SAWs since the shunt capacitance (about 85 fF in Ref. ) can be designed to strongly couple to the IDT thanks to their common finger structure (see Fig. 2 and compare left and right micrographs). The transitions between the energy levels of the qubit results in the emission of SAW phonons and, conversely, a SAW beam can excite energy level transitions in the artificial atom. A careful reader may wonder how the authors have verified that the quantum information between the qubit and the IDT is propagated by phonons instead of photons (in fact, the IDT is controlled by using microwave pulses). To solve this question, Gustafsson and colleagues take advantage of the slow propagation of SAWs. After the excitation of the qubit to a high-energy level, its state decays emitting a signal than can be read by the IDT. Figure 3 illustrates that this signal takes about 40 ns to travel the distance of about 0.1 mm separating the IDT and the qubit (i.e., the speed of the signal is about 2500 m/s). Hence the signal is phononic. Another check developed by the authors is a careful comparison between the measurement of the signal, by using two-tone spectroscopy, and the numerical predictions of a theoretical model of the system. From the point of view of future applications, SAW phonons have several striking features with respect to photons. Their slow speed of propagation allows that the qubits be tuned much faster than SAWs traverse inter-qubit distances on a chip; this property enables new dynamic schemes for processing quanta. Additionally, the SAW phonons wavelength at a given frequency is shorter than the size of the qubit (since it depends on sound speed instead of light speed), so new techniques for trapping quanta into cavities can be developed. In my opinion, the future for this technology in quantum information processing is bright. In summary, the propagation of quantum information using quantum acoustics has been demonstrated by using SAW phonons. This achievement provides new tools for quantum information processing in regimes difficult (or even impossible) to reach using photons. - “The sound of an atom has been captured” Phys.Org, Sep 11, 2014. ↩ - Gustafsson M.V., A. F. Kockum, M. K. Ekstrom, G. Johansson & P. Delsing (2014). Propagating phonons coupled to an artificial atom, Science, DOI: http://dx.doi.org/10.1126/science.1257219 ↩ - Lord Rayleigh, “On Waves Propagated along the Plane Surface of an Elastic Solid,” Proc. London Math. Soc. 4–11 (1885). DOI: 10.1112/plms/s1-17.1.4 ↩ - A. A. Maradudin, G. I. Stegeman, “Surface Acoustic Waves,” in Surface Phonons, edited by W. Kress, F. W. de Wette, Springer, 1991, pp 5–35. DOI: 10.1007/978-3-642-75785-3_2 ↩ […] el tema, puedes leer mi post (en inglés) “Phonons radiated by artificial atoms,” Mapping Ignorance, 22 Sep 2014. El artículo técnico descrito es M. V. Gustafsson, A. F. Kockum, M. K. Ekstrom, G. […] I’m confused because I always thought that the definition of a qubit cannot be associated to a physical thing. What really means an “artificial atom”? Superconducting circuits are really artificial atoms because their resonance frequencies resembles those in atoms. […] Atomo baten soinua entzun dela iragartzen zuen berriak izan duen oihartzunaren aurrean, Francisco R. Villatoro atomo artifizial bat zer den azaltzera etorri zaigu. Eta, entzundako doinu hori fonoiez osatuta dagoela: Phonons radiated by artificial atoms. […] […] Ante la noticia que tuvo bastante eco de que se había escuchado el sonido de un átomo, Francisco R. Villatoro sale al paso y explica qué es realmente un átomo artificial y que ese sonido está constituido realmente por […]

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The State of Quantum The superiority of any computing technology zeroes down to its processing capabilities, and over the years the classical computer chip’s processing power has been pushed to limits by shrinking its components, in order to reduce the distance travelled by electric signals in between. The famous Moore’s Law (by Gordon Moore) states “the number of transistors on a microchip double about every two years, though the cost of computers is halved”, helping us draw context on why billions of dollars are being invested into making these chips smaller and smaller. Apple’s 5-nanometre processor is a good example of where we are, but we’re seemingly hitting a wall in terms of marginal increases in processing power for every additional billion dollars invested. Furthermore, these classical computers require longer periods to solve complex problems and sometimes this can even go up to 10,000 years. While we’re progressing towards smaller circuits and complex problems, we’ve reached the physical limits of materials and the threshold for classical laws of physics to apply, hence chip designers are going subatomic to solve this. The use of quantum physics in computing has shown some progress in terms of achieving better processing capabilities over supercomputers. What exactly is quantum computing? A quantum computer (QC) uses qubits (the fundamental unit of a QC) to store and process information. Unlike the classical bit which is either 0 or 1 at a time, a qubit can be both 0 and 1 at the same time (explained under ‘Superposition’) — a property which enables a QC to solve problems faster by evaluating solutions simultaneously. Fundamentally a QC derives its power from these 3 main principles of quantum particles: - Superposition: The qubit’s ability to be 1 & 0 at the same time, i.e. in the state of probabilities (’x%’ probability of being 1 and ‘y%’ probability of being 0). - Interference: The qubit can cross its own trajectory and interfere with the direction of its path. - Entanglement: Two qubits are entangled if changing the state of 1 qubit instantaneously changes the state of the other in a predictable way, despite the amount of distance between them. The number of computations a QC could make is 2^n, where ’n’ denotes the number of qubits used. Hence, with each additional qubit, a QC would attain an exponential increase in processing power, which would be much faster than what Moore’s law stated about doubling transistors. We must also bear in mind that QCs won’t replace our current classical computers (eg: PC, smartphones, etc.), rather they’d complement them in a particular area or application. What does a quantum computer look like? This is the IBM System One with a 127-qubit processor. To ensure longer coherence times (period of qubits being in a quantum state) and increase the accuracy of calculations (by reducing noise), QCs are equipped with superconductors made from elements such as Niobium and kept at 1/100th of a degree Celsius i.e., just above absolute zero, using super-fluids like liquid Helium. Where can quantum computers add value? With early use cases like optimization, simulation and encryption, QCs are capable of saving billions of dollars and years in time across industries, and these include: - Process optimization: QC can help with supply chain optimization and manufacturing process optimization, thereby cutting down costs and establishing an efficient way. Volkswagen is using QC to optimize its manufacturing process, and Daimler is working towards making better automotive batteries. - Drug simulation: QC can enable a significant reduction in R&D costs and time to market for a new drug. Riverlane & Astex Pharmaceuticals are working with Rigetti Computing to develop an integrated application for simulating molecular systems to enable drug discovery. - Cryptography: A powerful enough quantum computer can break the most secure encryption ever created in a matter of seconds, thus emphasizing the need for post-quantum encryption to secure future use-cases. QuintessenceLabs, an Australian company, has been working on Quantum Random Number Generator (QRNG) & Quantum Key Distribution (QKD) technologies — the foundation for quantum encryption and data security. - Other interesting use cases: IBM is working on improving weather forecasting, JP Morgan is exploring applications in financial modelling & options pricing, and Rigetti is improving machine learning. Who’s who in quantum computing: Source: Silicon Foundry Various companies have been working towards achieving better QC performance and use cases. Essentially, the ecosystem is comprised of the following sub-verticals: - Quantum hardware: Most challenging sub-vertical that requires millions of dollars in investments for building out the QC, with efforts from talented experts. - Quantum software: Building software solutions for horizontal (or) industry-specific applications like molecular simulation, error correction, algorithm testing, etc. - Quantum systems & firmware: Solving for hardware error and qubit instability arising from environmental disturbances and imperfect devices. - Quantum encryption & AI: Working on technologies like QRNG & QKD to develop quantum-based encryption chips (or) software. - Cloud computing: Providing direct access to emulators, simulators and quantum processors. Mostly offered by hardware players like IBM, Google, etc. - Full-stack: These are companies that offer end-to-end quantum computing solutions. They already have a built QCs in house and provide access to it via the cloud. Research shows that ~35% of QC revenues will be captured by QC software players and 26% by hardware players. Geographically the U.S.A. has seen the most success in quantum computing, but the Chinese are also catching up. In October 2021, researchers from the University of Science and Technology of China (USTC), said one of the quantum computing systems, Zuchongzhi 2.1, is 100x more powerful than Google’s 53-qubit Sycamore. The Govt. of India has shown its conviction through its National Mission on Quantum Technologies & Applications mission (NM-QTA) with an INR 8,000 Cr budget (to be deployed over a 5-year period). Top Indian universities including IIT Madras, IISc Bangalore, TIFR, and IISER Pune have been spearheading QC research. Other institutions like MeitY, ISRO, and the Indian Army have also taken initiatives in this space. Quantum Computing is still years away from actual commercialization since we’re still in the Noisy Intermediate-Scale Quantum (NISQ) era. The creation of a 10,000 qubit QC and enough error correction would end the ‘NISQ era’ and mark the beginning of the ‘Universal Quantum’ era wherein QCs would be capable of breaking the RSA encryption (the bedrock of the internet’s encryption). Hence, overcoming challenges like error correction (by reducing noise), de-coherence (increase time period of a qubit’s quantum state), and output observance (reducing risk of data corruption while retrieving output) will help us transition towards the ‘Universal Quantum’ era. - Hello quantum world! Google publishes landmark quantum supremacy claim - Quantum computing can help prevent the onslaught of the next pandemic - Quantum Hegemony? China’s Ambitions and the Challenge to U.S. Innovation Leadership - Quantum Computing for Business Leaders - Quantum Processor Market Takes Off: A New Industry Born

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The most fundamental level in the study of matter and energy is quantum physics. It tries to learn more about the characteristics and actions of the very elements that make up nature. The fundamental knowledge of materials, chemistry, biology, and astronomy now includes quantum insights. These findings have been a great source of innovation, leading to the development of gadgets like transistors and lasers as well as significant advancements in fields like quantum computing that were previously seen as entirely theoretical. The potential of quantum research to alter our understanding of gravity and its relationship to space and time is being investigated by physicists. Quantum mechanics is a branch of physics that defines the behavior of particles, including atoms, electrons, photons, and nearly all molecules and sub-molecules. It serves as the theoretical cornerstone for all branches of quantum physics: quantum information science, quantum technology, quantum field theory, and quantum chemistry. The behavior of matter and radiation on an atomic scale is frequently strange, and the implications of quantum theory are so complex that require a deep understanding. Fundamentally, radiation and matter are both made up of particles and waves. The progressive discovery by scientists of the particle-like characteristics of radiation and the wave-like characteristics of matter served as the catalyst for the creation of quantum mechanics. History of Quantum Mechanics According to the University of St. Andrews in Scotland, quantum mechanics has first proposed as a collection of contentious mathematical explanations for phenomena that the mathematics of classical mechanics were unable to explain. It began at the beginning of the 20th century, at the time Albert Einstein published his theory of relativity, a different revolution in physics that explains the motion of objects moving quickly. Quantum mechanics cannot be traced back to a single researcher. Instead, numerous scientists contributed to a foundation that, between the late 1800s and 1930, gradually acquired recognition and experimental proof. Planck and Quanta German theoretical physicist, Max Planck. He is commonly referred to as the father of quantum theory. In order to calculate the frequencies of light energy radiated from a heated object, Planck developed a new mathematical formula. It demonstrated how hot things would emit reddish frequencies. The frequencies of all visible colors would be emitted by hotter objects, giving them the appearance of glowing white. The most crucial prediction of Planck’s formula was that no ultraviolet frequencies would be released. Planck’s original theory was that hot objects could only emit energy in discrete “packets” or tiny units at the subatomic scale (a single quantum is called a quantum). A quantum’s energy content increased with frequency, according to Planck. Lower frequencies, like red light, have less energy than higher frequencies, such as those in white light. Bohr Model and Electron Orbitals The popularity of quantum theory was rising. However, it remained merely a mathematical justification for some odd observations. Niels Bohr (Danish physicist) was the first to explain why energy exists in distinct packets. He presented a brand-new theory concerning the atom’s structure. Prior to Bohr, scientists believed that an atom was composed of a positively charged nucleus with negatively charged electrons revolving around it. However, Bohr completely altered this theory. According to him, those electrons had to follow one of a number of predetermined pathways. These paths resembled the orbits of planets around the Sun, and they were known as electron orbitals to him. There is a specific energy level for each orbital. An electron “jumps” from one orbital to the next largest orbital when it takes in enough energy. Energy is released when an electron “falls” into the next lowest orbital. The energy differential between the two orbitals is exactly reflected in the amount of energy released. This is why energy doesn’t exist on a continuous scale; instead, it exists in discrete values known as “quanta.” Einstein and Photons Even before Bohr, the photoelectric effect problem was resolved with the aid of quantum theory. This is the finding that illuminating a metal surface can cause electrons to fly off the metal. A larger amplitude led to more electrons ejecting when metal was exposed to light. Moreover, electrons are ejected with greater energy in response to higher-frequency light. The renowned German physicist, Albert Einstein, had a theory. He used the quantum theory of Planck to explain light. He proposed the idea that light can occasionally act as discrete electromagnetic energy packets. He gave these bundles the name photons. In summary, Planck saw electromagnetic radiation coming from the heated objects’ electrons as the quantized energy. In contrast, the electrons in the metal received energy from Einstein’s photons. The electron would exit its orbital and completely leave the metal if the photon energy was high enough. In this way, Bohr’s electron orbitals gave quantum mechanics a theoretical justification. The following fundamental Ideas also contributed to laying the groundwork for quantum physics: This idea has been around since the early days of quantum research. According to how they were measured, light and matter had the characteristics of either particles or waves, as evidenced by the results of the tests that led to this conclusion. The double-slit experiment is the most famous example of this, in which particles like electrons are fired at a board with two slits cut into it; behind the board, a screen is placed that illuminates when an electron strikes it. Quantum physics and upcoming quantum technologies are based on entanglement. Entanglement is a phenomenon that manifests at extremely small, subatomic scales, just like other parts of quantum science. When two or more items are connected in a way that allows them to be thought of as a unified system, even when they are extremely far apart, this phenomenon takes place. This mathematical idea illustrates the trade-off between opposing viewpoints. This indicates that two attributes of an object, such as its position, and velocity, cannot be accurately understood at the same time in terms of physics. We will only be able to determine an electron’s speed to a certain degree if we properly measure its position. This refers to characterizing an object as a composite of several potential states existing simultaneously. In mathematical terms, superposition can be thought of as an equation that has several solutions. The Probabilistic Nature of Quantum Objects and Mathematics As quantum phenomena are probabilistic, maths is also required to represent them. For instance, it might not be possible to precisely pinpoint an electron’s position. Instead, it may be said to be in a variety of potential positions, each of which has a chance of containing an electron, such as within an orbital. Mathematics is crucial to the study of quantum physics because many of its ideas are difficult, if not impossible, for us to visualize. Equations are utilized to describe or predict quantum objects and occurrences that human imaginations are capable of. Where to start quantum mechanics? Start with Quantum Basics: The classical intuition that serves us well in the macroscopic world but is utterly useless in the quantum realm must be ignored and unplugged in order to comprehend it. Let’s start by removing the outer layers of our traditional intuition. Schrödinger’s Cat in a Box In this fictitious experiment, a cat is placed in a box containing equipment that, when it detects beta particles released by a radioactive source, discharges a toxic gas. It serves as one example of the way that quantum mechanics compels us to think. A particle exists simultaneously in every position up until it is measured, exactly like a cat that is both dead and alive. De Broglie Wave De Broglie waves, often known as matter waves, are any aspects of a material object’s behavior or attributes that change over time or space in accordance with the mathematical equations used to explain waves. The concept of matter waves with wavelengths inversely proportional to particle momentum was proposed by French scientist Louis de Broglie in 1924. He claimed that each particle has its own set of matter waves, each of which has a certain wavelength. In quantum mechanics, any particle’s wave function is a matter wave, whose shape can be calculated using the Schrödinger equation. As a result, the most significant aspect of quantum mechanics is matter waves. Wave function Encoded Particle Information Since the particle is a wave, its position in space is dispersed. The wavefunction, which is calculated in quantum mechanics using the Schrodinger equation, contains all of the information about particles. The probability distribution for position, momentum, spin or any other observable quantity can be described using particle wavefunctions. Heisenberg's Uncertainty Principle The uncertainty principle, which was developed by German physicist and Nobel laureate Werner Heisenberg in 1927, states that we cannot know a particle’s position and speed with perfect accuracy. The more precisely we can determine a particle’s position, the less we know about that particle’s speed, and vice versa. In general, the uncertainty principle can be applied to any complementary pair of dual physical values that cannot be determined with arbitrary precision. When first trying to understand the fundamentals of quantum mechanics, you may notice that your brain will explode at any moment. However, when you go more into the complexity and nuances of equations and observe how they apply in real life, the interest grows and reveals beauty at its most basic levels. Planets and moon we're going to in the next 30 years If you're a space nerd, we've got the perfect poster for you! Buy Online Solar System Planet Posters at Abhiexo. Check our category page for more...

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Since the discovery of quantum physics, every development of particular significance has been called a "quantum leap". In computer technology, the invention of the quantum computer literally represents such a leap. But what makes it so special? What is the significance of this technological novelty? In order to clarify these and other questions, //next spoke with Dr. Roman Horsky, mathematician at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. For Roman Horsky, quantum computing is a key technology that will be important for many research questions in the coming years. He himself has been working on it since 2019. From his point of view, the topic has clearly picked up speed since 2020: "You can notice that movement is coming into the previously rather theoretical concept of quantum computing. For us at Fraunhofer, the technology is of great strategic importance. In June, the first IBM quantum computer on German soil was inaugurated; it is operated by Fraunhofer under local data protection law. This enables us to implement a number of research projects from different disciplines." If you want to describe how a quantum computer works, you quickly get into the complicated terminology of higher physics: there is talk of two-dimensional complex spaces, superposition states and interference. Roman Horsky tries to give a simple explanation: "Quantum computers - like classical computers - are first of all machines that perform calculations. Unlike classical computers, however, the minimum memory units are so small that the laws of quantum mechanics apply to them. This results in fundamental differences. The basic unit of a classical computer, the bit, can assume exactly two states: It is charged or not charged. With the qubit, the basic unit of a quantum computer, the situation is less intuitive. Here, too, there is the possibility of a reduction to two basic states, but qubits as a memory unit represent a superposition of the basic states and are entangled with each other. This makes parallel calculations possible. The classical computer, on the other hand, has to carry out its calculations one after the other." The number and interconnection of the qubits is decisive for the performance of a quantum computer, explains the financial mathematician. With the number of qubits, its computing power increases exponentially. But this in turn is currently also the challenge: the more qubits there are in a quantum computer, the more unstable the system becomes, says Horsky. And he continues: "Currently, about 50 to 70 qubits are realised in a universal quantum computer. This does not yet result in computing power that far exceeds that of classical computers. If the number of qubits is increased further, the system will quickly become unstable at its current state. Achieving durability in computing results and processes is the greatest challenge in quantum computing. The entire technology is extraordinarily sensitive to all kinds of external influences. That's what makes it so difficult to implement in practice." The "computing machine", which was built in Ehningen, Baden-Württemberg, is a joint project of many participants. "Together with IBM, we provide a technology in the Fraunhofer network that can also be used beyond our institutes by industry, research and politics," Horsky explains the principle. In a ticket model, external interested parties can "book in" on the quantum computer in Ehningen - currently for a low five-figure monthly fee. It is not yet possible for a quantum computer to run in routine operation, Dr Roman Horsky continues. "You have to think of it more like a laboratory unit. The whole thing has a strongly experimental character, and many things don't work smoothly yet." He also says that the capacities of these computers are still too limited to be able to map complex models. "Nevertheless, a number of very exciting research questions can be mapped, and we are just starting with various promising research projects from both the energy and the finance sector," says the Fraunhofer ITWM employee happily. Roman Horsky is particularly interested in questions from the financial and insurance environment. For ten years now, the graduate physicist, who completed his doctorate in financial mathematics, has been working in applications research at Fraunhofer ITWM. Together with a total of about 500 colleagues - a large part of them mathematicians, but also scientists from many other disciplines - he works on projects in the field of techno and business mathematics. "Even if it sounds theoretical, our work always has an application focus. We often work with partners in business and industry." He himself is based in the department of financial and actuarial mathematics and, among other things, looks after the possible fields of application of the quantum computer in this area. With "our own" quantum computer on German soil, it is possible to explore the potential of the technology in more detail and compare it with classical methods. "The exciting thing is that I can combine my skills from the fields of physics and financial mathematics in quantum computer technology. That fascinates me a lot," Horsky explains. The quantum computer requires a different approach to mathematical problems, he says. "The machine has specific characteristics and properties that entail a different form of ideal use. Accordingly, one needs other formulations of problems," Horsky explains. In fact, the machine, i.e. the underlying technology, defines the type of question. Horsky's department at the Fraunhofer ITWM is currently working on three specific research projects: For the energy sector, it is about optimising the use of power plants; in the financial sector, it is about simulating capital market models; and in the insurance sector, the quantum computer is supposed to help optimise the management of fixed assets by including stochastic factors. These and similar research projects will show how efficiently and stably the quantum computer works in application questions and what long-term perspectives can be derived for the technology. But no matter how successful the use of the quantum computer in the Fraunhofer network will be: These machines are still a long way from being a series model. Horsky states: "It is currently quite inconceivable that at some point every household will have a quantum computer. This kind of technology will always remain very specific. Maybe one day, like in the heyday of mainframes, it will be the case that large industrial companies will afford quantum-based computing systems." At this point in time, however, this too is still purely a vision of the future. The high vulnerability of qubits requires extreme shielding of the systems. Only when this succeeds and scalability is given, one can think about an everyday use, says Dr. Horsky. The website of the Fraunhofer Gesellschaft says: "There are still considerable hurdles to the operation of a quantum computer. The highest premise is to shield the fragile quanta against all environmental influences. They need a temperature lower than in space, must be cooled down to almost absolute zero of about minus 273 degrees, only work under vacuum conditions, must be electromagnetically shielded - only then is there a chance of useful calculations. Errors can occur due to external influences such as vibrations as well as during the manipulation and reading of qubits with the help of electromagnetic waves." Roman Horsky goes into more detail: "There are different technological approaches to quantum computing. Depending on the system, the stability increases, but at the expense of the range of applications." A distinction is made, he says, between gate-based quantum computers and so-called quantum annealers. In the latter, the qubits are arranged in a predetermined structure, which makes the system more stable. However, this structure only allows specific problems and calculations. It cannot be used freely. For Dr. Roman Horsky, quantum computing is a future topic that is gradually growing out of its infancy: "There is a spirit of optimism," says the scientist, even though it is still a highly complex topic. The quantum computer is not simply a working tool, the handling of this technology is rather an interdisciplinary topic: "You need know-how in mathematics, physics and engineering." Roman Horsky, at any rate, is looking forward to working with the complicated and sensitive machine in the future. "And I'm looking forward to seeing the computer up close in Ehningen soon. So far, I too only know quantum computers from illustrations in books." Text: Sabine Haas

