Datasets:

---

Ken4070TiS

/

qubit_arXiv

like 0

Linear Depth QFT over IBM Heavy-hex Architecture

Compiling a given quantum algorithm into a target hardware architecture is a challenging optimization problem. The compiler must take into consideration the coupling graph of physical qubits and the gate operation dependencies. The existing noise in hardware architectures requires the compilation to use as few running cycles as possible. Existing approaches include using SAT solver or heuristics to complete the mapping but these may cause the issue of either long compilation time (e.g., timeout after hours) or suboptimal compilation results in terms of running cycles (e.g., exponentially increasing number of total cycles). In this paper, we propose an efficient mapping approach for Quantum Fourier Transformation (QFT) circuits over the existing IBM heavy-hex architecture. Such proposal first of all turns the architecture into a structure consisting of a straight line with dangling qubits, and then do the mapping over this generated structure recursively. The calculation shows that there is a linear depth upper bound for the time complexity of these structures and for a special case where there is 1 dangling qubit in every 5 qubits, the time complexity is 5N+O(1). All these results are better than state of the art methods.

2402.09705v1

Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at $1.4 \mu M$. Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.

2402.08210v1

Dynamically Generated Decoherence-Free Subspaces and Subsystems on Superconducting Qubits

Decoherence-free subspaces and subsystems (DFS) preserve quantum information by encoding it into symmetry-protected states unaffected by decoherence. An inherent DFS of a given experimental system may not exist; however, through the use of dynamical decoupling (DD), one can induce symmetries that support DFSs. Here, we provide the first experimental demonstration of DD-generated DFS logical qubits. Utilizing IBM Quantum superconducting processors, we investigate two and three-qubit DFS codes comprising up to six and seven noninteracting logical qubits, respectively. Through a combination of DD and error detection, we show that DFS logical qubits can achieve up to a 23% improvement in state preservation fidelity over physical qubits subject to DD alone. This constitutes a beyond-breakeven fidelity improvement for DFS-encoded qubits. Our results showcase the potential utility of DFS codes as a pathway toward enhanced computational accuracy via logical encoding on quantum processors.

2402.07278v2

Estimating the Effect of Crosstalk Error on Circuit Fidelity Using Noisy Intermediate-Scale Quantum Devices

Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any potential quantum advantage would be limited. There is no doubt that crosstalk is one of the main sources of noise in noisy intermediate-scale quantum (NISQ) systems, and it poses a fundamental challenge to hardware designs. Crosstalk between parallel instructions can corrupt quantum states and cause incorrect program execution. In this study, we present a necessary analysis of the crosstalk error effect on NISQ devices. Our approach is extremely straightforward and practical to estimate the crosstalk error of various multi-qubit devices. In particular, we combine the randomized benchmarking (RB) and simultaneous randomized benchmarking (SRB) protocol to estimate the crosstalk error from the correlation controlled-NOT (CNOT) gate. We demonstrate this protocol experimentally on 5-, 7-, \& 16-qubit devices. Our results demonstrate the crosstalk error model of three different IBM quantum devices over the experimental week and compare the error variation against the machine, number of qubits, quantum volume, processor, and topology. We then confirm the improvement in the circuit fidelity on different benchmarks by up to 3.06x via inserting an instruction barrier, as compared with an IBM quantum noisy device which offers near-optimal crosstalk mitigation in practice. Finally, we discuss the current system limitation, its tradeoff on fidelity and depth, noise beyond the NISQ system, and mitigation opportunities to ensure that the quantum operation can perform its quantum magic undisturbed.