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Quantum engineers from UNSW Sydney have created artificial atoms in silicon chips that offer improved stability for quantum computing. In a paper published today in Nature Communications, UNSW quantum computing researchers describe how they created artificial atoms in a silicon ‘quantum dot’, a tiny space in a quantum circuit where electrons are used as qubits (or quantum bits), the basic units of quantum information. Scientia Professor Andrew Dzurak explains that unlike a real atom, an artificial atom has no nucleus, but it still has shells of electrons whizzing around the centre of the device, rather than around the atom’s nucleus “The idea of creating artificial atoms using electrons is not new, in fact it was first proposed theoretically in the 1930s and then experimentally demonstrated in the 1990s – although not in silicon. We first made a rudimentary version of it in silicon back in 2013,” says Professor Dzurak, who is an ARC Laureate Fellow and is also director of the Australian National Fabrication Facility at UNSW, where the quantum dot device was manufactured. “But what really excites us about our latest research is that artificial atoms with a higher number of electrons turn out to be much more robust qubits than previously thought possible, meaning they can be reliably used for calculations in quantum computers. This is significant because qubits based on just one electron can be very unreliable.” Professor Dzurak likens the different types of artificial atoms his team has created to a kind of periodic table for quantum bits, which he says is apt given that 2019 – when this ground-breaking work was carried out – was the International Year of the Periodic Table. “If you think back to your high school science class, you may remember a dusty chart hanging on the wall that listed all the known elements in the order of how many electrons they had, starting with Hydrogen with one electron, Helium with two, Lithium with three and so on. “You may even remember that as each atom gets heavier, with more and more electrons, they organise into different levels of orbit, known as ‘shells’. “It turns out that when we create artificial atoms in our quantum circuits, they also have well organised and predictable shells of electrons, just like natural atoms in the periodic table do.” Connect the dots Professor Dzurak and his team from UNSW’s School of Electrical Engineering – including PhD student Ross Leon who is also lead author in the research, and Dr Andre Saraiva – configured a quantum device in silicon to test the stability of electrons in artificial atoms. They applied a voltage to the silicon via a metal surface ‘gate’ electrode to attract spare electrons from the silicon to form the quantum dot, an infinitesimally small space of only around 10 nanometres in diameter. “As we slowly increased the voltage, we would draw in new electrons, one after another, to form an artificial atom in our quantum dot,” says Dr Saraiva, who led the theoretical analysis of the results. “In a real atom, you have a positive charge in the middle, being the nucleus, and then the negatively charged electrons are held around it in three dimensional orbits. In our case, rather than the positive nucleus, the positive charge comes from the gate electrode which is separated from the silicon by an insulating barrier of silicon oxide, and then the electrons are suspended underneath it, each orbiting around the centre of the quantum dot. But rather than forming a sphere, they are arranged flat, in a disc.” Mr Leon, who ran the experiments, says the researchers were interested in what happened when an extra electron began to populate a new outer shell. In the periodic table, the elements with just one electron in their outer shells include Hydrogen and the metals Lithium, Sodium and Potassium. “When we create the equivalent of Hydrogen, Lithium and Sodium in the quantum dot, we are basically able to use that lone electron on the outer shell as a qubit,” Ross says. “Up until now, imperfections in silicon devices at the atomic level have disrupted the way qubits behave, leading to unreliable operation and errors. But it seems that the extra electrons in the inner shells act like a ‘primer’ on the imperfect surface of the quantum dot, smoothing things out and giving stability to the electron in the outer shell.” Watch the spin Achieving stability and control of electrons is a crucial step towards silicon-based quantum computers becoming a reality. Where a classical computer uses ‘bits’ of information represented by either a 0 or a 1, the qubits in a quantum computer can store values of 0 and 1 simultaneously. This enables a quantum computer to carry out calculations in parallel, rather than one after another as a conventional computer would. The data processing power of a quantum computer then increases exponentially with the number of qubits it has available. It is the spin of an electron that we use to encode the value of the qubit, explains Professor Dzurak. “Spin is a quantum mechanical property. An electron acts like a tiny magnet and depending on which way it spins its north pole can either point up or down, corresponding to a 1 or a 0. “When the electrons in either a real atom or our artificial atoms form a complete shell, they align their poles in opposite directions so that the total spin of the system is zero, making them useless as a qubit. But when we add one more electron to start a new shell, this extra electron has a spin that we can now use as a qubit again. “Our new work shows that we can control the spin of electrons in the outer shells of these artificial atoms to give us reliable and stable qubits. This is really important because it means we can now work with much less fragile qubits. One electron is a very fragile thing. However an artificial atom with 5 electrons, or 13 electrons, is much more robust.” The silicon advantage Professor Dzurak’s group was the first in the world to demonstrate quantum logic between two qubits in silicon devices in 2015, and has also published a design for a full-scale quantum computer chip architecture based on CMOS technology, which is the same technology used to manufacture all modern-day computer chips. “By using silicon CMOS technology we can significantly reduce the development time of quantum computers with the millions of qubits that will be needed to solve problems of global significance, such as the design of new medicines, or new chemical catalysts to reduce energy consumption”, says Professor Dzurak. In a continuation of this latest breakthrough, the group will explore how the rules of chemical bonding apply to these new artificial atoms, to create ‘artificial molecules’. These will be used to create improved multi-qubit logic gates needed for the realisation of a large-scale silicon quantum computer. Research collaborators and funding Other authors on the paper include Drs. Henry Yang, Jason Hwang, Tuomo Tanttu, Wister Huang, Kok-Wai Chan and Fay Hudson, all from Professor Dzurak’s group, as well as long-time collaborators Dr Arne Laucht and Professor Andrea Morello from UNSW. Dr Kuan-Yen from Aalto University in Finland assisted the team, while Professor Kohei Itoh from Keio University in Japan provided enriched silicon-28 wafers from which the devices were made. The qubit devices incorporated nano-scale magnets to help enable qubit operation, and these were designed with support from a team led by Professor Michel Pioro-Ladrière at Université de Sherbrooke in Canada, including his PhD student Julien Camirand Lemyre. The project was funded with support from the Australian Research Council, the US Army Research Office, Silicon Quantum Computing Proprietary Limited, and the Australian National Fabrication Facility, with Drs Saraiva and Yang acknowledging support from Silicon Quantum Computing. The Canadian team received support from the Canada First Research Excellence Fund and the National Science Engineering Research Council of Canada.

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After a long day in the sun, you may come back with burning sensations and red skin. A trip to the beach is typically associated with sunscreen or sunburn. However, it might have more to do with it than you think. From tanning booths to cancer treatment, ultraviolet light plays an integral role in our lives. If you’re getting flashbacks to your grade school science class, don’t fret. Continue reading for everything you need to know about ultraviolet radiation and how it affects you. What is Ultraviolet Light?: Complete Explanation Ultraviolet (UV) light is a type of electromagnetic radiation that sits just above the spectrum of light that we can see with unaided eyes. It makes up a portion of the electromagnetic spectrum, with its wave frequencies measuring less than X-rays and blending into the violet range of visible light. The electromagnetic spectrum is typically measured in three metrics, depending on which is easiest to read. These include wavelength, frequency, and energy. Ultraviolet is typically measured in wavelength (meters) or frequency (hertz). While there’s no hard line to define the boundaries of UV, it typically ranges from 180 to 400 nanometers (nm). Ultraviolet light has a unique energy that causes them to break chemical bonds. This results in a variety of benefits, such as purifying water systems. However, overexposure can lead to damaging consequences such as sunburn. This portion of the electromagnetic spectrum comes from various natural and artificial sources. Its prevalence in new stars allows scientists to explore the universe as it formed. Furthermore, humans have used UV in practical applications following its discovery in the 1800s. Ultraviolet Light: An Exact Definition The U.S. Navy defines ultraviolet light as “part of the electromagnetic radiation spectrum,” where “electromagnetic radiation is made up of oscillating electric and magnetic fields which are propagated in free space and matter.” The military agency defines its wavelengths, which range “from approximately 180 nanometers (nm) to 400 nanometers.” The ultraviolet spectrum varies based on its physiologic effects. The Navy breaks down UV radiation into critical ranges: - UVA (near UV) 315 – 400nm - UVB (middle UV) 280 – 315nm - UVC (far UV) 180 – 280nm Where Does Ultraviolet Light Come From? Our sun is the largest producer of ultraviolet radiation within the range of our influence. Far UV is the most dangerous, but the atmosphere absorbs nearly all its wavelengths. It also absorbs about 95% of middle UV, which is the cause of sunburn. This is why it’s okay to be in the sun while not overexposing yourself. In astronomy, scientists use the electromagnetic spectrum to evaluate the age and characteristics of star clusters. NASA researchers have discovered that ultraviolet images of galaxies reveal star nurseries, with young stars producing energy much more powerful than our own sun. Evaluating the universe in the ultraviolet allows us to discover how stars form. In addition to the ultraviolet light from celestial bodies, energy waves of this type have been found in electric discharges. This occurs during the breakdown of gas and finds use in specialized lamps. How Do You Create Ultraviolet Light? One way to artificially produce ultraviolet light is with an electric discharge passing through a gas. Mercury vapor is the most used option in practical applications due to its consistency. The mercury vapor absorbs the UV radiation from the electric discharge and emits visible light as an exhaust. Who Discovered Ultraviolet Light? Ultraviolet rays were discovered in 1801 by German chemist Johann Wilhelm Ritter while searching for the polarities in the forces of nature. While experimenting in the opposite direction of William Herschel’s “heat rays,” Ritter discovered that silver chloride paper reacted to invisible frequencies faster than it did to violet. This experiment proved the existence of wavelengths beyond the visible light spectrum, with Ritter naming the wavelength deoxidizing rays for their ability to alter the chemical balance of objects. The name was dropped near the end of the century in favor of the more accurately descriptive name ultraviolet. What Are the Applications of Ultraviolet Light? While too much exposure to ultraviolet can lead to sunburn, people often use the frequency in moderation to tan skin. Tanning is most effective when subjected to UV wavelengths of 280 – 315nm. This can occur naturally through sun exposure or artificially with tanning lights. When specific objects are exposed to ultraviolet radiation, they can absorb it. UV waves that are absorbed cause the electrons within the object to increase in energy. As the electrons return to their original energy level, they emit the energy as absorbed light. This phenomenon, called fluorescence, results in some objects glowing or appearing brighter. We often see fluorescence used in safety equipment, where visibility is critical. In addition to its effects on the skin, UVB causes the body to produce vitamin D. This vitamin helps create serotonin, which is associated with sensations of happiness and joy. The World Health Organization recommends 5-15 minutes of direct sunlight on the skin for high vitamin D levels. Applications of Ultraviolet Light In the Real World Psoralen Ultraviolet Light Treatment Cancer Research UK is a nonprofit organization that’s exploring the use of ultraviolet light to treat skin conditions. Physicians use specific medicinal applications to increase the sensitivity of their patient’s skin. A UV light is directed at the condition, which slows down the growth of problem cells. Psoralen ultraviolet light treatment (PUVA) is used to treat lymphoma, psoriasis, and eczema, among other conditions. Similar to how some objects glow fluorescent light when exposed to UV radiation, so do the atmospheric gases at the earth’s magnetic poles. The Aurora Borealis (also known as the Northern Lights) occur around the Arctic and Antarctic when ultraviolet radiation concentrates in those magnetic fields. The radiation bounces off gas particles (usually oxygen atoms), which get excited and raise energy. As the particles return to their natural level, they emit brilliant green (and sometimes red or blue) light. Hubble Space Telescope As part of the Great Observatories project in the 1990s, NASA launched the Hubble Space Telescope to observe the universe in visible and ultraviolet light spectrums. Equipped with cameras, spectrographs, and interferometers, the space observatory analyzes the beginnings of the universe. The Hubble Space Telescope focuses on distant points of light to explore how stars form. Ultraviolet Light: Further Reading With technology rapidly improving, it’s important to know how electromagnetic radiation like ultraviolet light applies. Both naturally and artificially occurring, UV positively and negatively affects us alongside the rest of the spectrum. To learn more about electromagnetic uses, check out the articles below. - The James Webb Space Telescope: Complete History, Specs, and More – NASA’s latest telescope can observe the universe in infrared. Here’s what you need to know about it. - Top 10 Largest Space Telescopes in Orbit – James Webb is making headlines with its stellar imagery. What other telescopes is NASA using? - What’s the Next Big Thing in Technology? 10 Predictions From the Experts – From spaceflight to quantum computing, these 10 predictions could shape the future of technology. Bluetooth vs Infrared: What’s the Difference? – take a look at the most prominent wireless technologies we use to communicate without daily gadgets.

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In the 1960s, researchers at the science lab of the Ford Motor Company developed the superconducting quantum interference device, also known as a “SQUID.” It was the first usable sensor to take advantage of a quantum mechanical property—in this case, superconductivity. That made the SQUID one of the first generation of quantum sensors: devices that use a quantum system, quantum properties or quantum phenomena to make a physical measurement. Physicists took the idea and ran with it, coming up with new types of sensors they continue to use and improve today. SQUIDs have played a key role in the development of ultrasensitive electric and magnetic measurement systems and are still in use. For example, they amplify the detector signals for the Super Cryogenic Dark Matter Search. “As particle physicists, we’ve been using quantum sensing techniques for decades,” says SuperCDMS physicist Lauren Hsu of the US Department of Energy’s Fermi National Accelerator Laboratory. But SQUIDs are no longer the only quantum sensors around. One important recent development in quantum sensing is known as quantum squeezing—a way to circumvent quantum limitations that even quantum sensors have faced in the past. “The only way to do better is to start beating quantum mechanics.” The first quantum sensors Ford’s SQUIDs, which needed to be cooled to a few degrees above absolute zero, used superconducting loops to measure minuscule magnetic fields. SQUIDs didn’t turn out to be of much use in an automobile. But not all Ford researchers were beholden to expectations that their creations would wind up in a car. “This shows you how different the world was back in the 1960s,” says Kent Irwin, a physicist at Stanford University and SLAC National Accelerator Laboratory. “These days Ford is not doing basic physics.” A few decades later, while in graduate school, Irwin built on the idea of the Ford Company’s SQUID to develop a new quantum sensor: the first practical superconducting transition-edge sensor. Irwin took advantage of the fact that superconducting material loses its superconductivity when it heats up, regaining its resistance at a precise temperature. By keeping a superconducting material as close as possible to this temperature limit, he could create a sensor that would undergo a significant change at the introduction of even a small amount of energy. Just a single photon hitting one of Irwin’s transition-edge sensors would cause it to shift to a different state. The transition-edge sensor is well-known and has been adopted widely in X-ray astronomy, dark matter detection, and measurements of the cosmic microwave background radiation. “It’s very much old-school quantum 1.0,” Irwin says. Quantum sensing for gravitational waves A new generation of quantum sensors goes beyond quantum 1.0. Some of today’s sensors make use of more than just superconductivity: They’ve managed to use the Heisenberg uncertainty principle—usually thought of as a limitation to how well physicists can make measurements—to their advantage. The Heisenberg uncertainty principle puts a cap on how accurately you can measure a pair of related properties. For example, the more you know about the position of a particle, the less you can know about its momentum. Quantum squeezing takes advantage of these relationships by purposefully tipping the balance: moving all the uncertainty of a measurement to one side or the other. Gravitational-wave detectors, such as LIGO in the US, and Virgo and GEO in Europe, have used quantum squeezing to great effect. In 2015, LIGO—the Laser-Interferometer Gravitational-wave Observatory—detected the first gravitational waves, undulations of spacetime first predicted by Albert Einstein. Once it got going, it was picking up new signs of gravitational-wave events every month. LIGO detects gravitational waves using an interferometer, an L-shaped device in which two beams of light are set up to bounce off identical mirrors and return. Under normal conditions, the beams will arrive at the same time and cancel one another out. No signal will hit the detector. But if a subtle outside force knocks them out of sync with one another, they won’t cancel each other out, and photons will hit the detector. If a gravitational wave passes through the two beams, it will hit one and then the other, interrupting their pattern. LIGO’s measurements are limited by the quantum properties of the photons that make up their beams of light. At the quantum level, photons are affected by fluctuations, virtual particles popping in and out of existence in the vacuum. Those fluctuations could cause a false signal in the detector. How could LIGO researchers tell the difference? “LIGO is using the most powerful lasers they can build, and the best mirrors they can build, and their back is against the wall,” Irwin says. “The only way to do better is to start beating quantum mechanics.” Scientists at LIGO and other gravitational-wave detectors looked to quantum squeezing to help them with their virtual photon problem. To generate squeezed light, researchers used a technology called an optical parametric oscillator, within which an input wave of laser light is converted to two output waves with smaller frequencies. This process entangles pairs of photons, and the resultant correlations of their properties serve to reduce uncertainty in one aspect of the arriving photons, allowing LIGO scientists to better measure another aspect, helping them sort the signal from the noise. Since April 2019, when LIGO began running with the quantum squeezers, the observatory has been able to detect new gravitational-wave signals—signs of collisions between massive objects such as black holes and neutron stars—more frequently, going from about one detection per month to about one per week. Quantum sensing for dark matter detection Quantum squeezing has also recently found an application in the search for dark matter. Dark matter has never been observed directly, but clues in cosmology point to it making up approximately 85% of the matter in the universe. There are several different theories that describe what a dark matter particle could be. “The mass can be anywhere from a billionth the size of an electron up to a supermassive black hole,” Hsu says. “There are over 100 orders of magnitude that it can span.” The most promising small dark matter candidates are axions. In the presence of a strong magnetic field, axions occasionally convert into photons, which can then be detected by an experiment’s sensors. Like someone trying to find a radio station on a road trip in the middle of nowhere, they scan for a while at one frequency, to see if they detect a signal. If not, they turn the dial a little and try the next size up. It takes time to listen to each “station” once the detector is tuned to a particular possible axion signal; the more noise there is, the longer it takes to determine whether there might be a signal at all. The HAYSTAC experiment—for Haloscope at Yale Sensitive to Axion Cold Dark Matter—searches for axions by measuring two different components of electromagnetic field oscillations. Like LIGO, it is limited by the uncertainty principle; HAYSTAC researchers are unable to precisely measure both oscillations at once. But they didn’t need to. Like LIGO scientists, HAYSTAC scientists realized that if they could squeeze all the accuracy into just one side of the equation, it would improve the speed of their search. In early 2021, researchers announced that at HAYSTAC, they had also succeeded at using quantum squeezing to reduce noise levels in their experiment. Multiple groups have demonstrated promising new applications of superconducting circuit technology for axion detection. The “RF quantum upconverter” uses devices similar to Ford’s SQUIDs to evade the Heisenberg uncertainty principle in dark-matter searches at frequencies below HAYSTAC’s searches. Another uses a technology borrowed from quantum computing—qubits—as a sensor to evade Heisenberg’s limits at frequencies higher than HAYSTAC. Although neither technology has been used in dark matter searches yet, scientists believe that they could speed searches up by several orders of magnitude. At the current rate, it will still take axion experiments thousands of years to scan through every possible axion “station.” They may get lucky and find what they’re looking for early in the search, but it’s more likely that they’ll still need to find other ways to speed up their progress, perhaps with advances in quantum sensing, says Daniel Bowring, a Fermilab physicist who is involved in another axion search, the Axion Dark Matter Experiment. “It’s going to take a lot of people with really good imaginations,” Bowring says.

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Semiconductors are drivers of modern electronics, and they are the main enablers of our communications, computing, energy, transport, IoT systems and many more. Almost each and every device we have around us has a semiconductor in it, so no one can overestimate their importance in the world of technology. Today we’re trying to break down the notion of semiconductors, discover what’s inside this vital element and what trends are driving its development today. A semiconductor as the name implies is a material that has electrical behavior between conductors and insulation. Conductors are substances that easily transmit electricity, while insulators poorly transmit electricity. The semiconductor industry uses silicon as its primary material. Silicon is a good conductor, but it does not have the necessary characteristics to make a useful transistor. To change this, manufacturers add impurities to the silicon crystal structure. Impurities are atoms that do not belong to the regular arrangement of the crystal lattice. By adding these impurities, manufacturers can control how easily the electrons and holes move through the silicon. Silicon is the basis for all modern electronic devices. Transistor technology was first developed using germanium, a semiconductor with similar properties to silicon. Germanium is still used today, but silicon is much easier to work with. Because of this, silicon is still the dominant semiconductor material. Semiconductors are classified based on whether they are intrinsic or extrinsic. Intrinsic means that there are no impurities present in the material. Extrinsic means that the material requires doping to become conductive and therefore is considered a semiconductor. Intrinsic semiconductors have no additional doping elements added to them. These materials do not need to be externally charged before they conduct electricity. Intrinsic semiconducting materials are often referred to as bulk materials. Examples of intrinsic semiconductors are silicon (Si) and germanium (Ge). Extrinsic semiconductors are those that require doping to make them conductive. An example of an extrinsic semiconductor would be gallium arsenide, which is commonly used in transistors. Here, arsenic atoms have been added to the crystal structure of gallium to create positive charges called acceptor states. These states act as electron traps, causing the semiconductor to become electrically conductive. The IT industry cannot be separated from the development of the semiconductor industry. Semiconductors examples are transistors, MOSFETs, ICs, and diodes. One of the semiconductor materials commonly used in a digital device (logic-based circuit) technology development is a transistor. The invention of the transistor in 1947 helped in the development of second-generation computers into smaller, faster, more reliable, and more energy efficient than their predecessors. It was the era that transistors began their massive deployment which was started by Shockley until the birth of Fairchild Semiconductor which is considered as a pioneer in IC and transistor manufacturers. In the early 1960s, successful second-generation computers began to emerge in business, universities, and in government. These second-generation computers are computers that use full transistors. From here was born the next generation of computers that use hardware-based LSI, VLSI, ULSI to supercomputers. The birth of computer networking technology as well as the Internet, which is also supported by semiconductor-based devices, brought IT technology into the modern state as we know it today. Semiconductor has revolutionized electronic hardware, especially since the invention of the transistor. Semiconductors make hardware more compact and have better computing-related capabilities. The effect is that electronic components are now easier to obtain at affordable prices in the marketplace. This makes it easy for new developers to conduct research and innovation. LANARS provides hardware development services for creating new products and businesses, as well as for improving existing ones. The semiconductor, commonly known as the chipset, is the most important component. Despite their small size, semiconductor chips are the brains of an electronic system. In digital devices, the presence of semiconductors is needed to increase the speed of digital signal processing, including memory for data storage. As we are now in the industrial era 4.0, the need for semiconductor chips continues to grow. The semiconductor industry is also considered the lifeblood that is essential in accelerating digital transformation. The development of computers, the telecommunication industry, automotive equipment, especially electric vehicles (EVs), as well as digitalization in many sectors require the readiness of the semiconductor industry to prepare the required resources. In the midst of increasing demand for semiconductors, the global COVID-19 pandemic in 2020 hit almost the entire industry with a lockdown policy. This also has an impact on the supply of semiconductors, resulting in reduced supply, which has an impact on other industries. The affected industries include computers, Smart-TVs, smartphones, tablets, game consoles, and various electronic gadgets to the automotive industry. On the other hand, the COVID-19 pandemic has also increased the need for computers and gadgets in line with the school-from-home or work-from-home policies. This condition causes the semiconductor price trend to rise from the 2020 period to the present time. The implication results in 2021 the major players of semiconductor chipsets such as TSMC actually reap profits caused by the shortage of global chipset supply. According to a report from research firm TrendForce, if the top 10 chipset manufacturers combined, they will get a total revenue of US$127.4 billion in 2021. This figure is an increase of 48% compared to the previous year. As for 2022 itself, as reported by Deloitte, some observers say that semiconductor sales are expected to grow back by 10%, and could exceed US$ 600 billion for the first time in 2022. In the future, semiconductor trends will continue to be needed by various industries, although there is economic uncertainty is predicted, chipset availability is also expected to recover in 2023. Moore's Law predicts that the number of transistors in integrated circuits (IC) will double every year, is used as a reference by the semiconductor industry to set their research and development targets. This is evidenced by the birth of microprocessor capabilities that are increasing every year. But even Moore's law will eventually meet an impenetrable limit, increasing computer performance by adding transistors has so far been done by reducing the size of the transistor so that it can fit more in the same area. A few years ago, physicist Michio Kaku noted that there was a point where the silicon material used to make the transistor — or any substitute for it — could not be reduced any further. Several studies have initiated the use of other materials for the development of semiconductors. Third-generation semiconductor materials, such as gallium nitride (GaN) and silicon carbide (SiC), promise high-temperature resistance, high breakdown voltage, high frequency, high power, and high radiation resistance. However, for a long time, the use of these materials was limited to a narrow range of fields due to their complex processing methods and high cost. In recent years, breakthroughs in material growth and device fabrication have helped reduce the cost of third-generation semiconductor materials, enabling a wider range of applications. For example, SiC-based devices used for car inverters and GaN-based fast chargers appeared on the market. Semiconductor technology trends that have also been widely discussed to improve chip capabilities include parallel computing, quantum computing, to protein computers that work with DNA. Semiconductor is a material that has electrical properties between conductors and insulators. Semiconductors bring drastic changes in the technological development of mankind. From Shockley and Fairchild who make transistors to large manufacturers of chipset makers to giants like Intel that use semiconductors to create technology that plays a very important role in the development of computers, gadgets, household appliances, automation, telecommunications, and so on. The technological trend proclaimed by Moore’s Law has already occurred, and it is predicted that the number of transistor densities in a wafer will also be achieved. Therefore, there are various developments carried out to maximize semiconductors such as the use of third-generation materials, quantum computing, etc. semiconductor trends will continue to be needed by various industries, although economic uncertainty is predicted, chipset or semiconductors availability is also expected to recover in 2023.