2402.06952v3

Full Quantum Process Tomography of a Universal Entangling Gate on an IBM's Quantum Computer

Characterizing quantum dynamics is a cornerstone pursuit across quantum physics, quantum information science, and quantum computation. The precision of quantum gates in manipulating input basis states and their intricate superpositions is paramount. In this study, we conduct a thorough analysis of the SQSCZ gate, a universal two-qubit entangling gate, using real quantum hardware. This gate is a fusion of the square root of SWAP ($\sqrt{SWAP}$) and the square root of controlled-Z ($\sqrt{CZ}$) gates, serves as a foundational element for constructing universal gates, including the controlled-NOT gate. we begin by explaining the theory behind quantum process tomography (QPT), exploring the \textit{Choi-Jamiolkowski} isomorphism or the Choi matrix representation of the quantum process, along with a QPT algorithm utilizing Choi representation. Subsequently, we provide detailed insights into the experimental realization of the SQSCZ gate using a transmon-based superconducting qubit quantum computer. To comprehensively assess the gate's performance on a noisy intermediate-scale quantum (NISQ) computer, we conduct QPT experiments across diverse environments, employing both IBM Quantum's simulators and IBM Quantum's real quantum computer. Leveraging the Choi matrix in our QPT experiments allows for a comprehensive characterization of our quantum operations. Our analysis unveils commendable fidelities and noise properties of the SQSCZ gate, with process fidelities reaching $97.27098\%$ and $88.99383\%$, respectively. These findings hold promising implications for advancing both theoretical understanding and practical applications in the realm of quantum computation.

2402.06946v1

Transfer learning of optimal QAOA parameters in combinatorial optimization

Solving combinatorial optimization problems (COPs) is a promising application of quantum computation, with the Quantum Approximate Optimization Algorithm (QAOA) being one of the most studied quantum algorithms for solving them. However, multiple factors make the parameter search of the QAOA a hard optimization problem. In this work, we study transfer learning (TL), a methodology to reuse pre-trained QAOA parameters of one problem instance into different COP instances. To this end, we select small cases of the traveling salesman problem (TSP), the bin packing problem (BPP), the knapsack problem (KP), the weighted maximum cut (MaxCut) problem, the maximal independent set (MIS) problem, and portfolio optimization (PO), and find optimal $\beta$ and $\gamma$ parameters for $p$ layers. We compare how well the parameters found for one problem adapt to the others. Among the different problems, BPP is the one that produces the best transferable parameters, maintaining the probability of finding the optimal solution above a quadratic speedup for problem sizes up to 42 qubits and p = 10 layers. Using the BPP parameters, we perform experiments on IonQ Harmony and Aria, Rigetti Aspen-M-3, and IBM Brisbane of MIS instances for up to 18 qubits. The results indicate IonQ Aria yields the best overlap with the ideal probability distribution. Additionally, we show that cross-platform TL is possible using the D-Wave Advantage quantum annealer with the parameters found for BPP. We show an improvement in performance compared to the default protocols for MIS with up to 170 qubits. Our results suggest that there are QAOA parameters that generalize well for different COPs and annealing protocols.

2402.05549v1

Dynamics of measurement-induced state transitions in superconducting qubits

We have investigated temporal fluctuation of superconducting qubits via the time-resolved measurement for an IBM Quantum system. We found that the qubit error rate abruptly changes during specific time intervals. Each high error state persists for several tens of seconds, and exhibits an on-off behavior. The observed temporal instability can be attributed to qubit transitions induced by a measurement stimulus. Resonant transition between fluctuating dressed states of the qubits coupled with high-frequency resonators can be responsible for the error-rate change.