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Researchers at the Paul Scherrer Institute PSI have put forward a detailed plan of how faster and better defined quantum bits - qubits - can be created. The central elements are magnetic atoms from the class of so-called rare-earth metals, which would be selectively implanted into the crystal lattice of a material. Each of these atoms represents one qubit. The researchers have demonstrated how these qubits can be activated, entangled, used as memory bits, and read out. They have now published their design concept and supporting calculations in the journal PRX Quantum. On the way to quantum computers, an initial requirement is to create so-called quantum bits or "qubits": memory bits that can, unlike classical bits, take on not only the binary values of zero and one, but also any arbitrary combination of these states. "With this, an entirely new kind of computation and data processing becomes possible, which for specific applications means an enormous acceleration of computing power," explains PSI researcher Manuel Grimm, first author of a new paper on the topic of qubits. The authors describe how logical bits and basic computer operations on them can be realised in a magnetic solid: qubits would reside on individual atoms from the class of rare-earth elements, built into the crystal lattice of a host material. On the basis of quantum physics, the authors calculate that the nuclear spin of the rare-earth atoms would be suitable for use as an information carrier, that is, a qubit. They further propose that targeted laser pulses could momentarily transfer the information to the atom's electrons and thus activate the qubits, whereby their information becomes visible to surrounding atoms. Two such activated qubits communicate with each other and thus can be "entangled." Entanglement is a special property of quantum systems of multiple particles or qubits that is essential for quantum computers: The result of measuring one qubit directly depends on the measurement results of other qubits, and vice versa. Faster means less error-prone The researchers demonstrate how these qubits can be used to produce logic gates, most notably the "controlled NOT gate" (CNOT gate). Logic gates are the basic building blocks that also classical computers use to perform calculations. If sufficiently many such CNOT gates as well as single-qubit gates are combined, every conceivable computational operation becomes possible. They thus form the basis for quantum computers. This paper is not the first to propose quantum-based logic gates. "Our method of activating and entangling the qubits, however, has a decisive advantage over previous comparable proposals: It is at least ten times faster," says Grimm. The advantage, though, is not only the speed with which a quantum computer based on this concept could calculate; above all, it addresses the system's susceptibility to errors. "Qubits are not very stable. If the entanglement processes are too slow, there is a greater probability that some of the qubits will lose their information in the meantime," Grimm explains. Ultimately, what the PSI researchers have discovered is a way of making this type of quantum computer not only at least ten times as fast as comparable systems, but also less error-prone by the same factor. Text: Paul Scherrer Institute/Laura Hennemann The Paul Scherrer Institute PSI develops, builds and operates large, complex research facilities and makes them available to the national and international research community. The institute's own key research priorities are in the fields of matter and materials, energy and environment and human health. PSI is committed to the training of future generations. Therefore about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether PSI employs 2100 people, thus being the largest research institute in Switzerland. The annual budget amounts to approximately CHF 400 million. PSI is part of the ETH Domain, with the other members being the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL Lausanne, as well as Eawag (Swiss Federal Institute of Aquatic Science and Technology), Empa (Swiss Federal Laboratories for Materials Science and Technology) and WSL (Swiss Federal Institute for Forest, Snow and Landscape Research). "Now it's time for something new" - An interview from 30 January 2019 with Gabriel Aeppli and Christian Rüegg about new solutions for better computers and data storage systems. Condensed Matter Theory Group Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland Telephone: +41 56 310 27 78; e-mail: firstname.lastname@example.org [German, English] Universal Quantum Computing Using Electronuclear Wavefunctions of Rare-Earth Ions M. Grimm, A. Beckert, G. Aeppli, M. Müller PRX Quantum 21 January 2021 (online)

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Image: Dane Wirtzfeld/iStockphoto Ever since the laser saw the light of day a half century ago, researchers have been playing with the idea that something similar could be created using sound rather than light. But the concept made little headway in the ensuing decades. In 2009, the situation changed abruptly, when scientists at Caltech and the University of Nottingham, in England, using tiny drums and stacked semiconductors, respectively, employed conventional lasers to stimulate or probe the emission of a stream of “phonons”—the quasiparticles of sound—proving that phonon lasers, or “sasers,” were indeed a sound idea. Now researchers at NTT Basic Research Laboratories, in Japan, have taken a significant step forward by fabricating an entirely electromechanical resonator on a chip that also eliminates the need for the lasers that previous devices required. This advance makes integration with other devices easier, and applications like extremely high-resolution medical imaging and compact, low-power, high-frequency clock-pulse generators are now within reach, say its inventors. The word laser is an acronym for “light amplification by stimulated emission of radiation.” A laser works by exciting electrons around an atom to higher levels, which then shed the extra energy in the form of photons. This activity takes place in an optical resonator, which is essentially an enclosed chamber, typically with mirrors at either end. The trapped photons bounce back and forth, stimulating the emission of more photons of the same wavelength, some of which are allowed to escape in a controlled beam of laser light. “In our approach to the saser, we replaced the optical resonator with a microelectromechanical resonator, or oscillator, that moves up and down and produces a spectrum of discrete sonic vibrations, or phonon modes,” says Imran Mahboob, a researcher at NTT. “Simply put, we’re creating an electromechanical atom that we then jiggle to produce the phonons.” The resonator consists of a micrometer-scale gallium arsenide bar (250 x 85 x 1.4 micrometers) called a beam, which is suspended above a gap in a semiconductor chip and whose oscillations are controlled with piezoelectric transducers. An alternating voltage applied to the beam’s terminals induces alternating expansion and compression. In this scheme, the bar plays the part of an optical resonator, while three levels of oscillating tones or modes (high, middle, and low) mimic the changing of the electron energy levels of the atoms in a specific type of optical laser, generating phonons in the process. When the high state is excited, it generates phonon emissions in the middle and low states. With some fine-tuning of the system, so that the sum frequency of the middle and low states matches the high mode, emission in the low mode is resonantly enhanced, and a precise, highly stable phonon beam is produced, with fluctuations limited to one part in 2 million. Because the mechanical oscillations are extremely tiny, existing at the subnanometer level, “we place everything into a cryogenic environment with a temperature of around 2 kelvin to make them easier to observe,” says Mahboob. “This also ensures that the different resonance modes are precise, because if [the device is] hot, their frequencies would broaden and overlap so that the sum frequency of the middle and low states wouldn’t always match the high state.” Well-Balanced Beam: A gallium arsenide resonator is the heart of NTT's phonon laser.Image: NTT Notably, an output signal is observed only when the input voltage exceeds a specific figure. This threshold voltage is a signature feature of optical lasers, as is a large improvement in the beam’s frequency precision when phonon lasing is triggered. “So we’re convinced we have phonon lasing,” says Mahboob. As for how such a laser could be used, he says that the device’s compactness, low energy consumption, and the possibility of high frequency give it the potential to replace the relatively bulky quartz-crystal resonators used to provide stable frequencies for synchronized operations and precise timekeeping in computers and other electronic equipment. Superior medical ultrasound imaging is another possible application, and Mahboob speculates that one day the laser might be used as a medical treatment. Hiroshi Yamaguchi, an NTT senior distinguished researcher, also points out that by increasing the frequency of the oscillating states, the resonator could potentially be manipulated to store a discrete number of phonons. “This could open up new avenues to explore quantum cryptography and quantum computing,” he says, “as well as having the potential of enabling us to study quantum effects at the macro level.” But before such speculations can be seriously investigated, the researchers admit they must first overcome a major challenge. Whereas optical lasers can travel through a vacuum, a phonon beam requires a medium. In this research, the sound propagates through the semiconductor crystal, and the researchers are now working out how to handle this limitation. “On the other hand, the technology does have the advantage [in] that it’s a compound semiconductor,” points out Yamaguchi. “So it could, for example, easily be integrated with an optical device and an electrical device all on the same chip and, of course, integrated with a variety of systems. We believe this is a major advantage of our device.” Other phonon laser researchers have been improving their devices, too. Tony Kent, a professor of physics at the University of Nottingham who is working with semiconductor stack devices to realize sasers, has been working on using them for applications that need frequencies in the hundreds of gigahertz or even terahertz frequencies. “Our main focus is exploring applications for a terahertz saser as a stable, low-noise reference or local oscillator, for use in communications, medical imaging, and security screening, and as a source for acoustic sensors of nano-objects,” he says. Kent says that while he expects the NTT research to have a major impact on the fundamental science of micromechanical systems, he questions the practicality of some of the applications that are being suggested. Putting aside the problem of having to work with low temperatures, and the difficulty getting the sound out of the resonator and into the semiconductor crystal, the reported beam device works at a frequency of only around 1 megahertz,” says Kent. “Yet there are already technologies generating acoustic signals for ultrasound measurement available now with frequencies higher than 1 GHz.” About the Author John Boyd covers technology in Japan. In April 2013, he reported on the test of new silicon carbide power electronics in the Tokyo subway system.

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Keep yourself updated. Quantum computing is a novel paradigm for computing that was introduced as a concept in the 1980s and has enjoyed a lot of attention in the recent years as the research and development for building actual quantum computers has started to bear fruit. Quantum computing holds great promise for solving some of the most difficult computational problems. It is expected to bring major advantages, for example, for drug development, weather forecasting, different kind of optimizations problems, etc. Unfortunately, quantum computing also has a darker side; If large enough quantum computers become reality, then they may solve the computational problems that are the basis of modern computer security. Specifically, a quantum algorithm introduced by Peter Shor in the mid-1990s and subsequently called the Shor's algorithm can perform integer factorisation and find discrete logarithms in polynomial time (that is to say, significantly faster than what is possible with classical computers). RSA and Elliptic Curve Cryptography (ECC), which together cover practically all currently deployed public key cryptosystems, are based on integer factorisation and discrete logarithms, respectively. Consequently, quantum computing poses a threat to RSA and ECC and the security of the modern computation and communication infrastructure as a whole. The state-of-the-art of quantum computers is still far from being able to break practical cryptosystems, and certain difficult technical problems must be solved before quantum computers can be scaled to the sizes that pose a practical threat. Nevertheless, the threat of quantum computing must be taken seriously and it must be addressed pro-actively because often data needs to remain secure for decades and also rolling any new cryptosystems into practical use takes a long time. Despite the gloomy sky, it is important to understand that not all cryptography is at risk and that is a clear roadmap for protecting systems even in the era of quantum computing. First of all, symmetric cryptography (for example, AES or ChaCha20) are not affected by quantum computing in any significant way – there exist a quantum algorithm called Grover's algorithm which solves generic search problems faster and, consequently, affects also symmetric cryptography, but it can be countermeasured by doubling the key lengths. That is, you can just use 256-bit keys in systems where you currently use 128-bit keys and you will be safe: for instance, replace AES-128 with AES-256 and you are done. Although the currently deployed public key cryptosystems are vulnerable to the Shor's algorithm, the general idea of public key cryptography (asymmetric cryptography) is not, and new public key cryptosystems can be designed based on computational problems that cannot be solved with the Shor's algorithm. Such algorithms are already available and are also currently developed and studied further. They are often referred to as Post-Quantum Cryptography (PQC), but sometimes also called quantum-proof, quantum-safe, or quantum-resistant cryptography. The cryptographic research community has studied PQC already for more than 15 years (the first academic conference on PQC was held in 2006). It is essential to realize that when it comes to implementing PQC algorithms, they are like any other algorithms and can be implemented with existing classical computers. In December 2016, the American National Institute of Standards and Technology (NIST) announced that it will organize a competition-like process for developing a standard for PQC. NIST aims to standardize PQC algorithms in two categories: key-encapsulation mechanisms and digital signature algorithms. The PQC competition received 69 proposals that fulfilled the initial requirements and that entered Round 1. At the time of writing this blog, the competition has proceeded to Round 3, and 15 algorithms remain in the competition, seven of which are the finalists and the rest are alternatives. After Round 3, NIST will select the first winners from the finalists. They will be included in the forthcoming PQC standard that is expected to be ready within a couple of years. The key-encapsulation finalists are The digital signature finalists are NIST said in the Round 2 status report that Kyber and Saber are its favorite key-encapsulation mechanisms and Dilithium and Falcon are favored for digital signatures. Our view at Xiphera is that the situation has not changed, with the exception of digital signatures where Dilithium and Falcon have become even stronger favorites because Rainbow has been broken. The most promising algorithms (Kyber, Saber, and Dilithium) are based on different variations of the learning with errors problem over rings. The advantages of such cryptosystems over other algorithms in the competition are that they have relatively small key sizes and good performance, and do not really have any obvious weak points like many other candidates (for example, Classic McEliece provides good confidence in its security and fast computations, but suffers from extremely large key sizes). But, in the end, we must wait for NIST to make the final decision before we have any certainty about the algorithms that will form the basis of the future PQC standard. NIST has stated that the announcement of the winners will be done very soon – by the end of March 2022. Even after that the competition will continue with Round 4 including selected algorithms that may be added into the standard later on. Also certain European nations (for instance, Germany and France) have published their own recommendations on PQC. Anyone who designs new systems that require public key cryptography should study also them. The new PQC algorithms will imply changes into currently used security protocols, and it can be expected that algorithms can be rarely changed in a simple plug-and-play manner. One notable difference, especially when compared with the current ECC-based solutions, is that key sizes will grow regardless of which algorithm is announced as a winner, and for certain algorithms the growth would be significant. Additionally, protocols that currently rely on Diffie-Hellman key exchange will need to change to use a key-encapsulation mechanism for the key exchange (for instance, TLS 1.3). There may also be difference in the speed of computation compared to current highly optimized ECC implementations, but the difference in this respect is probably smaller than what people typically believe. Methods such as Kyber and Saber may be even faster than the current ECC counterparts. Another aspect to consider is that PQC algorithms have not yet gained a similar level of confidence in their security as RSA and ECC have at the moment. Therefore, it is not completely out of the question that a severe weakness even against classical attacks could be found, and Rainbow already gives us a frightening example. For this reason, many experts (for example, French ANSSI) recommend designing hybrid systems that combine PQC with classical public key cryptography such as ECC. Such hybrid protocols are already under development (see, for example, for TLS). We at Xiphera are eagerly waiting for the NIST decision and planning to start offering PQC IP cores and solutions to our customers soon after that. Dr. Matti Tommiska reviews the current status in quantum computing, and specifically its impact on the currently used public-key cryptographic algorithms. The winning algorithms of NIST (National Institute of Standards and Technology) PQC (Post Quantum Cryptography) competition will form the foundation of future public-key cryptographic algorithms and protocols. Since the adoption and finalization of PQC algorithms and protocols will likely take years, the crypto agility of FPGAs has clear advantages over fixed-function silicon. Find the full recording of the webinar and presentation slides here. If you want receive information about Xiphera's upcoming webinars, sign up for Xiphera's webinar subscription list here, and you'll never miss any of our fascinating webinars! Xiphera Ltd © 2023

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Einstein famously laboured hard to create the theory of general relativity, but it is less well known that he also helped to launch quantum mechanics, which he didn’t much care for. These two views of the world are the very foundation stones of modern physics – without them we would not have things such as space travel, medical imaging, GPS systems or nuclear energy. General relativity is unparalleled when it comes to describing the world on a large scale, such as planets and galaxies, while quantum mechanics perfectly describes physics on the smallest scale, such as the atom or even parts of the atom. Uniting the two into a consistent “theory of everything” is the single biggest challenge in physics today – and progress is slow. The Birth Of Modern Physics Our knowledge of the universe is based on a sequence of “natural laws”. With time many laws become morphed into new ones as a result of experimental evidence or changing conceptual prejudices. Einstein’s rejection of the concept of universal time was one of the most radical shifts in the history of physics. Its consequences have proved crucial to shaping some of the most profound developments in our understanding of nature. By fusing the three dimensions of space (height, width and depth) with that of a time direction to construct a “spacetime structure”, a new symmetry of nature could be uncovered. When Einstein later added gravitation to his theories, it led to experimentally verifiable predictions as well as the prediction of gravitational waves and black holes, beyond the natural scope of Newton’s existing law of gravitation. But Einstein didn’t just work on relativity. A big problem at the time was the fact that Maxwell’s laws, describing electromagnetic phenomena, were unable to explain why faint ultraviolet light falling on metallic electrodes could induce sparks more easily than bright red light. Einstein suggested that this could be understood if the energy in the light wave wasn’t continuously distributed as a wave but rather as a shower of individual “light bullets” (photons – also known as “light quanta”), each with an energy proportional to the colour (frequency) of the light. Many scientists were sceptical of this groundbreaking thought, as so many experiments had already shown that light was a wave. Millikan proved Einstein right. Nobel foundation/wikipedia, CC BY-SA One of them was Robert Millikan, who ironically eventually ended up experimentally verifying Einstein’s theory. Millikan also discovered that charged particles known as electrons have wave-like properties. Together with Einstein’s discovery, this pointed to a duality where both matter and light could be described as a particle or as a wave – an idea which led to the development of quantum mechanics by a number of scientists. This theory has had wide applicability on the smallest of scales, where gravity can often be neglected as it is so weak compared to the other forces affecting particles. Not only has it led to a consistent description of matter and radiation observed in everyday life, it has also made predictions of new particles and processes that are now observed in high-energy accelerator experiments on Earth or cosmic events in space. To unify the description of matter and radiation quanta with gravitation it became natural to contemplate “gravitational quanta” that carry the force of gravitation. String theory has emerged as a candidate to do this. It states that matter is made up of vibrating extended structures, like tiny strings or membranes, rather than point-like particles. Each type of vibration of these structures corresponds to a particular state of matter. One type of vibration also corresponds to a gravitational quantum. However, for the resulting quantum description to be consistent it becomes necessary to boost the dimension of spacetime by introducing additional space dimensions that are unobservable to the eye and current technology. To date, there has been no firm experimental confirmation of string theory. By contrast, in domains where gravitation appears irrelevant, quantum mechanics remains unchallenged, despite describing a very strange world. It states that particles can be in a number of different possible states at once. While the theory can predict a set of probabilities for the particle to be in a particular state, it cannot, in general, predict which probability will actually occur. In such cases, one must take a large number of observations and then calculate average measurements. Furthermore, such averages depend on what properties are to be measured and when such measurement decisions are made. This peculiar world picture sits uncomfortably alongside Einstein’s world view of causal events and frozen histories in spacetime. What’s more, according to quantum mechanics, one particle’s state can be correlated with another particle’s state, even if it is in a distant location. Einstein didn’t like this because it seemed to imply that correlations could occur over events that could not be connected by a beam of light, thereby breaking a rule that says nothing can travel faster than the speed of light. He felt that such “spooky action at a distance” was proof for the incompleteness of the theory, although experimental evidence since points to the contrary. Could the International Space Station be the key to probe the effects of gravity on quantum entanglement? NASA/wikipedia However, new experiments are underway to see whether gravitational interactions might influence such eerie action in unexpected ways. A research group in Vienna proposes to use the International Space Station to see how gravity might influence this action. A collection of entangled photon pairs will be created on Earth before one member of each pair is sent to the orbiting space station. There, a state known as polarisation will be recorded and compared with the state of its partner on Earth. It is unclear whether quantum mechanics or general relativity will need either mathematical or conceptual modification in response to future experimental probing. But while the outcome is difficult to predict, Einstein’s influence has been and remains pivotal in this quest. Robin Tucker, Professor in mathematical physics, Lancaster University This article was originally published on The Conversation. Read the original article.

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Distributed denial-of-service (DDoS) attacks can be a major threat to the availability... Nowadays, the very abstract ideas underlying the quantum physics are being translated into reality thanks to new technological capabilities in the field of nanotechnology and optical interactions. One of these ideas, the idea of a quantum internet and a quantum computer, will be discussed further in this article. While the subject is very broad, we’ll try to summarize the basic ideas behind these technologies. Quantum Internet allows to send quantum data (quantum bits or qubits) from one quantum computer to another. The media here is either a fiber optic cable or a free space connection with a clear line of sight between the starting and the destination point of a signal. Classical computers work with conventional bits that can be either zero or one. Quantum mechanics, however, allows qubits to be in a superposition state that can be 1 and 0 at the same time. Therefore, we can encode more information in qubits than in conventional bits. The amount of information that can be stored and processed using qubits is 2n, where n is the number of qubits. So, in two qubit systems, we need four numbers (bits) to determine the state of the system. To define the state of the three qubits system we need 8 numbers. If we have 300 qubits, the equivalent of classical bit information is 2300. Quantum computer is a computer where the number of operations grows exponentially. However, the improvement is not in the speed of an individual operation but rather a total number of operations to write the result. Therefore, quantum computers are not generally faster, they are faster only for specific types of calculations . We can easily grasp this concept by playing the light switch game provided by D-Wave . The game explains why a quantum computer is faster than a conventional computer in a process of finding the best combination of switches when a number of the switches is large. As stated, “The quantum computer begins with the bits in superposition (the switch can be in both ON and OFF states), ends with them behaving as regular classical bits, and finds the answer along the way”. However, with only 500 switches, there is not enough time in the universe to check all the configurations when conventional processors are used. So far, only a small number of quantum algorithms have been found. Here are some of the most famous ones: Shor’s Algorithm (factorization) Grover’s Algorithm (quick search in an unordered database) Deutsch–Jozsa Algorithm (produces an answer; the function is either constant or balanced) Let’s review the Shor’s algorithm in a bit more detail. It allows to solve any of the two mathematically equivalent problems below: - Finding the period of a complex periodic function or - Decomposing a very large number into the prime factors The second of these tasks is of significant practical importance since it is used in cryptography. When encrypting and decrypting secret messages (public key encryption) large numbers are used for which their factorization is known. It is clear that such numbers are easy to obtain: it is enough to multiply a large number of prime numbers, and we get a very large number for which the factorization is known. The recipient of the encoded secret message can decode it because the decoding procedure uses factorization of a long number, and he/she knows this decomposition. If a third party could factor this number into the prime factors, he/she would also be able to decode the message. However, this decomposition takes a lot of time. Therefore, from a practical point of view, it is impossible to decode such messages. But if the third party would’ve had a quantum computer, then he/she could decompose long numbers into simple factors quite fast and therefore could easily decipher such messages. The common cryptography method used today would stop working. This is one of the arguments that make the creation of a quantum computer important. On the other hand, quantum networking provides another secure communication benefit. Quantum Key Distribution (QKD) enables secure communication whose security relays on quantum mechanics. For instance, the spin of an electron can be used as a qubit since it can undergo transitions between the spin-up and spin-down quantum states, represented classically by 0 and 1. In other words, qubits are based on physicals properties of the particles such as electron spins or polarization of photon. However, if we would want to measure the electron’s spin, some of its properties would change. If we were to apply the temperature near the absolute zero (-273 Celsius), the electron would be spin down ↓. If we were to write the information to a qubit we would put the electron into a spin-up state ↑ by hitting it with a pulse of microwaves with specific frequency. We would not know the spin of an electron until we measure it. And when we measure it, the qubit’s physical properties are changed. Therefore, it is also impossible to make exact copies of qubits or to clone it. This is known as a quantum no-cloning theorem. Qubits perfectly suit for secure communication. If Bob and Alice exchange an encryption key using qubits and Eve intercepts communication, both Alice and Bob know that someone messed with qubits as the physicals properties of the qubits changed. Therefore, extracting quantum information without leaving a trace is impossible. The presence of Eve’s eavesdropping communication, can be easily detected. Nowadays, we can send qubits to short distances over telecommunication fibers up to 200 kilometers. The reason for that is Decoherence – a situation where the system being measured loses its specific quantum properties. In other words, the pure state quickly turns into a mixture when the quantum system interacts with the environment. So, the real challenge in building quantum Internet is to send qubits further than a few hundred kilometers. The single photon sent over a fiber optic cable can be lost. As we know, qubits cannot be copied or amplified so they cannot be resent without a notice. To solve this issue, the box called a quantum repeater is placed in the middle of the communication line and the pair of photons is exchanged between the repeater and the quantum computer on the left side of the line. Similarly, another pair of photons is exchanged between the repeater and a quantum computer located to the right of the communication line. Quantum repeaters are crucial for entanglement over long distances using fiber optic cables. The vision is to build a long-range quantum internet that will operate in parallel to the Internet we know today. We have already mentioned, that transmission of quantum signals over long distances is prevented by fiber attenuation and the no-cloning theorem. Therefore, one of the realistic scenarios is that the future Quantum Internet will consist of a global network of quantum repeaters that are developed and used in order to extend the range of communication. However, there is also another approach to this problem which is based on the deployment of satellite technology. China launched world’s first quantum communication satellite Miciusin 2016, and has since been busy testing and extending the limitations of sending entangled photons from space to ground stations on Earth and back again . Chinese and European researchers have tested the system by creating secure video conference between Europe and China. There are certain issues associated with quantum computing besides decoherence, such as the search for new algorithms as well as new methods of error correction. All of these problems however can be described in one phrase – scalability issues. Quantum computers are the “holy grail” of modern physics and informatics. The idea of a quantum computer and a quantum network looks unrealistic at first. A regular classical computer was probably perceived the same way at the time of Charles Babbage, the invention of which happened only a hundred years later. QCs on two or three qubits already exist, but they require the use of high technologies (pure substances, precise implantation of individual atoms, a highly accurate measurement system, etc.). However, as mentioned earlier, the main challenge is not the technological one but the fundamental one of scalability. It is unlikely that quantum computers will replace the classical computers in the near future. We can only speculate that the QCs would be put into clouds to offer unique services whereas personal computers would transmit or access the quantum-encrypted information through the cloud-based QCs. Hopefully, the scientific and technical progress of our time is fast enough, and we will not have to wait too long for quantum computing to become a common reality.

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As data travels through different networks, there is an increased possibility of attacks. AES is the encryption standard used by organizations worldwide to secure sensitive data. AES was published when the need for a better encryption model became apparent. While Data Encryption Standard (DES) was used for around 20 years, AES came as an alternative to DES when it started becoming vulnerable to brute force attacks. AES comes in 128, 192, and 256 bits. This article will help you understand the AES-128 in detail. AES-128 conceals plaintext data using an AES key length of 128 bits. AES-128 encrypts data using 10 transformation rouns and is best suited for protecting secret government information as recommended by the National Security Agency (NSA). The block size of the data encrypted using AES is always 128 bits. 128-bits is the least secure among other variants of the AES algorithm. However, this doesn’t mean that AES-128 is crackable. Since other variants such as 192-bits and 256-bits use more rounds for transformation, AES-128 is comparatively less secure. The steps involved in AES-128 encryption include the substitution of data using a substitution table, shifting rows, mixing columns, and insertion of another round key. How Secure is AES-128 Against Brute Force Attacks? AES processes 128 bits of input data at a time. Based on the substitution-permutation network, AES is a symmetric key. AES performs all its computations on bytes which means it treats the 128 bits of a block as 16 bytes. The bytes are processed as a matrix with 16 bytes organized into four columns and four rows. DES with a key size of 56 bits has been cracked using brute force attacks in the past. AES-128 is a 128-bit symmetric key that is computationally secure against brute force attacks. If you ask how long will it take to crack 128-bit encryption using a brute force attack, the answer would be 1 billion years. A machine that can crack a DES key in a second would take 149 trillion years to crack a 128-bit AES key. Hence, it is safe to say that AES-128 encryption is safe against brute-force attacks. AES has never been cracked yet and it would take large amounts of computational power to crack this key. Governmental organizations and businesses trust the AES for securing sensitive information. What’s the difference between AES-128 and AES-256? AES is considered safe against brute force attacks. Key size is a critical factor in determining whether the algorithm can be cracked. The key size should be large enough to resist attacks from modern computers with large computational power. Understandably, a 256-bit is more difficult to crack due to its key length. However, even cracking a 128-bit key would need quantum computing to generate the necessary brute force. One of the major differences between AES-128 and AES-256 is that the latter takes longer to execute and requires more computational power. Hence, wherever power and latency are a concern, AES-128 encryption is recommended. Regardless of whether AES-128 or AES-256 is used, the surrounding infrastructure should be strong and secure to keep hackers from breaking into the system. The software implemented should be secure and perform functions as the user wants it to. Every organization should have strict guidelines for data handling and storage. Users must follow the security best practices irrespective of what encryption model is being implemented. Choosing between AES-128 and AES-256 As stated earlier, AES-128 uses a 128-bit key length to encrypt and decrypt a block of message whereas AES-256 uses a 256-bit key length to encrypt and decrypt a block of message. Both encryption models have their own pros and cons. AES-128 has greater speed. It is comparatively more efficient and resistant to full attacks. AES-128 is suited to protect secret information. AES-256 on the other hand may be a bit slower and take longer to execute. However, it is used to protect the top-secret information of the government. AES-256 can resist brute force attacks but may not safeguard against related-key attacks. AES is the modern encryption standard capable of resisting attacks in the current threat landscape. Choosing AES-128 or AES-256 depends on each organization’s individual security needs. AES-18 is fast and resource-efficient and provides enough security against cyber attacks. But organizations that deal with highly sensitive information such as the defense sector should go with AES-256 as the longer key size provides extra protection against attacks. A 128-bit level of encryption has 2128 possible key combinations. AES is by far the most advanced encryption trusted by organizations worldwide. AES-128 is strong enough to meet future security needs. AES is used in self-encrypting disk drives, database encryption, and storage encryption. AES can be safely implemented in firmware, hardware, and applications that need low latency and high throughput. In the present day, AES is widely used in software and hardware. AES assures security only if the implementation is right. Keys should be stored properly as hackers can easily misuse data if they get their hands on the keys. Key management is critical to ensure AES provides a strong defense against attacks. AES remains the best choice for securing communications as it has more key length options. Appsealing is a robust mobile app security solution provider that ensures in-app protection with zero coding. It makes mobile security holistic and effective with real-time updates. Add scalable protection to your mobile apps with security solutions that are compatible with third-party libraries and provide threat analytics on attack vendors. Get in touch with AppSealing for end-to-end protection for a range of applications.

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A new method using conventional computing can reduce simulation time from 600 million years to months, challenging a claim of ‘quantum advantage’. Achieving ‘quantum advantage’ – where a quantum computer can achieve something even the world’s fastest conventional supercomputers can’t on a reasonable timescale – is an important landmark on the journey toward creating a useful quantum computer. Quantum computing is of course the holy grail, but it is often easy to lose sight of the importance of the computational resources we currently have that can help us along the way. Dr Raj Patel Researchers at the University of Bristol, Imperial College London, the University of Oxford, and Hewlett-Packard Enterprises are keeping pace with quantum advantage by developing new methods that can reduce simulation time on conventional computers by a speedup factor of around one billion. Quantum computers promise exponential speedups for certain problems, with potential applications in areas from drug discovery to new materials for batteries. But quantum computing is still in its early stages, so these are long-term goals. The new research, published today in the journal Science Advances, challenges a previous claim of quantum advantage by improving a method of conventional computing, vastly speeding it up. Claiming quantum advantage The study follows an experimental paper from the University of Science and Technology of China (USTC) that was the first to claim quantum advantage using photons – particles of light. In USTC's experiment, they generated a large and highly complex quantum state of light and measured it using single-photon detectors in a protocol called ‘Gaussian Boson Sampling’ (GBS). Their paper claimed that the experiment, performed in 200 seconds, would take 600 million years to simulate on the world's largest supercomputer. The new study reveals that updated methods of simulating GBS can reduce the predicted simulation time of 600 million years down to just a few months, a speedup factor of around one billion. Joint first author Dr Bryn Bell, previously of the Department of Physics at Imperial and now Senior Quantum Engineer at Oxford Quantum Circuits, said: “As researchers develop larger scale experiments, they will look to make claims of quantum advantage relative to classical simulations. Our results will provide an essential point of comparison by which to establish the computational power of future GBS experiments.” The value of current computational resources Co-author Dr Raj Patel, from the Department of Physics at Imperial and the University of Oxford said: “Our work with the University of Bristol and Hewlett-Packard Enterprises emphasises the need to continue developing simulation methods that run on ‘classical’ hardware. "Quantum computing is of course the holy grail, but it is often easy to lose sight of the importance of the computational resources we currently have that can help us along the way. “Using these resources to find the boundary at which quantum advantage can be obtained is not only of academic interest but is crucial in instilling confidence in potential stakeholders in emerging quantum technologies.” The team’s methods do not exploit any errors in the experiment and so one next step for the research is to combine their new methods with techniques that exploit the imperfections of the real-world experiment. This would further speed up simulation time and build a greater understanding of which areas require improvements. Joint first author Jake Bulmer, a PhD student at the University of Bristol, said: “The USTC estimate used the best-known simulation methods known at the time, but we were confident significant improvements could be made. By asking ourselves, what is it about this experiment which makes it complex, we could uncover understanding for how to simulate it in the most efficient way. “In essence, our methods reveal which parts of GBS experiments are important and which are not when it comes to designing new generations of devices. For example, we show that if the photon detectors are improved, this could substantially increase the complexity of the experiment. “These simulated experiments represent a tremendous achievement of physics and engineering. As a researcher, it is truly exciting to contribute to the understanding of where the computational complexity of these experiments arises. We were pretty thrilled with the magnitude of the improvements we achieved - it is not often that you can claim to find a one-billion-fold improvement!” ‘The boundary for quantum advantage in Gaussian boson sampling’ by Jacob F. F. Bulmer et al. is published in Science Advances. Based on a press release by the University of Bristol. Article text (excluding photos or graphics) © Imperial College London. Photos and graphics subject to third party copyright used with permission or © Imperial College London. Tel: +44 (0)20 7594 2412 Show all stories by this author Leave a comment Your comment may be published, displaying your name as you provide it, unless you request otherwise. Your contact details will never be published.

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When we study physics one thing that we are always told, is that it is definite. Either it can be something it or it cannot be. Right? Wrong. As opposed to the Newtonian theory of light’s nature as a particle, in the late 19th century, Albert Einstein revived light’s wave nature. The nature of light as the wave was proposed by Huygens but was dismissed because of Newton’s reputation. However, later it was established that light is “both a particle and a wave”, its essential theory was further evolved from electromagnetics into quantum mechanics. If you did not understand anything that’s written above just skip the jargons and read along with the wonderful manifestation of quantum physics in computing. Here is the simple definition of the concept behind Quantum Computing. The data is stored in a computer in the Boolean form, i.e in sets of 0 and 1. All digital circuits that we have presently are all dependent on this Boolean concept of data storage. You can say that all the computer circuitry that we have today, understands boolean logic; that is the manifestation of data and information in 0s and 1s. Quantum Computing challenges the present advances in digital electronics technology. Now data can exist in multiple bits, anything between 0 and 1. Here is an example to illustrate it: The lowest unit of memory is a bit. (Take it analogous to cell ‘the building block for the body’). A bit can be either 1 or 0. 8 bits make 1 byte. 1024 bytes as 1KB, 1024 KBs as 1 MB and so on. So for 1 byte, there can be 16 possible combinations in which data can be stored. For quantum computers, the smallest unit of memory is called Quantum Bit aka Qubit. A qubit can exist in both states simultaneously (0 and 1), as well as many other states in between. Now as opposed to the traditional concept Qubits can hold much more data, that gives rise to faster parallel computing. The future is very promising as computer scientists working on quantum computers believe that it will be possible to harness these mechanisms and build computers which will be millions of times more efficient than anything available today. Here are some of the real-world problems that Quantum Computers are expected to solve. With a lot of businesses and transactions online, the last decade had seen a 100% increase in data breaches by nefarious hacker groups holding digital businesses hostage for ransoms. Quantum computers will revolutionize data security as we see today. Even though quantum computers would be able to crack many of today’s encryption techniques, predictions are that they would create hack-proof replacements. Even though Quantum Computers is the thing for the future, don’t expect it to be a regular home use computer. The computers that we have now is not going to go anywhere or be replaced by Quantum Computers. In fact, classical computers are better at some tasks than quantum computers (email, spreadsheets and desktop publishing to name a few). Given the faster processing speed, Quantum computers are great for solving optimization problems. From figuring out the best way to schedule flights at an airport to determining the best routes on Google Maps things will be more efficient. Recently, Google announced its indigenous quantum computer 100 million times faster than any classical computer in its available. Now that you have a brief idea of what a quantum computer can do, let’s see what some of its advocates have to say about this. Satya Nadella, Microsoft CEO: “The world is running out of computing capacity. Moore’s law is kinda running out of steam … [we need quantum computing to] create all of these rich experiences we talk about, all of this artificial intelligence.” Seth Lloyd, author of Programming the Universe: “A classical computation is like a solo voice – one line of pure tones succeeding each other. Quantum computation is like a symphony – many lines of tones interfering with each other.” Jeremy O’Brien, a physicist at the University of Bristol: “In less than 10 years quantum computers will begin to outperform everyday computers, leading to breakthroughs in artificial intelligence, the discovery of new pharmaceuticals and beyond. The very fast computing power given by quantum computers has the potential to disrupt traditional businesses and challenge our cybersecurity.” While the world is optimistic to revolutionize a lot of things by using Quantum Computers, building one is no less than a scientific conundrum. With the sub-polar requirement of temperatures, Quantum computing requires extremely cold temperatures, as sub-atomic particles must be as close as possible to a stationary state to be measured. The cores of D-Wave quantum computers operate at -460 degrees f, or -273 degrees c, which is 0.02 degrees away from absolute zero. So don’t be surprised if the arctic and antarctic is the next destination to claim information supremacy

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Lensing caused by various analytic spacetimes. For all panels, we use Figure 3 as a background, oriented such that the camera is pointed at the white reference dot. The camera has a 60 degree feld of view and is at a distance of 15 Schwarzschild radii from the origin measured using Kerr- Schild coordinates. The top row shows Minkowski and Schwarzschild spacetimes. The bottom row shows two views of the Kerr spacetime, with dimensionless spin x = 0.95, viewed with the camera pointing parallel to the spin axis of the black hole (bottom left) and perpendicular to the spin axis (bottom right). (Credit: A. Bohn, F. Hebert, W. Throwe, D. Bunadar, K. Henriksson, M. Scheel, N. Taylor) The difficult part of this work is calculating the trajectory of the photons using the physics of general relativity. These equations are notoriously non-linear, so physicist sometimes simplify them by assuming that a system remains constant in the time it takes for light to pass by. The difficulty with black hole binaries is that this assumption does not hold— these objects orbit so rapidly as they approach each other that space-time warps, even during the time it takes for light to pass by. A BBH system of equal-mass black holes with no spin, viewed near merger with the orbital angular momentum out of the page. (Credit: A. Bohn, F. Hebert, W. Throwe, D. Bunadar, K. Henriksson, M. Scheel, N. Taylor) Andy Bohn(et al.) at Cornell University in Ithaca, New York, reveals how in-spiraling black hole pairs should distort the light field around them. The team has concluded that from large distances, binaries are more or less indistinguishable from single black holes. Only a relatively close observer would be able to see the fascinating detail that they have simulating or one with very high resolving power. The first observation of much bigger deflections, such as those produced by black holes or black hole pairs, will be something of a triumph for whoever spots them first. via physics arxiv Like this: Like Loading... Friday, September 19, 2014 When space probes, such as Rosetta and Cassini, fly over certain planets and moons, in order to gain momentum and travel long distances, their speed changes slightly for an unknown reason. A researcher has now analyzed whether or not a hypothetical gravitomagnetic field could have an influence. However, other factors such as solar radiation, tides, or even relativistic effects or dark matter could be behind this mystery. An artist’s rendition of Rosetta probe during a flyby. (Credit: ESA/C.Carreau) via The starboard truss of the International Space Station while Space Shuttle Endeavour docked with the station. The newly installed Alpha Magnetic Spectrometer (AMS) is visible at center left. (Credit: NASA) via The dome of the Blanco Telescope, which houses DECam, the 570-megapixel CCD camera used for the Dark Energy Survey, at the Cerro Tololo Inter-American Observatory in Chile. (Credit: Reidar Hahn) via The lonely landscape of Rosetta’s comet – Comet 67P/Churyumov-Gerasimenko from a distance of just 29 kilometers (Credit: ESA) via Mosaic of southern hemisphere of Miranda, the innermost regular satellite of Uranus, with radius of 236 km. Projection is orthographic, centered on the south pole. Visible from left to right are Elsinore, Inverness, and Arden coronae. (Credit: NASA/Jet Propulsion Laboratory/Ted Stryk) via An international team of physicists has shown that the mass ratio between protons and electrons is the same in weak and in very strong gravitational fields. Pictured above is the laser system with which the hydrogen molecules were investigated on earth. (Credit: LaserLaB VU University Amsterdam/Wim Ubachs) via The MIT BioSuit, a skintight spacesuit that offers improved mobility and reduced mass compared to modern gas-pressurized spacesuits. (Credit: Jose-Luis Olivares/MIT) via mit Like this: Like Loading... Friday, September 5, 2014 Artist impression of the Square Kilometer Array. If all goes according to plan in the next decade, we could see these small perturbations on the moon—and begin to solve some of the mysteries of space. (Credit: SKA) via Space travelers from around the world are headed to China this month for an international Planetary Congress, which will explore the possibilities for expanding human spaceflight cooperation among different countries. Pictured above is China’s first astronaut, Yang Liwei, is now vice director of the China Manned Space Engineering Office. (Credit: CMS) via An animation of the quicksort algorithm sorting an array of randomized values. The red bars mark the pivot element; at the start of the animation, the element farthest to the right hand side is chosen as the pivot. (Credit: RonaldH) via Rather than keeping all its eggs in D-Wave’s basket, Google’s “Quantum A.I. Lab” announced that it is starting a collaboration with an academic quantum computing researcher, John Martinis of the University of California-Santa Barbara. (Credit: Wiki, Timmer) via In the grasp of the Japanese robotic arm, NanoRack’s CubeSat deployer releases a pair of miniature satellites last month. (Credit: NASA) via discovery Like this: Like Loading...

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Flashes of what could become a transformative new technology shoot through a network of optical fibers beneath Chicago. Researchers have created one of the world’s largest networks for sharing quantum information– a field of science that relies on paradoxes so strange that Albert Einstein did not believe them. The network, which links the University of Chicago to Argonne National Laboratory in Lemont, is a rudimentary version of what scientists hope to one day become the Internet of the future. For now, it is open to businesses and researchers to test the basics of quantum information sharing. The network was announced this week by the Chicago Quantum Exchange, which also involves the Fermi National Accelerator Laboratory, Northwestern University, the University of Illinois and the University of Wisconsin. With $500 million in federal investment in recent years and $200 million from the state, Chicago, Urbana-Champaign and Madison are a leading region for quantum information research. Why does this matter to the average person? Because quantum information has the potential to solve currently unsolvable problems, both threaten and protect private information, and lead to breakthroughs in agriculture, medicine and climate change. While classical computing uses bits of information that contain a 1 or a zero, quantum bitsor qubits, are like a coin tossed in the air – they contain both a 1 and a zero, to be determined once detected. That property of being in two or more states at once, called superposition, is one of the many paradoxes of quantum mechanics: how particles behave at the atomic and subatomic levels. It’s also a potentially critical advantage, as it can handle exponentially more complex problems. Another important aspect is the property of entanglement, whereby qubits separated by large distances can still be correlated, so that a measurement in one place reveals a measurement far away. The recently expanded Chicago network, created in collaboration with Toshiba, scatters light particles called photons. Trying to intercept the photons destroys them and the information they contain, making it much harder to hack. The new network will allow researchers to “expand the boundaries of what is currently possible,” said University of Chicago professor David Awschalom, director of the Chicago Quantum Exchange. However, researchers need to solve many practical problems before large-scale quantum computing and networks are possible. For example, researchers at Argonne are working to create a “foundry” where reliable qubits can be forged. An example is a diamond membrane with small cells to hold and process qubits of information. Argonne researchers also created a qubit by freezing neon to hold a single electron. Since quantum phenomena are extremely sensitive to any disturbance, they can also be used as small sensors for medical or other applications, but they also need to be made more durable. The quantum network was launched in Argonne in 2020, but has now been extended to Hyde Park and opened for use by businesses and researchers to test new communications equipment, security protocols and algorithms. Any company that relies on secure information, such as bank financial records or hospital medical records, could potentially use such a system. While quantum computers are now under development, they may one day be able to perform much more complex calculations than current computers, such as folding proteins, which could be useful in developing drugs to treat diseases such as Alzheimer’s disease. In addition to stimulating research, the quantum field stimulates economic development in the region. A hardware company, EeroQ, announced in January that it is moving its headquarters to Chicago. Another local software company, Super.tech, was recently acquired and several others are starting up in the region. Since quantum computing can be used to hack into traditional encryption, it has also attracted bipartisan attention from federal lawmakers. The National Quantum Initiative Act was signed by President Donald Trump in 2018 to accelerate quantum development for national security purposes. In May, President Joe Biden ordered the federal agency to migrate to quantum-resistant cryptography on its most critical defense and intelligence systems. Ironically, basic math problems, such as 5+5=10, are somewhat difficult due to quantum computer† Quantum information will likely be used for advanced applications, while classic computing will likely remain practical for many everyday uses. The famous physicist Einstein mocked the paradoxes and uncertainties of quantum mechanics, saying that God does not “play dice” with the universe. But quantum theories have proven correct in applications from nuclear power to MRIs. Stephen Gray, senior scientist at Argonne who is working on algorithms to run on quantum computers, said quantum work is very difficult and no one fully understands it. But there have been significant advances in the field over the past 30 years, leading to what some scientists jokingly called Quantum 2.0, with practical advances expected over the next decade. “We bet that in the next five to ten years there will be a real quantum advantage (over classic computing),’ said Gray. ‘We’re not there yet. Some naysayers shake their sticks and say it will never happen. But we are positive.” Just as early work on conventional computers eventually led to cell phones, it’s hard to predict where the quantum research will lead, said Brian DeMarco, a physics professor at the University of Illinois at Urbana-Champaign who works with the Chicago Quantum Exchange. “That’s why it’s an exciting time,” he said. “Key uses are yet to be discovered.” 2022 Chicago Tribune. Distributed by Tribune Content Agency, LLC. Quote: Chicago Quantum Exchange Takes First Steps Towards a Future That Could Revolutionize Computing and Medicine (2022, June 22) retrieved June 24, 2022 from https://phys.org/news/2022-06-chicago -quantum-exchange-future-revolutionize .html This document is copyrighted. Other than fair trade for personal study or research purposes, nothing may be reproduced without written permission. The content is provided for informational purposes only.