2402.05409v1

Crosstalk Attacks and Defence in a Shared Quantum Computing Environment

Quantum computing has the potential to provide solutions to problems that are intractable on classical computers, but the accuracy of the current generation of quantum computers suffer from the impact of noise or errors such as leakage, crosstalk, dephasing, and amplitude damping among others. As the access to quantum computers is almost exclusively in a shared environment through cloud-based services, it is possible that an adversary can exploit crosstalk noise to disrupt quantum computations on nearby qubits, even carefully designing quantum circuits to purposely lead to wrong answers. In this paper, we analyze the extent and characteristics of crosstalk noise through tomography conducted on IBM Quantum computers, leading to an enhanced crosstalk simulation model. Our results indicate that crosstalk noise is a significant source of errors on IBM quantum hardware, making crosstalk based attack a viable threat to quantum computing in a shared environment. Based on our crosstalk simulator benchmarked against IBM hardware, we assess the impact of crosstalk attacks and develop strategies for mitigating crosstalk effects. Through a systematic set of simulations, we assess the effectiveness of three crosstalk attack mitigation strategies, namely circuit separation, qubit allocation optimization via reinforcement learning, and the use of spectator qubits, and show that they all overcome crosstalk attacks with varying degrees of success and help to secure quantum computing in a shared platform.

2402.02753v1

Comparative study of quantum error correction strategies for the heavy-hexagonal lattice

Topological quantum error correction is a milestone in the scaling roadmap of quantum computers, which targets circuits with trillions of gates that would allow running quantum algorithms for real-world problems. The square-lattice surface code has become the workhorse to address this challenge, as it poses milder requirements on current devices both in terms of required error rates and small local connectivities. In some platforms, however, the connectivities are kept even lower in order to minimise gate errors at the hardware level, which limits the error correcting codes that can be directly implemented on them. In this work, we make a comparative study of possible strategies to overcome this limitation for the heavy-hexagonal lattice, the architecture of current IBM superconducting quantum computers. We explore two complementary strategies: the search for an efficient embedding of the surface code into the heavy-hexagonal lattice, as well as the use of codes whose connectivity requirements are naturally tailored to this architecture, such as subsystem-type and Floquet codes. Using noise models of increased complexity, we assess the performance of these strategies for IBM devices in terms of their error thresholds and qubit footprints. An optimized SWAP-based embedding of the surface code is found to be the most promising strategy towards a near-term demonstration of quantum error correction advantage.

2402.02185v1

Efficient implementation of discrete-time quantum walks on quantum computers

Quantum walks have proven to be a universal model for quantum computation and to provide speed-up in certain quantum algorithms. The discrete-time quantum walk (DTQW) model, among others, is one of the most suitable candidates for circuit implementation, due to its discrete nature. Current implementations, however, are usually characterized by quantum circuits of large size and depth, which leads to a higher computational cost and severely limits the number of time steps that can be reliably implemented on current quantum computers. In this work, we propose an efficient and scalable quantum circuit implementing the DTQW on the $2^n$-cycle based on the diagonalization of the conditional shift operator. For $t$ time-steps of the DTQW, the proposed circuit requires only $O(n^2 + nt)$ two-qubit gates compared to the $O(n^2 t)$ of the current most efficient implementation based on quantum Fourier transforms. We test the proposed circuit on an IBM quantum device for a Hadamard DTQW on the $4$- and $8$-cycle characterized by periodic dynamics and recurrent generation of maximally entangled single-particle states. Experimental results are meaningful well beyond the regime of few time steps, paving the way for reliable implementation and use on quantum computers.

2402.01854v2

Benchmarking Multipartite Entanglement Generation with Graph States

As quantum computing technology slowly matures and the number of available qubits on a QPU gradually increases, interest in assessing the capabilities of quantum computing hardware in a scalable manner is growing. One of the key properties for quantum computing is the ability to generate multipartite entangled states. In this paper, aspects of benchmarking entanglement generation capabilities of noisy intermediate-scale quantum (NISQ) devices are discussed based on the preparation of graph states and the verification of entanglement in the prepared states. Thereby, we use entanglement witnesses that are specifically suited for a scalable experiment design. This choice of entanglement witnesses can detect A) bipartite entanglement and B) genuine multipartite entanglement for graph states with constant two measurement settings if the prepared graph state is based on a 2-colorable graph, e.g., a square grid graph or one of its subgraphs. With this, we experimentally verify that a fully bipartite entangled state can be prepared on a 127-qubit IBM Quantum superconducting QPU, and genuine multipartite entanglement can be detected for states of up to 23 qubits with quantum readout error mitigation.

2402.00766v1

Robust Error Accumulation Suppression for Quantum Circuits

We present a robust error accumulation suppression (REAS) technique to manage errors in quantum computers. Our method reduces the accumulation of errors in any quantum circuit composed of single- or two-qubit gates expressed as $e^{-i \sigma\theta }$ for Pauli operators $\sigma$ and $\theta \in [0,\pi)$, which forms a universal gate set. For coherent errors -- which include gate overrotation and crosstalk -- we demonstrate a reduction of the error scaling in an $L$-depth circuit from $O(L)$ to $O(\sqrt{L})$. This asymptotic error suppression behavior can be proven in a regime where all gates -- including those constituting the error-suppressing protocol itself -- are noisy. Going beyond coherent errors, we derive the general form of decoherence noise that can be suppressed by REAS. Lastly, we experimentally demonstrate the effectiveness of our approach regarding realistic errors using 100-qubit circuits with up to 64 two-qubit gate layers on IBM Quantum processors.

2401.16884v2

Geometric measure of entanglement of quantum graph states prepared with controlled phase shift operators

We consider graph states generated by the action of controlled phase shift operators on a separable state of a multi-qubit system. The case when all the qubits are initially prepared in arbitrary states is investigated. We obtain the geometric measure of entanglement of a qubit with the remaining system in graph states represented by arbitrary weighted graphs and establish its relationship with state parameters. For two-qubit graph states, the geometric measure of entanglement is also quantified on IBM's simulator Qiskit Aer and quantum processor ibmq lima based on auxiliary mean spin measurements. The results of quantum computations verify our analytical predictions.

2401.14997v1

Liouvillian Exceptional Points of Non-Hermitian Systems via Quantum Process Tomography

Hamiltonian exceptional points (HEPs) are spectral degeneracies of non-Hermitian Hamiltonians describing classical and semiclassical open systems with gain and/or loss. However, this definition overlooks the occurrence of quantum jumps in the evolution of open quantum systems. These quantum effects are properly accounted for by considering Liouvillians and their exceptional points (LEPs) [Minganti et al., Phys. Rev. A {\bf 100}, 062131 (2019)]. Here, we explicitly describe how standard quantum process tomography, which reveals the dynamics of a quantum system, can be readily applied to reveal and characterize LEPs of non-Hermitian systems. We conducted experiments on an IBM quantum processor to implement a prototype model simulating the decay of a single qubit through three competing channels. Subsequently, we performed tomographic reconstruction of the corresponding experimental Liouvillians and their LEPs using both single- and two-qubit operations. This example underscores the efficacy of process tomography in tuning and observing LEPs, despite the absence of HEPs in the model.

2401.14993v1

Quantum error mitigation for Fourier moment computation

Hamiltonian moments in Fourier space - expectation values of the unitary evolution operator under a Hamiltonian at different times - provide a convenient framework to understand quantum systems. They offer insights into the energy distribution, higher-order dynamics, response functions, correlation information and physical properties. This paper focuses on the computation of Fourier moments within the context of a nuclear effective field theory on superconducting quantum hardware. The study integrates echo verification and noise renormalization into Hadamard tests using control reversal gates. These techniques, combined with purification and error suppression methods, effectively address quantum hardware decoherence. The analysis, conducted using noise models, reveals a significant reduction in noise strength by two orders of magnitude. Moreover, quantum circuits involving up to 266 CNOT gates over five qubits demonstrate high accuracy under these methodologies when run on IBM superconducting quantum devices.