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Nobody agrees on how to build a quantum computer. Why? There are a dizzying number of competing technologies that underlie quantum computing — and so far "it's really too early to pick a winner." Microsoft has bet the farm on something called anyons. IBM, Intel and Google have backed superconductors. And even if those scientific words don't make much sense, at least you've heard of the organizations. Less well-known outfits, like startups IonQ, QuEra, D-Wave, PsiQuantum and Silicon Quantum Computing, are using a range of other esoteric scientific approaches to build quantum computers. No one involved doubts that it's worth attempting to build a machine that can process information using the strange laws that govern matter at the quantum level. But there is no consensus about how best to proceed — or who is going to get there first. "I think it's really too early to pick a winner," says Peter Knight, senior research investigator at Imperial College London. Quantum computers are created from processing units known as quantum bits, or qubits. As well as superconductors (which are materials with zero electrical resistance below specific low temperatures) and anyons (which are so-called "quasi-particles"; more on both below), researchers are creating qubits from ions, atoms, photons and even individual phosphorus atoms embedded in silicon, among others. Each one, as you might imagine given the lack of consensus, has its advantages and drawbacks. Leader of the pack At the moment, superconductors and trapped ions are widely considered the two leading options. Google's superconducting qubits, which are loops of superconductor constricted by a "pinch" at one point — the pinch, called a Josephson Junction, gives the loop similar quantum properties to an atom — are currently considered by many to be the class leader. Late last year, they performed a computation that would take the world's most powerful supercomputer 10,000 years. Google's quantum computer, called Sycamore, did it in 3 minutes and 20 seconds. (Read more.) It was a "very significant" milestone, according to Travis Humble, director of Oak Ridge National Laboratory's Quantum Computing Institute. "We finally had a quantum processor that needed a supercomputer to compare whether we were getting the right answer," he says. "As far as I know, that's the first time that has ever happened." Many startups, such as Rigetti of Berkeley and Finland's IQM, share the optimism about superconducting qubits. As do IBM and Intel. But that enthusiasm and Google's result don't mean other technologies will lose their investors' interest. "My impression is that there are a few different areas generating enthusiasm, so I don't think the field has skewed too much towards Google yet," says Joe Fitzsimons, CEO of Singapore-based startup Horizon Quantum Computing. There are some doubts about whether superconducting qubits can be built and operated in large enough numbers to make them actually useful in the long run. "The question is, how do you get to hundreds of thousands of qubits?" asks Benjamin Bloom of Berkeley-based Atom Computing. And that really is a big question. Google's Sycamore uses just 54 qubits. But no one knows how big a truly useful quantum computer will have to be; some experts claim that 1 million qubits might be required. And because superconducting qubits need to be cooled to around -270 Celsius (-454 Fahrenheit), cooling even thousands of them could prove to be an almost insurmountable headache. The other main contender A strong contender to leapfrog superconductors is so-called ion traps, which are used by companies including Alpine Quantum Technologies, based in Innsbruck, Austria. In ion traps, atoms forming the basis of the qubit have an electron removed, and the ion is held in position using magnetic fields. They have a big plus in terms of practical implementation: "Ion trap systems operate at room temperature and don't require special cooling," says Thomas Monz, a co-founder of Alpine. Monz certainly doesn't characterize Alpine as "behind" in the quantum race. He points out that it and its main competitor on this technology — IonQ, a startup spun out of the University of Maryland — are operating with more than 100 qubits that induce fewer errors than superconducting qubits. They also have established interfaces for operation and modular designs that will make scaling up feasible. That Alpine has a device with more quibits than Google but hasn't demonstrated supremacy isn't necessarily a sign of inferiority: Performance isn't just about raw qubit numbers, and algorithms aren't easily transferred between quantum computers anyway. Instead, think about it as a different but competing technology that is maturing at a different rate, with different priorities. As Monz puts it: "What's the best computer? Do you value the small and mobile one? Or the one with the fast CPU?" Some competitors remain unconvinced about superconductors and ion traps, despite their "frontrunner" status. "Their systems are currently almost unusable — you can only put toy problems on them," says Alan Baratz, CEO of D-Wave Systems, alluding to the currently fragile nature of the world's leading quantum computers. (D-Wave has its own unique — and controversial — approach to quantum computing hardware; see sidebar.) Besides D-Wave, plenty of other technologies are up for the challenge. In Australia, Silicon Quantum Computing hopes that its ambitious scheme to piggyback on the well-established fabrication routines of the semiconductor industry means it can assemble qubits that are easy to manufacture at very large scales. SQC's qubits are the electrons hosted on a single phosphorus atom embedded in a silicon chip, but the company doesn't expect to have a useful general purpose quantum computer until the 2030s. Microsoft might take even longer. Its technology, called topological quantum computing, centers on a particle called — wait for it — a non-abelian anyon. This doesn't occur naturally. It will only pop into existence in very particular circumstances, such as when strong magnetic fields are applied to ultrathin sheets of semiconductors. Not many people outside the company are sure that it can ever be created in a reliable enough way to support a technology infrastructure. "The Microsoft bet is really interesting: It's been extraordinarily hard to make these qubits," Knight says. But, he adds, their properties could allow Microsoft's quantum computations to run without generating errors. That would mean a significant reduction in the number of qubits required because, for most of the technologies, the overwhelming majority of the qubits in a working quantum computer — at least 5 in 6 — are required exclusively for error correction. "Their approach demonstrates that when you've got serious resources, you can look at alternative platforms." That also seems to be true of the most recent entrant to the race. In November 2019, news leaked that Palo Alto-based PsiQuantum raised $230 million to develop its photon-based qubit technology, originally developed at the University of Bristol, into a fully fledged quantum processor. So, there are stronger contenders and underdogs, for sure, but the field is crowded — and there is no outright winner right now. But then, there may never be: The reality is that quantum computers could end up never even usurping classical machines. "If we ever use quantum computers," Humble says, "they are probably going to be integrated into our supercomputing systems." When is a quantum computer not a quantum computer? D-Wave Systems has been seen as a kind of bad boy of quantum computing for a few years. Its approach, known as annealing, doesn't involve operating quantum versions of logic gates. Instead, its machines allow thousands of superconducting qubits to interact in loosely defined ways. "What that means is that we can scale much more rapidly," says Alan Baratz, D-Wave's CEO. To use a D-Wave quantum computer, you first formulate your question in a way that mirrors looking for the lowest point in a landscape. Asking the right question kicks the qubits into a high-energy state where they occupy many quantum states at once. If the question is correctly formulated, it is akin to simultaneously occupying all the points in the landscape of answers. As the qubits settle toward their lowest energy state, they reveal the lowest point in the landscape — the required answer. Criticisms have arisen because D-Wave's is not a general purpose machine: Relatively few problems can be efficiently solved this way. Still, it has plenty of fans, including users at NASA, Volkswagen, Lockheed Martin and Oak Ridge National Laboratory. "We're seeing interesting results," says Travis Humble, director of ORNL's Quantum Computing Institute. "I can test much larger problem sizes than I could on devices that only have 50 qubits."

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“Every rose has its thorn,” the song goes, but not every rose has electronic wires running through its body. The futuristic idea of plant cyborgs is making the leap from science fiction to real-world science. What’s the big deal? Swedish researchers have been working on ways to regulate plant growth, using electronic wires grown inside the plants own nutrient channels to host sensors and drug-delivery systems. The aim is to provide just the right amount of plant hormones at just the right time. Such efforts could provide even more precise human control over plant production and agriculture. A separate but no less exciting project involves embedded biofuel cells that could literally turn plants into solar power plants. If all goes well, sensors and other devices could someday harvest electricity from the natural process of photosynthesis that enables plants to turn sunlight into chemical energy. It’s not often that such a sweet-smelling prospect begins with a humble garden rose. But that’s where the first successful steps toward electronic plants has begun. A team at Linköping University in Sweden has taken a huge step forward with the first experiments demonstrating electronic circuits within the living bodies of plant stems and leaves. Their research is detailed in the 20 November 2015 issue of the journal Science Advances. They grew electronic wires as long as 10 centimeters within garden rose stems and turned leaves into patchy electronic displays capable of changing colors between light and dark on demand. They also built working transistors—the basic switches at the heart of modern electronics—based on the wires embedded within the plants. “In a sense, we are then introducing a nervous system into the plants,” says Magnus Berggren, a professor of organic electronics at Linköping University in Sweden. But the researchers didn’t perform Frankenstein-style surgery to implant the wires. Instead, they made use of the xylem, plants’ natural system of channels that typically carry water and nutrients from the roots to stems, leaves, and flowers. The team’s early attempts to thread conductive polymer wires through the xylem led to the xylem being clogged or the plants exhibiting severe toxic reactions. But the researchers eventually discovered that a liquid solution containing a polymer called poly(3,4-ethylenedioxythiophene), or PEDOT, could readily be taken up by the xylem and distributed evenly throughout. What’s more, they found, it would eventually form a solid wire capable of conducting electricity. The presence of such “xylem wires” still allows the channels to carry the necessary water and nutrients for plant survival. Berggren explained how the liquid solution containing dissolved chains of PEDOT-S:H—a chemical variation of PEDOT—was able to form solid wires with the help of both the xylem’s vascular channels and the plants’ delayed immune response: After some time, the plant reacts against this unknown material. A common reaction against pathogens or toxic materials involves exchange of monovalent ions with divalent ones. The increase of divalent ions promote self-organization and formation of the actual conducting wires along the inner walls of the xylem channels. In a sense, the plant is helping us to separate the the event of distribution of the conducting and electronic materials from the event of film formation along the xylem walls. Successful creation of the xylem wires also allowed the researchers to create “organic electrochemical transistors” within the plants; these transistors convert chemical signals into electronic outputs. Such transistors could form the basic hardware for more sophisticated plant cyborg devices. The team even used the plant circuitry to demonstrate digital logic gates—the building blocks for performing more complex electronic and computing operations. Other experiments turned the leaves of roses into living electronic displays. The Swedish researchers accomplished this by encapsulating a leaf in a syringe filled with a different PEDOT solution. When the syringe’s plunger was pulled up, it created a vacuum that sucked gas out of the leaf through the “stomata” pores on the leaf surface. Once the syringe plunger was pushed down, the PEDOT solution rushed into the pores to fill the spaces between the leaf’s veins. The result was a patchy network of conductive material within the leaf. Researchers sandwiched the leaves between PEDOT films to create electrical contacts with the PEDOT inside the leaves. That enabled the team to remotely manipulate the material within the leaves, changing their color between lighter and darker patterns. The switch between light and dark typically took about 20 seconds. The researchers observed that a pattern, whether light or dark, would remain visible for about 10 minutes. The researchers mostly experimented with cut rose stems and leaves, but what works in garden roses could also help create other electronic plants, Berggren said. The basic structure of roses resembles those of larger plants such as trees, which means trees could also theoretically become living plant cyborgs or “e-plants.” Jeremy Hsu has been working as a science and technology journalist in New York City since 2008. He has written on subjects as diverse as supercomputing and wearable electronics for IEEE Spectrum. When he’s not trying to wrap his head around the latest quantum computing news for Spectrum, he also contributes to a variety of publications such as Scientific American, Discover, Popular Science, and others. He is a graduate of New York University’s Science, Health & Environmental Reporting Program.

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We all have an idea of how the internet works: Packets of data and communication are transmitted across interconnected devices using a routing network that follows Transport Control protocol and Internet Protocol. This data is sent electronically via copper wires, by bursts of light via optical fibers, or wirelessly via microwaves. However, the internet network as we know it is what scientists actually consider “classical.” And that’s because there is a more advanced way of securing data transfer: quantum network. What is a Quantum Network? A quantum network is an internet network that makes use of the properties of photons to transmit data. It allows quantum devices to exchange information within a particular environment that harnesses the principles of quantum mechanics. As such, it would be difficult to understand what a quantum network is or how quantum internet works without a basic understanding of quantum physics. Quantum mechanics describes the physical properties of nature at the atomic (and subatomic particle) scale. In very simple terms, this branch of quantum physics governs the laws of the very small. And photons are the smallest quantum of electromagnetic fields, including light and radio waves. It is the smallest energy packet of electromagnetic radiation. Also read: The Evolution of Data Centers Lies in Digital Transformation How Do Quantum Networks Work? A quantum network would allow the ultra secure transmission and exchange of quantum communications between distinct quantum endpoints or devices over fiber optic cables. Quantum devices will use their own “qubits” or quantum bits — the equivalent of bits used by ordinary computers but can be in a superposition of both ‘0’ and ‘1.’ Information is stored in these qubits, which are encoded keys that are typically polarized photons. These photons can travel very easily along fiber optic cables. If there is an attempt to intercept the encoded keys, the delicate quantum state of the qubits will be destroyed, along with the data they hold. When such intrusion happens, the endpoints will be alerted. The ability to detect any intrusion lends the quantum network unprecedented capabilities that are rather impossible for today’s web applications to carry out. Moreover, quantum networks apply uniquely quantum phenomena, such as no-cloning, entanglement, and superposition. These phenomena are not available to ordinary internet networks. Photons exist in a superposition of all their possible quantum states and when they are measured, they are forced to select one of these states. Unfortunately, a quantum state can’t be measured without any disturbance, thus betraying any attempt at measurement. An unknown quantum state also cannot be copied or cloned. Therefore, a well-designed quantum network is inherently safe from this behavior. You may be wondering, though, how quantum communication can be amplified in order to reach its recipients from afar if a photon cannot be copied or duplicated. Thanks to entanglement, which is another quantum phenomenon, the range of quantum networks can be extended. A quantum network’s main purpose is to enable qubits on one device to be entangled with the qubits on another device. This entanglement serves many potential purposes, including encryption. Measurements on entangled photons are always correlated with each other, so repeatedly reading the qubits’ quantum states allows users to create a secret code. This correlation of entangled photons applies regardless of how far apart they are. As such, quantum network repeaters that apply entanglement to extend a quantum network’s range can be developed. The Benefits of Quantum Networks We have already established that quantum networks are ultra secure and are impervious to any kind of cyberhacking. Encrypted messages will be impossible to intercept. However, aside from the assurance of security, quantum internet can transmit large volumes of information across wide distances at a much faster speed than classical networks are capable of. This could be revolutionary for apps and software, as well as for any updates they need to deploy over the air. Also read: Networking 101: NVMe over TCP What Sectors Will Benefit Most from Quantum Internet? The financial sector will greatly benefit from using quantum internet, especially in securing online banking transactions. Consumers will feel safer and confident sharing personal data and doing their banking and financial activities online because of this promise of security. Other sectors that will benefit greatly from using quantum internet include the public and healthcare sectors. A faster and safer internet will help these sectors expedite their processes and provide services promptly. Quantum computing will also allow organizations under this sector to solve complex problems and to conduct large-scale experiments and studies. Quantum Networks Now Quantum networks are still in the experimental stage and tech companies are still starting to build them. IT professionals, researchers, academics, and other experts in the field are still developing devices that are essential for a quantum network infrastructure, including quantum routers, gateways, hubs, repeaters, and other tools. Also, recently, the United States Department of Energy (DOE) published the first blueprint laying out its step-by-step strategy on how to realize the quantum internet dream. It is expected that this particular project will be granted federal funding of nearly $625 million. Quantum Networks in the Future Once quantum internet takes off, we can expect the birth of a whole new industry. Of course, classical or ordinary internet will remain and they will exist side by side. While we can expect large organizations to utilize quantum networks to safeguard the large volume of valuable data they have in their possession, individual consumers are most likely to continue using classical internet. This isn’t surprising considering that quantum internet is a new technology and will likely be expensive in the beginning. In addition to slow adoption because of the expense of overhauling current classic systems, there’s also the fact that it takes time for people to adapt to new technologies. This lack of urgency is also rooted in the “if-it-ain’t-broke-why-fix-it” attitude that consumers often initially have when new technologies are introduced. However, in time, quantum internet will become more accessible and more affordable to a growing number of people. The longer it is used, the more commonplace and mainstream it will become. Read next: Networking 101: Understanding SASE

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Yaniv Erlich, a core member of the New York Genome Center and associate professor of Computer Science and Computational Biology at Columbia University, holds a small three-dimensionally (3D)-printed bunny in front of his webcam. The toy, he says, is actually a storage device. "The plastic fibers in the bunny have silica beads," he says, "and inside these beads is DNA that encodes a file with instructions on how to print an exact replica of this bunny." As with a real rabbit, the 3D-printed toy, developed with chemical engineer Robert Grass at ETH Zürich, carries its own blueprints in the DNA within it. "You can chop off any part of the bunny," Erlich explains, "and there's DNA in every piece, and you can amplify it and print a new bunny. We think we can replicate them to about 1021, or enough bunnies for everyone in the world until the end of humanity." The project is less about toymaking than it is about the transformative potential of DNA data storage. DNA boasts a rare combination of durability, low energy consumption, and phenomenal density. "We estimate that a DNA system could store one exabyte per cubic inch," says computer scientist Karin Strauss, a principal research manager at Microsoft. By using a DNA data storage system, she said, "What requires a whole datacenter to store today would fit in the palm of your hand." On a basic level, DNA storage involves taking the four basic molecules in DNA—adenine, thymine, cytosine, guanine, or A, T, C, and G—and mapping them to sequences of bits, so "A" might correspond to 00 and "T" to 01. Scientists take a sequence of bits and synthesize and store DNA that represents those bits. Strauss, computer scientist Luis Ceze of the University of Washington, and their interdisciplinary team recently developed a fully automated, end-to-end system. Previous systems required help from chemists and other scientists, but the new prototype automatically encodes the bits, makes the DNA, stores that DNA, retrieves and reads it, and then returns the data. In the first iteration, they stored the word 'hello'. "It is by no means a high-performance system," Strauss says. "It was intended to be a first demonstration that automation in DNA data storage is indeed possible, end to end. But the maturity will improve. Eventually we could see DNA storage devices that look like racks, but with fluidics components, inside datacenters." Strauss and Ceze recently were named to share the 2020 Maurice Wilkes Award, for their work on DNA-based digital data storage. Another recent breakthrough focused on efficiently reading and retrieving DNA-stored data. Computer engineer James M. Tuck, chemical engineer Albert Keung, and their colleagues at North Carolina State University recently published a paper detailing their novel approach, which they call Dynamic Operations and Reusable Information Storage, or DORIS. The technique employs what they call a toehold system, in which a single-stranded piece of DNA is attached to a double-stranded section that stores data. The single strand, or toehold, effectively carries the file name, or identifying information, which allows them to efficiently search for specific DNA data. Once they retrieve a file, they make RNA copies of the DNA and its stored data, then return the original DNA to the storage medium undamaged. Previous systems relied on more involved chemistry or molecular manipulations that could degrade the stored data in the long run. The system holds great potential, says Tuck, for a very dense, resilient storage system. "In a relatively small space, we'd be able to store lots of information, label it with distinct addresses, and pull out the information we want while having minimal degradation on the library that's there," he says. As for applications, the storage density and durability of DNA make it ideal for archival storage, according to Strauss, who suspects the first iterations might appear in the controlled environment of a datacenter. Erlich has additional applications in mind. In the future, car parts could be embedded with DNA that harbors data on how to manufacture the component, should it become obsolete. An artificial knee or hip could contain a patient's relevant medical details, so doctors operating on the prosthetic in the future could easily recover important health information. Tuck adds that it would be a waste not to find a way to compute on DNA-stored data where it resides, and Strauss and Ceze have made advances in that area. Keung, meanwhile, hopes that instead of choosing a particular system, researchers will continue to explore creative approaches. "We are at this inflection point with how we build computers right now, with the end of Moore's Law in sight, and different efforts into quantum computing and molecular computing," says Ceze. "It's becoming increasingly clear that these approaches are all good at different things, and we need to develop this portfolio of new technologies to ensure we can continue building better computers." Gregory Mone is a Boston-based science writer and the author, with Bill Nye, of Jack and the Geniuses: At the Bottom of the World. No entries found

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Physicists push limits of Heisenberg Uncertainty Principle - New experiments with vibrating drums push the boundaries of quantum mechanics. - Two teams of physicists create quantum entanglement in larger systems. - Critics question whether the study gets around the famous Heisenberg uncertainty principle. Recently published research pushes the boundaries of key concepts in quantum mechanics. Studies from two different teams used tiny drums to show that quantum entanglement, an effect generally linked to subatomic particles, can also be applied to much larger macroscopic systems. One of the teams also claims to have found a way to evade the Heisenberg uncertainty principle. One question that the scientists were hoping to answer pertained to whether larger systems can exhibit quantum entanglement in the same way as microscopic ones. Quantum mechanics proposes that two objects can become “entangled,” whereby the properties of one object, such as position or velocity, can become connected to those of the other. An experiment performed at the U.S. National Institute of Standards and Technology in Boulder, Colorado, led by physicist Shlomi Kotler and his colleagues, showed that a pair of vibrating aluminum membranes, each about 10 micrometers long, can be made to vibrate in sync, in such a way that they can be described to be quantum entangled. Kotler’s team amplified the signal from their devices to “see” the entanglement much more clearly. Measuring their position and velocities returned the same numbers, indicating that they were indeed entangled. Evading the Heisenberg uncertainty principle? Another experiment with quantum drums — each one-fifth the width of a human hair — by a team led by Prof. Mika Sillanpää at Aalto University in Finland, attempted to find what happens in the area between quantum and non-quantum behavior. Like the other researchers, they also achieved quantum entanglement for larger objects, but they also made a fascinating inquiry into getting around the Heisenberg uncertainty principle. The team’s theoretical model was developed by Dr. Matt Woolley of the University of New South Wales. Photons in the microwave frequency were employed to create a synchronized vibrating pattern as well as to gauge the positions of the drums. The scientists managed to make the drums vibrate in opposite phases to each other, achieving “collective quantum motion.” The study’s lead author, Dr. Laure Mercier de Lepinay, said: “In this situation, the quantum uncertainty of the drums’ motion is canceled if the two drums are treated as one quantum-mechanical entity.” This effect allowed the team to measure both the positions and the momentum of the virtual drumheads at the same time. “One of the drums responds to all the forces of the other drum in the opposing way, kind of with a negative mass,” Sillanpää explained. Theoretically, this should not be possible under the Heisenberg uncertainty principle, one of the most well-known tenets of quantum mechanics. Proposed in the 1920s by Werner Heisenberg, the principle generally says that when dealing with the quantum world, where particles also act like waves, there’s an inherent uncertainty in measuring both the position and the momentum of a particle at the same time. The more precisely you measure one variable, the more uncertainty in the measurement of the other. In other words, it is not possible to simultaneously pinpoint the exact values of the particle’s position and momentum.Heisenberg’s Uncertainty Principle Explained.Credit: Veritasium / Youtube.com Big Think contributor astrophysicist Adam Frank, known for the 13.8 podcast, called this “a really fascinating paper as it shows that it’s possible to make larger entangled systems which behave like a single quantum object. But because we’re looking at a single quantum object, the measurement doesn’t really seem to me to be ‘getting around’ the uncertainty principle, as we know that in entangled systems an observation of one part constrains the behavior of other parts.” Ethan Siegel, also an astrophysicist, commented, “The main achievement of this latest work is that they have created a macroscopic system where two components are successfully quantum mechanically entangled across large length scales and with large masses. But there is no fundamental evasion of the Heisenberg uncertainty principle here; each individual component is exactly as uncertain as the rules of quantum physics predicts. While it’s important to explore the relationship between quantum entanglement and the different components of the systems, including what happens when you treat both components together as a single system, nothing that’s been demonstrated in this research negates Heisenberg’s most important contribution to physics.” The papers, published in the journal Science, could help create new generations of ultra-sensitive measuring devices and quantum computers.