2401.13048v1

Novel techniques for efficient quantum state tomography and quantum process tomography and their experimental implementation

This thesis actively focuses on designing, analyzing, and experimentally implementing various QST and QPT protocols using an NMR ensemble quantum processor and superconducting qubit-based IBM cloud quantum processor. Part of the thesis also includes a study of duality quantum simulation algorithms and Sz-Nagy's dilation algorithm on NMR where several 2-qubit non-unitary quantum channels were simulated using only a single ancilla qubit. The work carried out in the thesis mainly addresses several important issues in experimental QST and QPT which include: i) dealing with invalid experimental density (process) matrices using constraint convex optimization (CCO) method, ii) scalable QST and QPT using incomplete measurements via compressed sensing (CS) algorithm and artificial neural network (ANN) technique, iii) selective and direct measurement of unknown quantum states and processes using the concept of quantum 2-design states and weak measurement (WM) approach and iv) quantum simulation and characterization of open quantum dynamics using the dilation technique.

2401.09941v1

The Quantum Cryptography Approach: Unleashing the Potential of Quantum Key Reconciliation Protocol for Secure Communication

Quantum cryptography is the study of delivering secret communications across a quantum channel. Recently, Quantum Key Distribution (QKD) has been recognized as the most important breakthrough in quantum cryptography. This process facilitates two distant parties to share secure communications based on physical laws. The BB84 protocol was developed in 1984 and remains the most widely used among BB92, Ekert91, COW, and SARG04 protocols. However the practical security of QKD with imperfect devices have been widely discussed, and there are many ways to guarantee that generated key by QKD still provides unconditional security. This paper proposed a novel method that allows users to communicate while generating the secure keys as well as securing the transmission without any leakage of the data. In this approach sender will never reveal her basis, hence neither the receiver nor the intruder will get knowledge of the fundamental basis.Further to detect Eve, polynomial interpolation is also used as a key verification technique. In order to fully utilize the quantum computing capabilities provided by IBM quantum computers, the protocol is executed using the Qiskit backend for 45 qubits. This article discusses a plot of % error against alpha (strength of eavesdropping). As a result, different types of noise have been included, and the success probability of the desired key bits has been determined. Furthermore, the success probability under depolarizing noise is explained for different qubit counts.Last but not least, even when the applied noise is increased to maximum capacity, a 50% probability of successful key generation is still observed in an experiment.

2401.08987v1

Digital quantum simulation of gravitational optomechanics with IBM quantum computers

We showcase the digital quantum simulation of the action of a Hamiltonian that governs the interaction between a quantum mechanical oscillator and an optical field, generating quantum entanglement between them via gravitational effects. This is achieved by making use of a boson-qubit mapping protocol and a digital gate decomposition that allow us to run the simulations in the quantum computers available in the IBM Quantum platform. We present the obtained results for the fidelity of the experiment in two different quantum computers, after applying error mitigation and post-selection techniques. The achieved results correspond to fidelities over 90%, which indicates that we were able to perform a faithful digital quantum simulation of the interaction and therefore of the generation of quantum entanglement by gravitational means in optomechanical systems.

2401.08370v3

Study on quantum thermalization from thermal initial states in a superconducting quantum computer

Quantum thermalization in contemporary quantum devices, in particular quantum computers, has recently attracted significant theoretical interest. Unusual thermalization processes, such as the Quantum Mpemba Effect (QME), have been explored theoretically. However, there is a shortage of experimental results due to the difficulty in preparing thermal states. In this paper, we propose a method to address this challenge. Moreover, we experimentally validate our approach using IBM quantum devices, providing results for unusal relaxation in equidistant quenches as predicted for the IBM qubit. We also assess the formalism introduced for the QME, obtaining results consistent with the theoretical predictions. This demonstration underscores that our method can streamline the investigation of thermal states and thermalization in quantum physics.

2403.14630v2

Quantum Simulations of Hadron Dynamics in the Schwinger Model using 112 Qubits

Hadron wavepackets are prepared and time evolved in the Schwinger model using 112 qubits of IBM's 133-qubit Heron quantum computer ibm_torino. The initialization of the hadron wavepacket is performed in two steps. First, the vacuum is prepared across the whole lattice using the recently developed SC-ADAPT-VQE algorithm and workflow. SC-ADAPT-VQE is then extended to the preparation of localized states, and used to establish a hadron wavepacket on top of the vacuum. This is done by adaptively constructing low-depth circuits that maximize the overlap with an adiabatically prepared hadron wavepacket. Due to the localized nature of the wavepacket, these circuits can be determined on a sequence of small lattices using classical computers, and then robustly scaled to prepare wavepackets on large lattices for simulations using quantum computers. Time evolution is implemented with a second-order Trotterization. To reduce both the required qubit connectivity and circuit depth, an approximate quasi-local interaction is introduced. This approximation is made possible by the emergence of confinement at long distances, and converges exponentially with increasing distance of the interactions. Using multiple error-mitigation strategies, up to 14 Trotter steps of time evolution are performed, employing 13,858 two-qubit gates (with a CNOT depth of 370). The propagation of hadrons is clearly identified, with results that compare favorably with Matrix Product State simulations. Prospects for a near-term quantum advantage in simulations of hadron scattering are discussed.

2401.08044v2

Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem

Simon's problem is to find a hidden period (a bitstring) encoded into an unknown $2$-to-$1$ function. It is one of the earliest problems for which an exponential quantum speedup was proven for ideal, noiseless quantum computers, albeit in the oracle model. Here, using two different $127$-qubit IBM Quantum superconducting processors, we demonstrate an algorithmic quantum speedup for a variant of Simon's problem where the hidden period has a restricted Hamming weight $w$. For sufficiently small values of $w$ and for circuits involving up to $58$ qubits, we demonstrate an exponential speedup, albeit of a lower quality than the speedup predicted for the noiseless algorithm. The speedup exponent and the range of $w$ values for which an exponential speedup exists are significantly enhanced when the computation is protected by dynamical decoupling. Further enhancement is achieved with measurement error mitigation. This constitutes a demonstration of a bona fide quantum advantage for an Abelian hidden subgroup problem.

2401.07934v2

Simulating quantum field theories on gate-based quantum computers

We implement a simulation of a quantum field theory in 1+1 space-time dimensions on a gate-based quantum computer using the light front formulation of the theory. The nonperturbative simulation of the Yukawa model field theory is verified on IBM's simulator and is also demonstrated on a small-scale IBM circuit-based quantum processor, on the cloud, using IBM Qiskit. The light front formulation allows for controlling the resource requirement and complexity of the computation with commensurate trade-offs in accuracy and detail by modulating a single parameter, namely the harmonic resolution. Qubit operators for the bosonic excitations were also created and were used along with the fermionic ones already available, to simulate the theory involving all of these particles. With the restriction on the number of logical qubits available on the existent gate-based Noisy Intermediate-Scale Quantum (NISQ) devices, the trotterization approximation is also used. We show that experimentally relevant quantities like cross-sections for various processes, survival probabilities of various states, etc. can be computed. We also explore the inaccuracies introduced by the bounds on achievable harmonic resolution and Trotter steps placed by the limited number of qubits and circuit depth supported by present-day NISQ devices.

2401.04496v2

Context-Aware Coupler Reconfiguration for Tunable Coupler-Based Superconducting Quantum Computers

We address interconnection challenges in limited-qubit superconducting quantum computers (SQC), which often face crosstalk errors due to expanded qubit interactions during operations. Existing mitigation methods carry trade-offs, like hardware couplers or software-based gate scheduling. Our innovation, the Context-Aware COupler REconfiguration (CA-CORE) compilation method, aligns with application-specific design principles. It optimizes the qubit connections for improved SQC performance, leveraging tunable couplers. Through contextual analysis of qubit correlations, we configure an efficient coupling map considering SQC constraints. Our method reduces depth and SWAP operations by up to 18.84% and 42.47%, respectively. It also enhances circuit fidelity by 40% compared to IBM and Google's topologies. Notably, our method compiles a 33-qubit circuit in less than 1 second.