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Quantum Computing: Understanding the Basics and Its Potential Impact Quantum computing is an emerging field of technology with the potential to revolutionize the way computing is done. It promises incredible performance boosts, faster and more efficient computing, and even the ability to process information in ways that are impossible right now. But what is quantum computing, and what impact could it have? What is Quantum Computing? Quantum computing is a method of computing that takes advantage of the rules of quantum mechanics. It uses qubits instead of classical bits, which enables them to hold much more data than normal binary bits. This gives quantum computers the ability to perform operations at a much faster speed than traditional computers. The Potential of Quantum Computing Quantum computers have the potential to revolutionize many industries and fields. Here are some of the potential applications of quantum computing: - Data Analysis: Quantum computers are capable of sifting through large datasets and discovering patterns that are too complex for traditional computers to uncover. - Cryptography: Quantum computers can be used to securely encrypt data, making it virtually impossible for hackers to break through. - Artificial Intelligence: Quantum machines are able to look at data in a truly unique way, making them ideal for advanced AI applications. The Challenges Ahead Despite its amazing potential, quantum computing is still in its infancy. The technology is complex and challenging, and a lot of work still needs to be done to perfect it. There are also numerous challenges that need to be addressed, such as security and reliability. The Future of Quantum Computing While the challenges ahead of quantum computing may be daunting, its potential is undeniable. It could profoundly change the way we think about computing, making it possible to solve problems that are currently impossible. The future of quantum computing is sure to be an exciting one. What is quantum computing basics? Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers. Today, IBM Quantum makes real quantum hardware — a tool scientists only began to imagine three decades ago — available to hundreds of thousands of developers. Quantum computing works on the principle of quantum logic. It uses qubits — particles like electrons and photons — instead of the binary digits (bits) used in classical computing. These qubits can exist in multiple states simultaneously and represent both 1s and 0s at the same time. As a result, a single qubit can contain much more information than a classical bit and make calculations faster and more complex. What is the impact of quantum computing? With computing power based on the known universe’s power, quantum computers will do the math at a speed of 158,000,000 times faster than conventional computers. They can solve a computation in four minutes that would take today’s computers thousands of years to solve. This fact alone poses a great number of opportunities. The wide range of potential applications include machine learning, big data analysis, artificial intelligence, simulation of organic molecules, and the simulation of complex financial models. Quantum computing can enable the development of smarter and faster research, faster transportation, the development of the Internet of Things (IoT), and will lead to revolutionary medical breakthroughs. For example, it could enable us to develop new medicines and treatments faster than ever before. Along with the advancement of medical science, quantum computing could help in the development of new materials and substances. In the world of finance, quantum computing could have a dramatic impact on the ability to optimize markets, portfolios, and transactions. This could lead to the development of smarter trading systems and improved predictive analytics. Finally, quantum computing could help to safeguard national information and improve cybersecurity. It has been suggested that quantum computers could drastically reduce the security risks posed by hackers because quantum computing could break current encryption algorithms. What is the potential of quantum computing? By solving problems with more accuracy and speed than digital computers, quantum computers have the potential to accelerate scientific discovery and innovation, revolutionize financial market modeling and simulations, and empower machine learning and artificial intelligence. It could also create far more secure databases and networks, allowing for encrypted machines that can’t be hacked. In the future, quantum computers have the potential to lead to revolutionary advancements in encryption and computing power that revolutionize the way we interact with technology. What impact will quantum computing have on humans lives? Quantum computing has many potential uses, such as quantum engineering, cryptography, machine learning, artificial intelligence, simulations, and optimizations. It could speed up drug discovery and help with medical research by speeding up chemical reactions or protein folding simulations. Quantum computing could also be used for efficient data storage, data security, and quantum communications. Quantum computing could revolutionize the cryptocurrency market and be used for the secure sharing of data. It could also be used to process large amounts of data quickly, which could help with decision making and risk management. Ultimately, quantum computing could help improve our lives in many ways by helping to make data more secure, advancing medical research, and enabling more efficient and powerful calculation. There are no reviews yet.

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SEPTEMBER 8, 2021 — A UTSA researcher is part of a collaboration that has set a world record for innovation in quantum computing. The accomplishment comes from R. Tyler Sutherland, an assistant professor in the College of Sciences’ Department of Physics and Astronomy and the College of Engineering and Integrated Design’s Department of Electrical Engineering, who developed the theory behind the record-setting experiment. Sutherland and his team set the world record for the most accurate entangling gate ever demonstrated without lasers. According to Sutherland, an entangling gate takes two qubits (quantum bits) and creates an operation on the secondary qubit that is conditioned on the state of the first qubit. “For example, if the state of qubit A is 0, an entangling gate doesn’t do anything to qubit B, but if the state of qubit A is 1, then the gate flips the state of qubit B from 0 to 1 or 1 to 0,” he said. “The name comes from the fact that this can generate a quantum mechanical property called ‘entanglement’ between the qubits.” Sutherland adds that making the entangling gates in your quantum computer “laser-free” enables more cost-effective and easier to use quantum computers. He says the price of an integrated circuit that performs a laser-free gate is negligible compared to the tens of thousands of dollars it costs for a laser that does the same thing. “Laser-free gate methods do not have the drawbacks of photon scattering, energy, cost and calibration that are typically associated with using lasers,” Sutherland explained. “This alternative gate method matches the accuracy of lasers by instead using microwaves, which are less expensive and easier to calibrate.” This quantum computing accomplishment is detailed in a paper Sutherland co-authored titled, “High-fidelity laser-free universal control of trapped-ion qubits.” It was published in the scientific journal, Nature, on September 8. Quantum computers have the potential to solve certain complex problems exponentially faster than classical supercomputers. One of the most promising uses for quantum computers is to simulate quantum mechanical processes themselves, such as chemical reactions, which could exponentially reduce the experimental trial and error required to solve difficult problems. These computers are being explored in many industries including science, engineering, finance and logistics. “Broadly speaking, the goal of my research is to increase human control over quantum mechanics,” Sutherland said. “Giving people power over a different part of nature hands them a new toolkit. What they will eventually build with it is uncertain.” That uncertainty, says Sutherland, is what excites him most. Sutherland’s research background includes quantum optics, which studies how quantum mechanical systems emit light. He earned his Ph.D. at Purdue University and went on to Lawrence Livermore National Laboratory for his postdoc, where he began working on experimental applications for quantum computers. He became a tenure-track assistant professor at UTSA last August as part of the university’s Quantum Computation and Quantum Information Cluster Hiring Initiative. UTSA Today is produced by University Communications and Marketing, the official news source of The University of Texas at San Antonio. Send your feedback to firstname.lastname@example.org. Keep up-to-date on UTSA news by visiting UTSA Today. Connect with UTSA online at Facebook, Twitter, Youtube and Instagram. Come join us at "Taste of Success" in the Loefller Room to hear about the opportunities that the Department of Physics and Astronomy has to offer with guest speaker Dr. Chris Packham.Loeffler Room (BSB 3.03.02,) Main Camus Learn to use the simple but powerful features of EndNote, a citation management tool. In this hands-on workshop, participants will learn to setup an EndNote library, save references and PDFs, and automatically create and edit a bibliography.Virtual event Do you professors require the use of BibTex or LaTex to format your references? This workshop will cover where to get your references in BibTex format, import them into a LaTex editor, and much more.Virtual event Join us as we raise awareness to the topic of child abuse through Cardboard Kids decoration. We have basic art supplies, feel free to bring extra embellishments. This will be in indoor space next to FreeBirds. Trigger Warning: This event is intended to raise awareness of child abuse, child abuse will be a topic of conversation.Outdoor Learning Environment 1 (OLE,) Student Union, Main Campus Join the UTSA community in celebrating the life of Dr. Thelma Duffey.Aula Canaria (BV 1.328), Buena Vista Building, Downtown Campus The proposed annual BME Research Symposium will allow students to present their undergraduate research free of charge, providing them with the opportunity to network and build their professional skills.H-E-B Student Union Ballroom 1 & 2, Main Campus The UTSA Marches Committee, in partnership with the Cesar E. Chavez Legacy and Education Foundation, invites everyone to the 27th annual Cesar E. Chavez March for Justice. This event is in conjunction with the "Yes We CAN" food donation drive with the San Antonio Food Bank. Guests are encouraged to bring canned food items with them to the march to deposit cans into barrels before the march begins.1310 Guadalupe St, San Antonio, TX 78207 The University of Texas at San Antonio is dedicated to the advancement of knowledge through research and discovery, teaching and learning, community engagement and public service. As an institution of access and excellence, UTSA embraces multicultural traditions and serves as a center for intellectual and creative resources as well as a catalyst for socioeconomic development and the commercialization of intellectual property - for Texas, the nation and the world. To be a premier public research university, providing access to educational excellence and preparing citizen leaders for the global environment. We encourage an environment of dialogue and discovery, where integrity, excellence, inclusiveness, respect, collaboration and innovation are fostered. UTSA is a proud Hispanic Serving Institution (HSI) as designated by the U.S. Department of Education. The University of Texas at San Antonio, a Hispanic Serving Institution situated in a global city that has been a crossroads of peoples and cultures for centuries, values diversity and inclusion in all aspects of university life. As an institution expressly founded to advance the education of Mexican Americans and other underserved communities, our university is committed to ending generations of discrimination and inequity. UTSA, a premier public research university, fosters academic excellence through a community of dialogue, discovery and innovation that embraces the uniqueness of each voice.

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How Does a Dilution Refrigerator Work? A cryogen-free dilution refrigerator is a closed-loop cooling system that provides cooling to temperatures of millikelvins – colder than outer space. The system is used to cool down samples and devices, which are attached to a metallic flange in the dilution refrigerator. The systems are used for instance in quantum computing, materials science, astrophysics, and fundamental research. The dilution refrigerator systems can provide temperatures of < 10 millikelvin and can operate without moving parts at the low temperature stages. This is enabled by the dilution unit inside the system, which provides necessary cooling to reach these ultra-low temperatures. The cooling power of the dilution unit comes from the heat of mixing of the mixture of helium-3 (He-3) and helium-4 (He-4) isotopes. This is enabled by the peculiar property of helium, that its two isotopes can remain dissolved down to the lowest temperatures, whereas other fluids tend to separate completely at sufficiently low temperature. Phase Separation of Helium Isotopes He-3 and He-4 represent two different fundamental particles. He-3 is a fermion, while He-4 is a boson. Bosons can undergo a phenomenon called Bose-Einstein condensation, where multiple particles can occupy the lowest quantum mechanical energy state. This phenomenon is responsible for the onset of superfluidity of He-4 at 2.17 kelvin under saturated vapor pressure. For fermions, on the other hand, such phenomenon is not possible since only two fermions (with opposite spins) are allowed to occupy same quantum mechanical energy state. The superfluid state in He-3 is thus much more difficult to achieve, and it does not happen in the operational temperature range of the dilution refrigerator. The normal fluid He-3 is also called fermi fluid. A dilution refrigerator uses the heat of mixing of those two isotopes of helium, He-3 and He-4, to obtain cooling. At temperatures below 0.87 kelvin (exact temperature depends on the He-3 concentration) the He-3– He-4 mixture will separate into two phases: an He-3 rich phase (concentrated phase) and an He-3 poor phase (dilute phase). Phase diagram of helium-3—helium-4 mixture. Approaching absolute zero temperature, the concentrated phase becomes pure He-3 while in the dilute He-4 rich phase there remains 6.6% of He-3. The enthalpy of He-3 in the dilute phase is larger than in the concentrated phase. Hence energy is required to move He-3 atoms from the concentrated to the dilute phase. In a dilution refrigerator this energy is taken from a well isolated environment so cooling will occur. Essentially the cooling provided by the dilution unit is based on the He-3 requiring heat when pumped into the dilute phase, which provides cooling in the environment this happens in. Operation of the Dilution Unit In the dilution refrigerator, the isolated environment where the mixing of the isotopes happens is called the Mixing Chamber. That’s where the phase boundary is located, and where the cooling occurs when the He-3 is pumped through the phase boundary. Other essential parts of the dilution unit are the still chamber, the continuous flow heat exchanger (in the form of a spiral), and the step heat exchangers. In a steady state operation, He-3 comes to the dilution unit pumped with a gas handling system. It enters the dilution unit precooled first by the pulse tube cryocooler down to about 3 kelvin, and through the main flow impedance in the still chamber. From there it proceeds to the continuous flow heat exchanger and then to the step heat exchangers, which cool the He-3 going to the mixing chamber. From the mixing chamber the He-3 goes into the still chamber and in a gas phase is evaporated through a still pumping line, eventually coming back to the start of the process. Below you can see a diagram of the cooling cycle. Dilution refrigerator cooling cycle. 1. He-3-rich gas phase, 2. Still, 3. Heat exchangers, 4. He-3-poor phase, 5. Mixing Chamber, 6. Phase separation, and 7. He-3-rich phase. The efficiency of the dilution refrigerator is determined by the efficiency of the heat-exchangers. The incoming He-3 should be cooled by the outgoing He-3 as much as possible. The available cooling power is determined by the circulation rate of He-3. The larger the flow, the larger the cooling power, provided that the heat-exchangers are capable of handling the increased flow rate. The temperature of the still and mixing chamber plate are controlled by heaters. The mixing chamber has a heater, which is there for diagnostics purposes; it can be used to characterize unit behaviour under various heat loads, i.e., simulate an installed experiment. The still heater on the other hand is essential to the unit operation. Without heating, the vapor pressure in the still chamber becomes so small, that pumps cannot effectively circulate He-3, resulting in reduced cooling power. Hence, heat must be applied to the still to increase evaporation. As He-3 has larger vapor pressure than He-4, this process distils He-3 out of the mixture (the He-3 concentration in the gas phase is ~90%) After the He-3 gas evaporates from the still, it is pumped through a gas handling system (GHS), in which it is purified and then allowed back into the condensing line. Dilution unit. 1. Still, 2. Continuous flow heat exchangers, 3. Step heat exchangers, and 4. Mixing chamber. The entire dilution refrigerator consists of the different temperature stages, with the dilution unit located in the lowest stages. The stages are easily recognizable as they are made of large metallic plates. The stages are separated by non-conductive supports and heat switches, whose conductivity can be controlled. Using them the stages can be thermally connected or disconnected. The dilution unit is attached to three of these metallic plates. The still chamber of the dilution unit sits on top of the still flange, under that and after the continuous flow heat exchanger there is the cold plate, and the mixing chamber is located on top of the mixing chamber flange. Finally, below that there is the experimental space enabling measurements in millikelvin temperatures. All these stages will have temperature sensors to provide the user information about the temperatures at different stages. Cooling With a Push of a Button For the user of a dilution refrigerator all this cooling power is provided with a push of a button providing ease of use, with no need to understand all the mechanics proving this cooling. But for those that are curious, you now know that the dilution unit is the heart of the dilution refrigerator and enables it to provide the lowest temperatures for research and applications. There are however numerous other components to the dilution refrigerator measurement system. Somehow all the helium in the system has to move around it and be precooled to the temperatures required for the dilution unit operations. We must also protect all of this from the outside environment to keep everything running efficiently. To learn about all that, read our blog on the components of the dilution refrigerator measurement system.

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The general public might think of the 21st century as an era of revolutionary technological platforms, such as smartphones or social media. But for many scientists, this century is the era of another type of platform: two-dimensional materials, and their unexpected secrets. These 2-D materials can be prepared in crystalline sheets as thin as a single monolayer, only one or a few atoms thick. Within a monolayer, electrons are restricted in how they can move: Like pieces on a board game, they can move front to back, side to side or diagonally — but not up or down. This constraint makes monolayers functionally two-dimensional. The 2-D realm exposes properties predicted by quantum mechanics — the probability-wave-based rules that underlie the behavior of all matter. Since graphene — the first monolayer — debuted in 2004, scientists have isolated many other 2-D materials and shown that they harbor unique physical and chemical properties that could revolutionize computing and telecommunications, among other fields. For a team led by scientists at the University of Washington, the 2-D form of one metallic compound — tungsten ditelluride, or WTe2 — is a bevy of quantum revelations. In a paper published online July 23 in the journal Nature, researchers report their latest discovery about WTe2: Its 2-D form can undergo “ferroelectric switching.” They found that when two monolayers are combined, the resulting “bilayer” develops a spontaneous electrical polarization. This polarization can be flipped between two opposite states by an applied electric field. “Finding ferroelectric switching in this 2-D material was a complete surprise,” said senior author David Cobden, a UW professor of physics. “We weren’t looking for it, but we saw odd behavior, and after making a hypothesis about its nature we designed some experiments that confirmed it nicely.” Materials with ferroelectric properties can have applications in memory storage, capacitors, RFID card technologies and even medical sensors. “Think of ferroelectrics as nature’s switch,” said Cobden. “The polarized state of the ferroelectric material means that you have an uneven distribution of charges within the material — and when the ferroelectric switching occurs, the charges move collectively, rather as they would in an artificial electronic switch based on transistors.” The UW team created WTe2 monolayers from its the 3-D crystalline form, which was grown by co-authors Jiaqiang Yan at Oak Ridge National Laboratory and Zhiying Zhao at the University of Tennessee, Knoxville. Then the UW team, working in an oxygen-free isolation box to prevent WTe2 from degrading, used Scotch Tape to exfoliate thin sheets of WTe2 from the crystal — a technique widely used to isolate graphene and other 2-D materials. With these sheets isolated, they could measure their physical and chemical properties, which led to the discovery of the ferroelectric characteristics. WTe2 is the first exfoliated 2-D material known to undergo ferroelectric switching. Before this discovery, scientists had only seen ferroelectric switching in electrical insulators. But WTe2 isn’t an electrical insulator; it is actually a metal, albeit not a very good one. WTe2 also maintains the ferroelectric switching at room temperature, and its switching is reliable and doesn’t degrade over time, unlike many conventional 3-D ferroelectric materials, according to Cobden. These characteristics may make WTe2 a promising material for smaller, more robust technological applications than other ferroelectric compounds. “The unique combination of physical characteristics we saw in WTe2 is a reminder that all sorts of new phenomena can be observed in 2-D materials,” said Cobden. Ferroelectric switching is the second major discovery Cobden and his team have made about monolayer WTe2. In a 2017 paper in Nature Physics, the team reported that this material is also a “topological insulator,” the first 2-D material with this exotic property. In a topological insulator, the electrons’ wave functions — mathematical summaries of their quantum mechanical states — have a kind of built-in twist. Thanks to the difficulty of removing this twist, topological insulators could have applications in quantum computing — a field that seeks to exploit the quantum-mechanical properties of electrons, atoms or crystals to generate computing power that is exponentially faster than today’s technology. The UW team’s discovery also stemmed from theories developed by David J. Thouless, a UW professor emeritus of physics who shared the 2016 Nobel Prize in Physics in part for his work on topology in the 2-D realm. Cobden and his colleagues plan to keep exploring monolayer WTe2 to see what else they can learn. “Everything we have measured so far about WTe2 has some surprise in it,” said Cobden. “It’s exciting to think what we might find next.”