2401.03817v2

$\mathcal{PT}$-symmetric mapping of three states and its implementation on a cloud quantum processor

We develop a new $\mathcal{PT}$-symmetric approach for mapping three pure qubit states, implement it by the dilation method, and demonstrate it with a superconducting quantum processor provided by the IBM Quantum Experience. We derive exact formulas for the population of the post-selected $\mathcal{PT}$-symmetric subspace and show consistency with the Hermitian case, conservation of average projections on reference vectors, and Quantum Fisher Information. When used for discrimination of $N = 2$ pure states, our algorithm gives an equivalent result to the conventional unambiguous quantum state discrimination. For $N = 3$ states, our approach provides novel properties unavailable in the conventional Hermitian case and can transform an arbitrary set of three quantum states into another arbitrary set of three states at the cost of introducing an inconclusive result. For the QKD three-state protocol, our algorithm has the same error rate as the conventional minimum error, maximum confidence, and maximum mutual information strategies. The proposed method surpasses its Hermitian counterparts in quantum sensing using non-MSE metrics, providing an advantage for precise estimations within specific data space regions and improved robustness to outliers. Applied to quantum database search, our approach yields a notable decrease in circuit depth in comparison to traditional Grover's search algorithm while maintaining the same average number of oracle calls, thereby offering significant advantages for NISQ computers. Additionally, the versatility of our method can be valuable for the discrimination of highly non-symmetric quantum states, and quantum error correction. Our work unlocks new doors for applying $\mathcal{PT}$-symmetry in quantum communication, computing, and cryptography.

2312.16680v2

Characterization of entanglement on superconducting quantum computers of up to 414 qubits

As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entanglement is a quantum resource crucial for achieving quantum advantage, the challenge in its generation makes it a valuable benchmark for measuring the performance of universal quantum devices. In this work, we study entanglement in Greenberger-Horne-Zeilinger (GHZ) and graph states prepared on the range of IBM Quantum devices. We generate GHZ states and investigate their coherence times with respect to state size and dynamical decoupling techniques. A GHZ fidelity of $0.519 \pm 0.014$ is measured on a 32-qubit GHZ state, certifying its genuine multipartite entanglement (GME). We show a substantial improvement in GHZ decoherence rates for a 7-qubit GHZ state after implementing dynamical decoupling, and observe a linear trend in the decoherence rate of $\alpha=(7.13N+5.54)10^{-3}\mu s^{-1}$ for up to $N=15$ qubits, confirming the absence of superdecoherence. Additionally, we prepare and characterize fully bipartite entangled native graph states on 22 superconducting quantum devices with qubit counts as high as 414 qubits, all active qubits of the 433-qubit IBM Osprey device. Analysis of the decay of 2-qubit entanglement within the prepared states shows suppression of coherent noise signals with the implementation of dynamical decoupling techniques. Additionally, we observe that the entanglement in some qubit pairs oscillates over time, which is likely caused by residual ZZ-interactions. Characterizing entanglement in native graph states, along with detecting entanglement oscillations, can be an effective approach to low-level device benchmarking that encapsulates 2-qubit error rates along with additional sources of noise, with possible applications to quantum circuit compilation.