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Secrets of the superfast computers of tomorrow Image 1 of 14 The thorniest problems, solved in days Qubits allow multiple states to be simultaneously stored. The process, known as superposition, gives quantum computers (envisioned in this concept art) exponentially faster processing speeds. Problems that would take traditional computers millions of years to solve could instead take days. But they can't take the heat One of the challenges, however, is building a chip that can support multiple qubits. The problem: Quantum computers need to operate at near-absolute zero temperatures. Right now, D-Wave is the only quantum computing company that has managed to manufacture a processor with more than 1,000 qubits, a milestone that paves the way for further advancements in the field. Can we beat China? We may be years away from fully harnessing the power of quantum computers, but that hasn’t stopped the U.S. government from pursuing the next-best thing, a supercomputer that will be more than five times faster than China’s Tianhe-2, shown here. Get to know petaflops Contracted by the US Department of Energy, the supercomputer named Aurora will be able to reach a peak performance of 180 petaflops, or 180 quadrillion operations a second. Speed beyond reckoning Need help visualizing a quadrillion? An average American employee would have to work full-time for 250 million years to earn a quadrillion pennies. Aurora's potential: Pick a technology The Aurora will be primarily dedicated to scientific projects. Other potential uses: designing better batteries and solar panels, or improving transportation systems and wind turbines. The Aurora will be built by Intel and Cray Inc., and will cost $200 million to build. It’s expected to become operational by 2018. Harnessing the most powerful computer Most supercomputers are built to take on multiple scientific projects, but the Blue Brain Project has just one simple goal: to reverse-engineer the human brain and create a virtual brain in a supercomputer. Neuron by neuron Started in 2005 by the u00c9cole polytechnique fu00e9du00e9rale de Lausanne in Switzerland, the objective of the Blue Brain Project is to simulate each neuron of the human brain to better understand the brain and the development of neurological diseases. Here, Blue Brain scientist Ying Shi eyeballs a 3D animation of one such brain neuron. $1 billion brain Considering there are around 100 billion neurons in an average human brain, the scope of the simulation is staggering. Perhaps that’s why the project is starting smaller, with rat brain tissue, shown here undergoing a Blue Brain experiment. Thanks to a $1.3 billion grant from the European Commission in 2013, the project is well underway, with an estimated completion date in 2023. With so many supercomputers in operation worldwide (including Japan’s K Computer, shown here), it’s no wonder that the U.S. government is trying to research the next best thing. Partnering with IBM, Raytheon, and Northrop Grumman, IARPA, the super-secretive arm of the U.S. Department of Defense’s DARPA project, is doing just that. Make way for the exaflop Known as Cryogenic Computing Complexity, or C3, the project is expected to pave the way for exascale computing, which would allow computers to perform 1,000 petaflops, or 1 exaflop, a second. Unlike most modern supercomputers (like this petaflop computer in Germany), the C3 would involve superconductors that don’t need heavy-duty cooling solutions. Stone cold amazing Further, C3 would develop cryogenic memory, which as the name implies, would serve as a supercooled memory complement to the superconducted processors. If successful, C3 would seem light years ahead of today’s most amazing computers, such as NASA’s Pleiades. That said, it’s currently unclear when the first C3 supercomputer will see the light of day. Supercomputers at home What if, instead of being limited to scientists, researchers, and analysts, everyone had access to a supercomputer? That’s the thinking behind supercomputer.io, an online collaborative that crowdsources computing power from thousands of users who own a Parallella computer, a multi-core bare-bones computer sold by Adapteva. Processor pipe dreams It’s expected that supercomputer.io will grow its userbase, unlocking its potential to use tens of thousands and even a million cores, allowing it to tackle more complex problems. Until that day, supercomputers such as Switzerland’s Piz Daint, with its 36,096 cores (shown here), will just have to do.

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Every day, there seems to be a new advancement in computing–whether it’s OpenAI releasing ChatGPT AI, or Google announcing a breakthrough in quantum computing. Some researchers think that traditional computing is reaching its limits, despite all these advances. In order to create the next generation of technology, some scientists are getting inspiration from the world’s most powerful computer: the human brain. Biocomputing is a field that uses biological molecules such as DNA and cells to create hardware. The idea is that if we’re able to merge brain organoids, or clumps of neurons in a petri dish, with computing systems then we might be able to create computers with the operational power of the human mind. The concept isn’t exactly new. We’ve seen biocomputers in movies, books, and TV shows like Dune and The Terminator. There have also been limited instances of it in real life. In October 2022, a team of scientists were even able to demonstrate that a group of brain cells in a petri dish could “play” the video game Pong. DishBrain was a system that connected to a cluster of neurons. To move the paddle to hit the ball, the cells would send electrical signals to the computer to tell it what to do. Over time, the neurons were actually able to improve their Pong game–reducing the amount of times they missed the ball and increasing the amount of times they did. They were capable of adapting to the new environment and setting goals. While they might have mad gaming skills, a fully operational biocomputer still remains a bit of a white whale for biotechnologists. “Since the beginning of the computer era, engineering has aimed to emulate brain-like functionality, most obviously by striving for artificial intelligence,” Thomas Hartung, a professor of environmental health sciences at Johns Hopkins, told The Daily Beast. “Still, we are far away from achieving brain functionality [in a computer.]” Advances in brain organoids have shown that they’re able to replicate certain aspects of memory and even cognition while in a petri dish. Hartung leads a Johns Hopkins team that is creating the field of organoid Intelligence (OI) which describes developments in biocomputer technology and the systems involved. The group published a paper of their proposal in the journal Frontiers in Science on Feb. 28. The team believes that research into biocomputing would have a number of benefits outside of creating more advanced and powerful computers. It would also be more efficient in terms of energy and better for the environment. Frontier, one of the world’s most powerful supercomputers, was able to produce the computational capacity of a single human brain just last year, according to Hartung. However, it requires a “million times more energy” than our minds–not to mention $600 million. “The hope is that some of the remarkable functionalities of the human brain can be realized in OI such as its ability to make fast decisions based on incomplete and contradictory information (intuitive thinking), the continuous learning, and the data- and energy-efficiency,” Hartung explained. Additionally, Hartung claimed that the field of OI could also lead to the development of new treatments for neurological disorders like dementia or Alzheimer’s. The development of biocomputers requires research into the “biology of learning, memory, and other cognitive functions.” This will allow scientists to use brain organoids to potentially test for new drugs and treatments for cognitive decline. There are many ethical issues to be aware of when dealing with mini-brains. Issues surrounding potential sentience or self-awareness with biocomputers need to be addressed–which brings into question whether or not something like this should be pursued at all. What does it mean if the computer you’re using is essentially a human inside of a machine? Can it experience “pain?” What do we even consider sentient when it comes to computers anyway? What happens when a biocomputer crosses this line? To their credit, the team is incorporating ethicists into their OI discussions and “agreed on a concept of embedded ethics where they actually follow developments and observe the actual work in the laboratory,” Hartung said. However, ethical questions surrounding biocomputing are likely to remain as long as human brain cells continue being used. A fully functional biocomputer remains a distant reality. Hartung believes that it could take decades before OI is powerful enough to have the computational power of a mouse’s brain. However, the research to actually create a biocomputer will go a long way in not only creating the next generation of computers, but also potentially finding new treatments for some of the most destructive neurodegenerative conditions out there. And you don’t need the smartest brain to see why that’s good. The post How Human Brain Cells Might Someday Power Computers appeared first on The Daily Beast.

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Quantum Computing involves the use of several quantum phenomena to perform computations. One of these is entanglement. These phenomena help speed up exponentially the computational power, thus bringing computing to the next level as quantum computers operate at much higher speeds than classical computers. It also allows them to use less energy in performing the same operations as a classical one. Decoherence and quantum computing Quantum computers are very powerful, but they are also very fragile. When qubits interact with their environment, they decay and ultimately disappear in a process called decoherence. Decoherence is caused by a range of factors, including light, heat, sound, vibration, radiation, and even the act of measuring a qubit itself. While supercooled fridges and vacuum chambers are used to shield qubits from the outside world, errors still creep into quantum calculations. Technology is not yet sufficiently advanced to create a stable quantum computer that is broadly useful. Why does quantum computing matter? The need for knowledge has always engulfed humans and is the major driver of technological evolutions. From what started as an abacus and turned into high-end calculators for everyday use, computers have seen a similar advancement. Within three decades, computing powers changed from a mere five thousand addition problems (ENIAC) to millions of complex problems in a matter of seconds. This exponential advancement has not hindered the progress that humans still dream of achieving. Technological changes have always occurred whenever there has been a problem to solve. The work of decades upon decades in different technological areas has left little room for improvements. However, whenever existing technology was unable to solve the tasks at hand, humans have tried to resolve the issue with further advancement. One similar case has been that of Quantum Computing. When complexities ensued and classical computers could not answer the underlying questions, quantum computers were invented. Hence, a new era of advancement followed. Understanding quantum computing Modern computers encode information in bits that have a binary value. That is, the information can only take a value of 1 or 0. Quantum computers, on the other hand, utilize subatomic particles called quantum bits (qubits). Qubits possess some strange quantum properties. Connected qubits provide a significant increase in processing power when compared to the equivalent number of bits in a modern computer. The quantum properties responsible for this increased performance are: - Superposition – defined as the ability to exist in multiple states. Qubits can represent numerous possible combinations of 1 and 0 simultaneously. This enables quantum computers to rapidly assess a vast number of potential outcomes. Once a result has been calculated, the quantum state of qubits reverts to a binary state of either 1 or 0. - Entanglement. Qubits are said to be entangled when two members of a pair exist in a single quantum state. In other words, changing the state of one qubit will instantaneously change the state of the other. Scientists do not understand how or why entanglement occurs but adding entangled qubits to a quantum computer produces an exponential increase in computational power. How does quantum computing work? As the name suggests, Quantum Computing involves the use of several quantum phenomena to perform computations. One of these is entanglement. Quantum entanglement is basically a phenomenon that occurs when a group or a pair of particles interact or are in the same proximity but their quantum state cannot be determined independently of each other. Similarly, another phenomenon that is part of Quantum Computing is superposition. Superposition states that any two quantum states can be added or “superposed”. The result will be another quantum state. In the same way, this also entails that every quantum state is a sum of other quantum states which can be two or more in number. Quantum Computing uses these phenomena to perform faster computations than classical computers such as integer factorization. It is widely argued that whichever problems that quantum computers solve, can also be solved by classical computers. Alternatively, whichever problems can be solved by classical computers as well. The difference that exists between the two is the time that both take while solving the problems. This advantage of quantum computers over classical computers is known as “quantum supremacy”. Just like classical computers store information in the form of bits (0 or 1), quantum computers use what are known as “qubits”. As mentioned before, using phenomena like superposition and entanglement quantum computers are able to allow subatomic participles in more than one state. This means that at the same time it could be a 1 or a 0. This makes quantum computers operate at much higher speeds than classical computers. It also allows them to use less energy in performing the same operations as a classical one. Commercial applications for quantum computing Quantum Computing has a wide array of applications, which makes it one of the most exciting technologies to look forward to. Within the healthcare industry, it cannot only be used for research purposes but diagnostics and treatment as well. Since quantum computers have high processing power, it will enable researchers to use them in order to simulate interactions between different proteins of the human genome and drugs. This will allow them to evaluate drugs based on their interactions and can lead to pharmacological advancements. In diagnostics, MRI machines can be made to operate at higher levels and provide greater detail which will help the doctors in identifying medical issues. Similarly, treatments like radiotherapy can be further enhanced due to the use of quantum computing as it will be able to withstand complex simulations and provide answers in a timely manner. In the field of Finance, quantum computing can help in detecting fraud based on pattern recognition. Coupled with machine learning, neural networks can be trained timely and thereby improving the detection rate immensely. From a Marketing perspective, quantum computing can be used to process and analyze large amounts of data which can be used to put forward targeted advertisements at potential customers based on their behavior. The same can be done through classical computers, but quantum computing certainly has an edge in providing better and timely service due to the data being in large amounts. Optimization problems, which are encountered by companies like delivery services or arranging flight schedules, can be solved using quantum computers. It has uses in almost all avenues, whether they are public projects or advancements in data handling. What would normally take unimaginable amounts of time can be solved through the use of quantum computers. Major advantages of quantum computing The major advantage that quantum computers hold is that they are equipped to find optimal solutions to problems that have infinitely many variables. Due to their high processing power, quantum computers are able to run millions of simulations to test whatever theories that users might have. This gives it an ultimate advantage over other systems. Quantum computers at extremely cold temperatures. The temperatures required are near absolute zero. To achieve such a cold temperature, the chip is required to be cooled down. This is achieved through liquified helium, which makes the chip very cold. To achieve superconductivity, such low temperatures are essential for quantum computing. Research is being conducted to make quantum computing possible at higher temperatures, but no such significant improvement is expected in the near future. Scientists are developers are constantly in the run to make quantum computing possible given the large number of applications that it entails. Machine learning will benefit the most when stability is achieved in terms of quantum computations. Technology giants like Google and IBM are in the constant run to achieve quantum supremacy, with each taking steps to ensure the world witnesses a stable quantum computer in the next few years. What’s the major drawback (for now) of quantum computing? One of the issues that quantum computers encounter is any disturbance in the computer’s surroundings. Since they are very fragile, vibrations in the surroundings can impact the atoms, and decoherence will be caused. Despite their high demands, quantum computers will actually reduce the power consumption to operate. This is achieved through a process known as “quantum tunneling.” The possibilities are endless, and researchers are in a rush to make it happen. Other potential applications for quantum computing The potential applications for quantum computing are understandably vast. But in the short term, some of the most promising applications include: - Simulating the behavior of matter at the molecular level. Volkswagen and Daimler AG are using quantum computers to simulate the chemical composition of electric-vehicle batteries. The auto-makers hope that these simulations will highlight new ways of making battery technology more efficient. Pharmaceutical companies are using similar chemical simulations to assess compounds that could be used in new drugs. - Optimization. An obvious application of quantum computing is any scenario where a large amount of data must be analyzed in a timely fashion. Airbus is using the technology to help determine the most fuel-efficient ascent and descent paths for their range of aircraft. Volkswagen is also using quantum computing to calculate routes that avoid congestion for taxis in large cities. - Quantum computing uses elements of quantum mechanics to create high-performance computers that analyze large amounts of data rapidly. - Quantum computing is based on qubits and the two quantum properties of superposition and entanglement. Qubits offer significant benefits over traditional binary computers because they can exist in multiple states simultaneously. - Quantum computing is still in its infancy because qubits tend to decay to a non-quantum state when exposed to disturbances. Nevertheless, they are currently being used in the transport and pharmaceutical industries to drive innovation and performance. Connected Business Frameworks And Analyses Stability AI Ecosystem

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Internet security could soon have a new enemy: quantum computers. Such computers will be able to break existing encryption algorithms, removing protection for data exchanged over the Internet. Those who build quantum computers will make a lot of money. These statements make appealing headlines. However, we must exercise caution when thinking about real-world implications of quantum computing. In reality, a general-purpose quantum computer doesn’t exist yet. The day it does, it will be fast, but pretty bad at solving cryptographic puzzles. Some companies – like European IT services corporation Atos – are already selling quantum software, without ever having built a quantum computer. And the true business case for using this technology should interest smart-city visionaries more than those who are concerned with Internet privacy. Quantum is not for code breaking Contemporary semiconductors process information using bits, that is, units that can take either a state of 0 or the state of 1. Quantum computing relies on qubits (aka quantum bits). A qubit can simultaneously take a state of 1 and 0. Hence, two qubits can represent four states, four qubits 16 states and so forth. In addition, qubits are “entangled” because they can interact with one another to arrive at a solution. While the current semiconductors enable exact calculations (2+2=4), quantum computing is based on probabilities. In addition, most current qubit technologies require an extremely low temperature to operate. Higher temperatures decrease qubits’ stability, ultimately increasing computational noise. When you compute 2+2, the quantum computer will return several results, with 4 ideally having the highest probability. Yet, given the noise, when someone computes 2+2 hundreds of times, it might be that in some of the iterations, 4 isn’t the result with the highest probability. While companies invest a lot of money to reduce the noise in quantum calculations, it is likely to be there for a long time. These difficulties could well make quantum processors unsuitable for common encryption problems. Computers rely on precise calculations when encrypting or decrypting files. A recipient would not be able to decrypt an encrypted message using a quantum processor. Such a processor would only be able to approximately apply encryption keys. Consequently, it might be unable to break encryption behind current Internet protocols. Develop quantum software before hardware As you can imagine, there is no single standard for building a quantum computer. It is as if we were in the pre-ENIAC days when no one knew how to build a transistor, not to mention a CPU. Companies like IBM or Microsoft are investing a lot of money to build quantum hardware. This is an expensive and highly uncertain task. Atos, under the leadership of its CEO Thierry Breton, has chosen a different path. It has developed the Atos Quantum Learning Machine (Atos QLM) which allows programmers to write software without waiting for a general-purpose quantum computer to be built. The QLM can do that because it simulates the laws of physics that govern quantum computing. A similar technique is used to simulate behaviours of physical projects that don’t yet exist, such as airplanes. For example, a programmer can state that she wants to simulate interactions with a 16-qubit quantum computer, and the platform will behave accordingly. As of July 2018, the QLM was capable of simulating up to 41 qubits. As more and more companies use this platform, they are likely to converge on a common approach to program quantum computers and may also agree on what quantum hardware should look like. It would be like giving ENIAC’s creators in 1940s a platform for writing programs on an Intel processor in 1970s. This, in turn, would allow engineers to create a better ENIAC in anticipation of Intel’s architecture. Hence, software will drive the hardware with Atos leading the way into the future. According to the Atos executives I interviewed, their QLM sells really well in the United States. This makes them proud to be part of a European company that can compete on an equal (or better) footing with much larger American players. It also puts Atos at the core of the emerging ecosystem around quantum computing, as other participants develop technologies that would be compatible with QLM. Quantum in smart cities Despite its challenges, quantum computing is best suited for cases that involve massive data processing, but don’t require 100 percent precision in computations. Future smart cities represent a context in which such problems abound. Imagine London or Paris full of driverless cars. The artificial intelligence algorithms, sitting under the hood of every smart car, would solve the local problems. They would navigate the streets by constantly scanning the car’s environment to determine the best tactic, for instance, should the car stop or accelerate at the nearby intersection. Yet, such local decisions might not be optimal on a larger scale. Thus, the city might want to have a quantum computer to optimise the city-wide traffic flows. The system could give different suggestions to different cars to shorten their travel time. Even if a given forecast – e.g. the next five cars should detour via Street A to unclog Street B – is only 98 percent accurate, it would still be good enough on average. Everyone would have a better chance to arrive in time for dinner. Other possible uses of quantum computing include the optimisation of electrical grids: This is another problem that requires massive computational power, but can tolerate small errors. Working with quantum computers is a little like being in Alice’s Wonderland. These computers will be powerful, yet imprecise; a general-purpose machine is not built, yet we can write software for it. They will not be privacy’s enemies, but the friends of complex problems. Andrew Shipilov is a Professor of Strategy and Akzo Nobel Fellow at INSEAD. He is a programme director for Blue Ocean Strategy, an Executive Education programme. He is also a co-author of Network Advantage: How to Unlock Value from Your Alliances and Partnerships. Leave a Comment No comments yet.

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Five years ago, teams of physicists at Harvard University caused quite a sensation when they demonstrated that light pulses could be drastically slowed down and even brought to a standstill, then reactivated at will and sent on their merry way. Commentators were quick to predict stunning new applications in communications and in optical and quantum computing. The enthusiasm quickly evaporated, however, when it sank in that the experiments at Harvard had required enormously complex laser apparatus that could fill a room. Now, though, separate groups in the United States and Europe say that they have built and successfully tested more compact, rugged, and efficient means of delaying the pulses. Their work seems to clear the way for the kinds of applications foreseen by the Harvard pioneers, including not just those in optical switching and quantum communications but also others in network synchronization, radar, and even computer memory. Of course, you can slow a light beam by directing it through glass or any other material with a relatively high index of refraction. And a dark piece of paper will stop a beam quite dependably. But by absorbing the photons, the paper destroys the beam irretrievably. What the Harvard researchers had found was a way of slowing or stopping light pulses without destroying their constituent photons and then re-creating the pulses utterly unchanged. Lene Vestergaard Hau, a Danish physicist at Harvard, was the first to stop light. What she had done, in effect, was imprint the information carried by photons into spin patterns in clouds of atomic gases—”parking” the pulses in a gaseous medium, as she put it—and then reconstitute the pulses as desired, in a technique somewhat reminiscent of holography. Any information carried by the beam would remain perfectly intact. Hau’s close competitor at Harvard, Mikhail Lukin, anticipated using this stop-light technology as a means of transporting quantum states from one part of a computer to another, an essential process in any large computer based on quantum principles. There are nearer-term possibilities, too: a buffer for a router, for example, in which an optical delay line might keep one train of light pulses briefly on hold, allowing another train to pass through the router. Phased-array radars, commonly used in the military, could also benefit. In a phased-array radar, many small antennas transmit pulses that are delayed electronically in a systematic way to create a narrow beam that can be steered by changing the delays to the individual antennas. But producing and controlling these delays electronically is costly. It might be cheaper to devise a system in which electronic input is converted to optical signals, delayed in a tunable system, and then reconverted into electronic signals that are fed to microwave signal amplifiers and individual antennas in the correct phase. In the new work, the European and U.S. groups are slowing light pulses in optical fibers rather than in atomic gases, by up to several nanoseconds. They’re taking advantage of a phenomenon known as stimulated Brillouin scattering, which involves using sound waves to change the refractive index in a material. When incoming light waves encounter the changed refractive index, they scatter and slow down as some of the light is reflected back into the fiber and interferes with the incoming beam. Both groups—a team led by Luc Thévenaz at the Swiss Federal Institute of Technology, in Lausanne, and the other led by Alexander Gaeta at Cornell University, in Ithaca, N.Y.—were able to send data pulses with wavelengths of roughly 1550 nanometers through one end of spooled optical fibers. The fibers ranged in length from several hundred meters to a few kilometers, simulating real-world conditions. Using a pump beam with a slightly different frequency from the data beam, the teams generated sound waves in the fiber. The sound wave scatters the control beam, lowering its frequency to that of the data beam. Both beams interfere constructively, slowing the pulse down. The team led by Gaeta reported delaying 15-nanosecond-long pulses by more than 25 ns. The Lausanne team reported similar results, delaying pulses by up to 30 ns [see photo, " Taking Pulse”]. To be sure, those delay times of barely more than a pulse length are still too short for data to actually be represented. ”To be useful, this effect should be capable of delaying the pulse by at least a few pulse lengths,” comments Harvard’s Lukin. Another limit, especially for broadband applications, is the maximum frequency of the delayed pulses achieved in the experiments, which was only 35 megahertz. But that problem seems solvable: both groups recently reported success in increasing the bandwidth by modulating the control beam, giving it a bandwidth of several hundred megahertz. That additional bandwidth increased the bandwidth of the slowed pulses, too. ”There is no real limit for the extension of the bandwidth—we can extend it up to many tenths of a gigahertz,” says Thévenaz. The first real-world applications may not be that distant, says Daniel Gauthier of Duke University, in Durham, N.C., who participated in the Gaeta group’s research. One application he sees right away is a pulse regenerator. Its use would restore pulse trains that have been distorted by traveling over long distances through optical fibers and are out of sync with the system clock, which enables the system to determine where meaningful data strings start. ”You need to resynchronize the data pulse stream with the system clock, and for that you need one-pulse-width adjustment,” says Gauthier.

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Despite the difficulties, however, there has been progress in several areas of quantum computing. As the state of a qubit is, in effect, outside of the physical universe, the quantum computer can move away from classical computer designs using transistors connected by microscopic wires. Moore's Law has so far delivered massive growth in computer processing power as transistors and the connections between then become smaller with each passing year. However, things are starting to change, and solid-state quantum computers look set to bridge the gap between traditional transistor based computers and their quantum cousins. In a quantum computer, the computations are carried out by an exchange of information between individual qubits. This exchange of information is achieved by teleportation. This doesn't mean that a qubit, such as an atom or photon, is 'dematerialised' à la Star Trek, but that the properties of one qubit are transferred to another. This has been achieved at the University of Vienna and the Austrian Academy of Science. An optical fibre was used to connect lab buildings that were situated apart from each other across the river Danube. The lab was able to teleport qubits of information using a technique called polarisation. They succeeded in exploiting the entanglement phenomenon, which meant that two particles were tied together when in fact they're physically separate – the spooky distance that Einstein talked about. The particles existed in a parallel universe where they were able to change their state. As a result, they could exchange information, which is just what they would need to do in order to make meaningful calculations. So how far away are we from building working quantum computers? Actually, we have already constructed some of these near-mythical machines, even though they've employed relatively few working qubits. The earliest example was built in 1998 by scientists working at MIT and the University of Waterloo. It only had three qubits, but it showed the world that quantum computers were not just a fairy tale that physicists told their children. Two years later, a seven-qubit quantum computer that used nuclear magnetic resonance to manipulate atomic nuclei was built by Los Alamos National Labs. 2000 was also the year that IBM proved it too could build a quantum computer. Dr Isaac Chuang led the team that built a five-qubit quantum computer which enabled five fluorine atoms to interact together. The following year saw IBM once again demonstrate a working quantum computer. This time the firm was able to use Shor's algorithm. IBM used a seven-qubit quantum computer to find the factors of the number 15. A more complex quantum computer was also built in 2006 by MIT and Waterloo, and in 2007 a company called D-Wave burst onto the market with what it claimed was the world's first 16-qubit quantum machine. RIDE D-WAVE: D-Wave Systems' 16-qubit quantum computer is the subject of much debate D-Wave has yet to prove that its system is a true quantum computer, but this year also saw a team at Yale build the first solid-state quantum processor. The two-qubit superconducting chip was able to perform some basic calculations. The significance of this development by Yale's scientists is that it shows that a quantum computer can be built using electronics not that dissimilar to the components found in your desktop PC. Yale's system used artificial atoms that could be placed in the superpositional state quantum computers require. Until this development, scientists could not get a qubit to last longer than a nanosecond.In comparison, the Yale qubit lasted microseconds. This is long enough to perform meaningful calculations. Scientists working at the Universities of Manchester and Edinburgh have combined tiny magnets with molecular machines to create what could end up being the building blocks for future quantum computers. Professor David Leigh of the University of Edinburgh's School of Chemistry said: "This development brings super-fast, non-silicon-based computing a step closer. The magnetic molecules involved have potential to be used as qubits, and combining them with molecular machines enables them to move, which could be useful for building quantum computers. The major challenges we face now are to bring many of these qubits together to build a device that could perform calculations, and to discover how to communicate between them." Looking forward to that goal, one of the most promising developments in the field is quantum dots. These are nano-constructions made of semiconductor material. As such, we can use many of the techniques that we now use to build traditional computers to harness quantum dot technology. It may be possible to manufacture quantum dots in much the same way as we currently manufacture microprocessors. If the technology were successful, we could build quantum computers with as many qubits as we need. As things stand it's still too early to make complete logic gates from quantum dots, but the technology looks very promising indeed. The supercomputers we have today look like abacuses when compared to the processing power that quantum computers promise. With so many different avenues being explored by scientists, the final working structure of the quantum computer has yet to be realised. What recent work does show is that it's a realistic ambition to build a commercial quantum computer over the next few years. When that power arrives, we'll see a truly quantum shift in how we all manipulate information. First published in PC Plus Issue 289 Liked this? Then check out Why computers suck at maths Sign up for TechRadar's free Weird Week in Tech newsletter Get the oddest tech stories of the week, plus the most popular news and reviews delivered straight to your inbox. Sign up at http://www.techradar.com/register

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Scientists have gotten one step closer to a quantum internet by creating the world’s first multinode quantum network. Researchers at the QuTech research centre in Netherlands created the system, which is formed from three quantum nodes entangled by the spooky laws of quantum physics that govern subatomic particles. it’s the first time that more than two quantum bits, or “qubits,” that do the calculations in quantum computing are linked together as “nodes,” or network endpoints. Researchers expect the first quantum networks to unlock a wealth of computing applications that can’t be performed by existing classical devices — like faster computation and improved cryptography. “It will allow us to attach quantum computers for more computing power, create unhackable networks and connect atomic clocks and telescopes along side unprecedented levels of coordination,” Matteo Pompili, a member of the QuTech research team that created the network at Delft University of Technology in Netherlands, told Live Science. “There also are a lot of applications that we can’t really foresee. One might be to create an algorithm which will run elections in secure way, as an example .” In much same way that the normal computer bit is that the basic unit of digital information, the qubit is that the basic unit of quantum information. Just like the bit, the qubit are often either a 1 or a 0, which represent 2 possible positions during a two-state system. But that’s almost where the similarities end. Because of the bizarre laws of the quantum world, the qubit can exist during a superposition of both the 1 and 0 states until the instant it’s measured, when it’ll randomly collapse into either a 1 or a 0. This strange behavior is that the key to the power of quantum computing, because it allows a qubit to perform multiple calculations simultaneously. The biggest challenge in linking those qubits together into a quantum network is in establishing and maintaining a process called entanglement, or what Einstein dubbed “spooky action at a distance.” this is often when two qubits become coupled, linking their properties in order that any change in one particle will cause a change in other, even they’re separated by vast distances. You can entangle quantum nodes during a lot of the way , but one common method works by first entangling the stationary qubits (which form the network’s nodes) with photons, or light particles, before firing the photons at one another . once they meet, the 2 photons also become entangled, thereby entangling the qubits. This binds the 2 stationary nodes that are separated by a distance. Any change made to at least one is reflected by an instant change to other. “Spooky action at a distance” lets scientists change the state of a particle by altering the state of its distant entangled partner, effectively teleporting information across big gaps. But maintaining a state of entanglement may be a tough task, especially because the entangled system is usually in danger of interacting with the outside world and being destroyed by a process called decoherence. This means, first, that the quantum nodes need to be kept at extremely cold temperatures inside devices called cryostats to minimize the probabilities that the qubits will interfere with something outside the system. Second, the photons utilized in the entanglement can’t travel very long distances before they’re absorbed or scattered, — destroying the signal being sent between two nodes. “The problem is, unlike classical networks, you can’t amplify quantum signals. If you are trying to copy the qubit, you destroy the first copy,” Pompili said, referring to physics’ “no-cloning theorem,” which states that it’s impossible to make a identical copy of an unknown quantum state. “This really limits the distances we can send quantum signals to the tens of hundreds kilometers. If you would like to line up quantum communication with someone on the opposite side of the world, you’ll need relay nodes in between.” To solve the matter, the team created a network with three nodes, during which photons essentially “pass” the entanglement from a qubit at one among the outer nodes to at least one at the middle node. The middle node has two qubits — one to acquire an entangled state and one to store it. Once the entanglement between one outer node and therefore the middle node is stored, the middle node entangles the opposite outer node with its spare qubit. With all of this done, the middle node entangles its two qubits, causing the qubits of the outer nodes to become entangled. But designing this weird quantum mechanical spin on the classic “river crossing puzzle” was the smallest amount of the researchers troubles — weird, for sure, but not too tricky a idea. To form the entangled photons and beam them to the nodes in right way, the researchers had to use a complex system of mirrors and laser light. The really tough part was the technological challenge of reducing pesky noise in system, also as ensuring all of the lasers used to produce the photons were perfectly synchronized. “We’re talking about having 3-4 lasers for each node, so you begin to possess 10 lasers and three cryostats that each one have to work on same time, along side all of the electronics and synchronization,” Pompili said. The three-node system is especially useful because the memory qubit allows researchers to establish entanglement across the network node by node, instead of the more demanding requirement of doing it all at once. As soon as this is often done, information are often beamed across the network. Some of the researchers next steps with their new network are going to attempt this information beaming, along with improving essential components of the network’s computing abilities in order that they will work like regular computer networks do. All of those things will set the size that the new quantum network could reach. They also want to see if their system will allow them to establish entanglement b/w Delft and therefore the Hague, two Dutch cities that are roughly 6 miles (10 kilometers) apart. “Right now, all of our nodes are within 10-20 meters [32- 66 feet] of each-other,” Pompili said. “If you would like something useful, you would like to travel to kilometers. This is often getting to be the first time that we’re getting to make a link between long distances.” The researchers published their findings April 16 in the journal Science.

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You’ve heard that the first computer was the size of a small house, right? And how amazing it is that we all carry computers around in our pockets now? Well, some computers still are the size of houses—or even apartment buildings. These huge computers are so big because they’re super fast. And they’re capable of some amazing things. Exascale supercomputers are the next frontier in computing. They can quickly analyze massive volumes of data and realistically simulate many of the extremely complex processes and relationships behind the fundamental forces of the universe—in a way that’s never been done before. Many industries and systems could be affected, including precision medicine, climate science, and nuclear physics. Here’s a little more about how exascale computing works and how it stands to change the world. How is computer speed measured? One way scientists measure computer performance speed is in floating-point operations per second (FLOPS). These operations are simple arithmetic, like addition or multiplication, involving a number containing a decimal, like 3.5. A person can typically solve an operation such as addition with a pencil and paper in one second—that’s 1 FLOPS. Computers can solve these operations much faster. They are so fast that scientists use prefixes to talk about the speed. A typical laptop is capable of a few teraFLOPS, or a trillion operations per second. What is a supercomputer? The first supercomputer was developed in 1964, running 3,000,000 FLOPS, or 3 megaFLOPS. Since then, research teams have been in a constant race to build a faster computer. In 1996, computers hit the terascale milestone—that’s 12 zeros—when the US Department of Energy’s Intel ASCI Red supercomputer was measured at 1.06 teraFLOPS. The Roadrunner supercomputer was the first to pass the petascale milestone (15 zeros) when it was recorded running 1.026 petaFLOPS in 2008. Exascale computing is more than a million times faster than ASCI Red’s peak performance. “Exa” means 18 zeros. That means an exascale computer can perform more than 1,000,000,000,000,000,000 FLOPS, or 1 exaFLOPS. To contextualize how powerful an exascale computer is, an individual would have to perform one sum every second for 31,688,765,000 years to equal what an exascale computer can do in one single second. In May 2022, the Frontier supercomputer at the Oak Ridge National Laboratory in Tennessee clocked in at 1.1 exaFLOPS, becoming the first exascale computer on record and the current fastest supercomputer in the world. Over the coming years, Frontier could reach a theoretical peak of two exaFLOPS. Which industries could be affected by exascale computing? Exascale computing could allow scientists to solve problems that have until now been impossible. With exascale, exponential increases in memory, storage, and compute power may drive breakthroughs in several industries: energy production, storage, transmission, materials science, heavy industry, chemical design, AI and machine learning, cancer research and treatment, earthquake risk assessment, and many more. Here are some of the areas where exascale computing might be used: - Clean energy. Exascale computing could help develop resilient clean-energy systems. New materials developed with exascale computing can perform in extreme environments or adapt to changes in the water cycle, for example. - Medical research. Exascale computing can support the analysis of massive data volumes and complex environmental genomes. It can also support cancer research in analyzing patient genetics, tumor genomes, molecular simulations, and more. - Manufacturing. Using exascale computing could accelerate the adoption of additive manufacturing by allowing faster and more accurate modeling and simulation of manufacturing components. How is exascale computing different from quantum computing? Exascale computers are digital computers, like today’s laptops and phones, but with much more powerful hardware. On the other hand, quantum computers are a totally new approach to building a computer. Quantum computers won’t replace today’s computers. But using the principles of quantum physics, quantum computing will be able to solve very complex statistical problems that are difficult for today’s computers. Quantum computing has so much potential and momentum that McKinsey has identified it as one of the next big trends in tech. Put simply, exascale computing—and all classical computing—is built on bits. A bit is a unit of information that can store either a zero or a one. By contrast, quantum computing is built on qubits, which can store any combination of zero and one at the same time. When classical computers solve a problem with multiple variables, they have to conduct new calculations every time a variable changes. Each calculation is a single path to a single result. On the other hand, quantum computers have a larger working space, which means they can explore a massive number of paths simultaneously. This possibility means that quantum computers can be much, much faster than classical computers. For a more in-depth exploration of these topics, see McKinsey’s insights on digital. Learn more about our Digital Practice—and check out digital-related job opportunities if you’re interested in working at McKinsey. “Quantum computing just might save the planet,” May 19, 2022. Peter Cooper, Philipp Ernst, Dieter Kiewell, and Dickon Pinner. “Quantum computing use cases are getting real—what you need to know,” December 14, 2021. Matteo Biondi, Anna Heid, Nicolaus Henke, Niko Mohr, Lorenzo Pautasso, Ivan Ostojic, Linde Wester, and Rodney Zemmel. “Top trends in tech,” June 11, 2021, Jacomo Corbo, Nicolaus Henke, and Ivan Ostojic. “A game plan for quantum computing,” February 6, 2020, Alexandre Ménard, Ivan Ostojic, Mark Patel, and Daniel Volz.

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What is quantum physics? Let’s start with the fundamental information representation in a regular computer - the bit. It can be 1 or 0 which is convenient for representing information via a switch controlling the flow of electricity; 1 and 0 map to on and off. In a quantum computer the fundamental representation of information is a qubit. A qubit can represent not only a 0 or 1, but a combination of both at the same time. Well, what do we mean really by “both”? This is a tricky question, because this is where our everyday experience doesn’t help, and the laws of quantum mechanics take over. Quantum mechanics tells us the state of qubit can be any complex “superposition” of a 0 and 1. Fortunately we can visualize these superposition states of the qubit as points on the surface of a sphere. Now, instead of a switch with one of two values, we can represent the state of a qubit mathematically as a point on the surface of a sphere. Different points represent different qubit states - different combinations of 0 and 1. Many of the logic operations used in regular computers can be mapped to rotations of the qubit state on the sphere. For instance, a NOT gate which flips 0<->1 has an analog quantum bit flip which rotates a qubit state along a meridian of the bloch sphere. This can rotate 0-->1, 1-->0, and does the same to any superposition as well. What’s a superposition? That’s a state in which a qubit cannot be purely described as being 1 or 0, but rather some complex combination. In our graphical representation, a state on the equator of the Bloch sphere is actually an equal superposition of 0 and 1. Move towards the N pole and it’s a bit more heavily weighted to 0. Move the other way and it’s more heavily weighted to 1. Move around the equator and something different changes - the phase of the qubit. At different points on the sphere this leads the superposition to change between |0>+|1> and |0>-|1>. Changing this is a bit like moving along a wave from peak to trough and back - it’s the same wave, just different phases. Now here’s something interesting - when you measure a qubit in superposition, you get either a 0 or 1. That’s it. You can never determine if a qubit was in a superposition with one measurement; instead you have to perform many measurements. Even if the exact same state is prepared every time, the outcome of each measurement will always be random. The likelihood of measuring 0 or 1 is determined by how much 0 or 1 appears in the superposition - where you are on the sphere. This idea - that measurement collapses quantum superpositions - has huge impacts on how quantum computers actually function! We build real qubits using all kinds of different hardware - tiny loops of superconducting circuits or individual atoms in traps. We can use two different physical states to form a qubit and then perform logical operations by blasting the atoms with light - either microwaves or laser light. Tiny pulses timed just right can flip the qubit from one state to another [illustrate laser impinging atom, atom flipping from one orbital to the other] There’s one more element we use in quantum computers - entanglement. This is a special link between quantum systems that can only be described using quantum physics. In a sense when two objects - like qubits - become entangled, they can’t really be described as two objects any longer. They’re now one shared object - a condition that can again be induced by applying the right pulse of laser or microwave radiation. There are various ways to represent this visually as we do in Q-CTRL products, but it has huge impacts on how adding qubits to a quantum computer increases the overall performance of the system. Now we can get to the heart of why quantum computing is really hard: Noise. We know that when you hear that word you probably think about loud sounds like the noise coming from traffic that makes it hard to concentrate. We mean something a bit different here; noise describes all of the things that cause interference in a quantum computer. Just like a mobile phone call can suffer interference leading it to break up, a quantum computer is susceptible to interference from all sorts of sources, like electromagnetic signals coming from WiFi or disturbances in the Earth’s magnetic field. When qubits in a quantum computer are exposed to this kind of noise, the information in them gets degraded just the way sound quality is degraded by interference on a call. This is known as decoherence. When a qubit is sitting idle - not even being used in a computation - its state can be affected by interference. But when we’re performing a quantum logic operation, like a bit flip, we can also suffer from errors that cause us to rotate by the wrong amount. In either case the quantum state doesn’t end up where you expect, and over time can be randomized or even totally erased - clearly not a good thing when that quantum state was actually representing information. Compared with standard computers, quantum computers are extremely sensitive to this kind of noise. A typical transistor in a microprocessor can run for about a billion years at a billion operations per second, without ever suffering a hardware fault. By contrast, typical quantum bits become randomized in about one one-thousandth of a second. That’s a huge difference. Quantum algorithms need to execute many operations across a large number of qubits. Decoherence causes the information in our qubits to become randomized - and this leads to errors in the algorithm. The greater the influence of noise, the shorter the algorithm that can be run. Right now, instead of trillions of operations, we can typically only perform dozens before noise causes a fatal error. So what do we do about this? To start, for the past two decades teams have been working to make their hardware more passively stable - shielding it from the noise that causes decoherence. At the same time theorists have designed a clever algorithm called Quantum Error Correction that can identify and fix errors in the hardware. Sounds amazing! But the downside is that to make it work you have to spread the information in one qubit over lots of qubits. In many estimates it may take 1000 or more physical qubits to realize just one error-corrected qubit. And the worse your noise is, the more you need. Today’s machines are nowhere near capable of getting benefits from this kind of Quantum Error Correction. This is where Q-CTRL comes in. We add something extra - quantum firmware - which can stabilize the qubits against noise and decoherence without the need for extra resources. Learn more about Q-CTRL’s quantum firmware here. Learn how Quantum Error Correction can enable the quantum computing revolution Learn how quantum firmware accelerates the performance of quantum computers. Learn how about the current "noisy" era of quantum computing and what it stands to deliver. Learn the basics of how to build quantum algorithms for quantum computing. Discover the fundamentals of quantum physics for quantum computing Learn how the fragility of quantum hardware lets us detect the undetectable Discover the technology that will power a new information age Learn how quantum control accelerates the path to useful quantum technologies. Take the next step on your journey with short articles to help you understand how quantum computing and sensing will transform the world

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China’s moon landing: a giant leap for space science The People’s Republic of China successfully landed the first spacecraft, called the Chang’e-4, on the far side of the moon on Jan. 2. The probe was sent to search for rare earth metals and helium-3, used to make safer, more productive energy. Scientists hope to find answers about the evolution of our solar system and the origins of the universe. This is a stunning achievement. The landing of Chang’e-4 is just part of China’s overall space program that is centered on improving conditions for the country’s centrally planned society. China is developing a lunar space station and plans on sending a crew to the moon as soon as 2022. In contrast to China, the U.S. space program is driven by capitalist competition and the development of military weapons. Trump called on the Department of Defense and the Pentagon to develop a “Space Force,” a sixth branch of the military for the purpose of protecting U.S. assets in space and attacking its enemies during wars. The National Aeronautics and Space Administration’s International Space Station will soon be decommissioned. There are no plans for a replacement. NASA’s budget has been stalled. Contributions of China’s space program China now produces 90 percent of the world’s rare earth metals. The Chang’e-4 landing is searching for new sources of valuable materials like copper, aluminum, iron and rare earth metals essential for emerging technologies like cell phones, computers and other electronics and medical equipment. Nuclear fusion, the next generation of nuclear power, will someday replace nuclear fission, which is now used to fuel nuclear power plants. Fusion can generate four times as much energy as fission without hazardous environmental problems like radioactive waste. Helium-3, an ideal fuel for nuclear fusion, is an isotope of the element helium. There are an estimated 1 to 5 million tons of it on the moon, compared to only 15 tons on Earth. Once nuclear fusion technology matures, it will take 100 tons of helium-3 each year to meet global energy demands. Ouyang Ziyang, a prominent Chinese space scientist, predicted 13 years ago: “Each year three space shuttle missions could bring enough fuel for all human beings across the world.” (China Daily, July 26, 2006) For now it is too expensive to haul helium-3 back to Earth, but it may be useful as fuel for future spacecraft to explore deeper into space. Because the moon rotates just once each time it circles the Earth, only one side of its surface is visible from Earth. The far side of the moon is shielded from noise caused by radio waves, cell phones, power lines, GPS satellites and Wi-Fi. Scientists stationed on the moon’s far side will be able to look more deeply into space. In so doing, more will be learned about the evolution of the universe, the birth of the first stars and the formation of our solar system. Photographs from a Soviet spacecraft in 1959 showed that the far side of the moon has a thicker, older crust with deeper and more numerous craters. Scientists don’t know with certainty why the crust is thicker there. Change’e-4 is designed to help answer that question. Craters created by ancient asteroid hits on the thicker crust have not been filled in with lava flows since they were formed. Because of this, they may hold information about the early history of the moon’s formation and the development of our solar system. Chang’e-4 landed inside the oldest, deepest crater, called the Von Kármán Crater, on the far side of the moon. This basin offers scientists more information on the moon’s composition, structure and evolution and may be rich in rare earth metals and iron. Since the moon blocks transmissions from the Chang’e-4 probe, China launched a relay satellite called “Queqiao,” or “Magpie Bridge,” which bounces information and images from the probe back to China’s receiving stations. The Chang’e-4 lander carried the first mini-greenhouse to the moon. A mini biosphere is being set up with six live species: cotton, rapeseed, potato, fruit fly, yeast and arabidopsis, a flowering plant in the mustard family. This is a crucial step in establishing a longer visit by astronauts and developing a lunar space station. Cooperation and education, not competition Deng Xiaoping, China’s leader from 1978 until his retirement in 1989, told the world in 1978 that China was not taking part in the space race. He explained that the goal of China’s space program was to improve the standard of living for the Chinese people. It would focus on communications, remote sensing and meteorology. Scientists from Sweden and Germany collaborated with China on designs for some of the eight scientific instruments used in the Chang’e-4 mission. The Swedish Institute of Space Physics developed an instrument that will investigate how solar wind interacts with the lunar surface. Instead of working with China, President Donald Trump argued that the Chinese and Russian space programs are a threat to his Space Force. This is U.S. imperialist saber rattling. China provides four times as many college degrees in science, technology, engineering and mathematics (STEM) than the United States. Federal funding for education has decreased in the U.S., where a college degree is very expensive — and does not ensure better jobs for graduates. The U.S. is falling behind in space exploration and in other areas of scientific development. Speaking about students in China, U.S. astronomer and professor Chris Impey said, “They have very young engineers in their space program — very keen, very well trained, very ambitious.” He said China’s space program, like its economy, is growing explosively, at roughly 10 percent a year for the past decade. (NPR, May 11, 2015) China’s impressive space program In other areas of science and technology, like artificial intelligence and quantum computing, China is developing more quickly than the U.S. China recently launched a quantum satellite into space that physicists say can lead to a super-secure, super-fast quantum-internet system for China. The first Chinese satellite launch, which happened in 1970, focused on commercial applications. Since 2003, China has launched two space labs and sent six crews, including 12 taikonauts (Chinese astronauts), into low orbit. In 2016, China completed the world’s largest telescope built to detect radio signals, potential signs of life, from distant planets. That year, the country launched the Tiangong 2 space lab, which has been orbiting Earth since then. Last year China sent 38 launches into space, more than any other country. Many of them carried GPS-type systems that already cover China and much of Asia. China is currently working on developing a space lab to be stationed on the moon, after which the country will be able to send crews of scientists to continue exploration there. The Chang’e-4 is the first moon landing by any country since 2013. China annually spends about $2 billion on its space budget, compared to NASA’s $18 billion budget — and its space program is growing 10 times faster! How can China, a still-developing country, make these profound advances with less money? Deirdre Griswold, editor of Workers World newspaper, answered this question in a WW Commentary on Dec. 20 — “because the basic infrastructure is publicly owned, not in the hands of a profit-seeking, parasitic ruling class.”

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