2312.15170v2

Deterministic Ansätze for the Measurement-based Variational Quantum Eigensolver

Measurement-based quantum computing (MBQC) is a promising approach to reducing circuit depth in noisy intermediate-scale quantum algorithms such as the Variational Quantum Eigensolver (VQE). Unlike gate-based computing, MBQC employs local measurements on a preprepared resource state, offering a trade-off between circuit depth and qubit count. Ensuring determinism is crucial to MBQC, particularly in the VQE context, as a lack of flow in measurement patterns leads to evaluating the cost function at irrelevant locations. This study introduces MBVQE-ans\"atze that respect determinism and resemble the widely used problem-agnostic hardware-efficient VQE ansatz. We evaluate our approach using ideal simulations on the Schwinger Hamiltonian and $XY$-model and perform experiments on IBM hardware with an adaptive measurement capability. In our use case, we find that ensuring determinism works better via postselection than by adaptive measurements at the expense of increased sampling cost. Additionally, we propose an efficient MBQC-inspired method to prepare the resource state, specifically the cluster state, on hardware with heavy-hex connectivity, requiring a single measurement round, and implement this scheme on quantum computers with $27$ and $127$ qubits. We observe notable improvements for larger cluster states, although direct gate-based implementation achieves higher fidelity for smaller instances.

2312.13241v1

Enhancing quantum utility: simulating large-scale quantum spin chains on superconducting quantum computers

We present the quantum simulation of the frustrated quantum spin-$\frac{1}{2}$ antiferromagnetic Heisenberg spin chain with competing nearest-neighbor $(J_1)$ and next-nearest-neighbor $(J_2)$ exchange interactions in the real superconducting quantum computer with qubits ranging up to 100. In particular, we implement, for the first time, the Hamiltonian with the next-nearest neighbor exchange interaction in conjunction with the nearest neighbor interaction on IBM's superconducting quantum computer and carry out the time evolution of the spin chain by employing first-order Trotterization. Furthermore, our novel implementation of second-order Trotterization for the isotropic Heisenberg spin chain, involving only nearest-neighbor exchange interaction, enables precise measurement of the expectation values of staggered magnetization observable across a range of up to 100 qubits. Notably, in both cases, our approach results in a constant circuit depth in each Trotter step, independent of the initial number of qubits. Our demonstration of the accurate measurement of expectation values for the large-scale quantum system using superconducting quantum computers designates the quantum utility of these devices for investigating various properties of many-body quantum systems. This will be a stepping stone to achieving the quantum advantage over classical ones in simulating quantum systems before the fault tolerance quantum era.

2312.12427v2

Quantum Fourier Transformation Circuits Compilation

In this research paper, our primary focus revolves around the domain-specific hardware mapping strategy tailored for Quantum Fourier Transformation (QFT) circuits. While previous approaches have heavily relied on SAT solvers or heuristic methods to generate hardware-compatible QFT circuits by inserting SWAP gates to realign logical qubits with physical qubits at various stages, they encountered significant challenges. These challenges include extended compilation times due to the expansive search space for SAT solvers and suboptimal outcomes in terms of the number of cycles required to execute all gate operations efficiently. In our study, we adopt a novel approach that combines technical intuition, often referred to as "educated guesses," and sophisticated program synthesis tools. Our objective is to uncover QFT mapping solutions that leverage concepts such as affine loops and modular functions. The groundbreaking outcome of our research is the introduction of the first set of linear-depth transformed QFT circuits designed for Google Sycamore, IBM heavy-hex, and the conventional 2-dimensional (2D) grid configurations, accommodating an arbitrary number of qubits denoted as 'N'. Additionally, we have conducted comprehensive analyses to verify the correctness of these solutions and to develop strategies for handling potential faults within them.

2312.16114v1

Utilizing Novel Quantum Counters for Grover's Algorithm to Solve the Dominating Set Problem

Grover's algorithm is a well-known unstructured quantum search algorithm run on quantum computers. It constructs an oracle and calls the oracle O($\sqrt N$) times to locate specific data out of N unsorted data. This represents a quadratic speedup compared to the classical unstructured data sequential search algorithm, which requires to call the oracle O(N) times. We are currently in the noisy intermediate-scale quantum (NISQ) era in which quantum computers have a limited number of qubits, short decoherence time, and low gate fidelity. It is thus desirable to design quantum components with three good properties: (i) a reduced number of qubits, (ii) shorter quantum depth, and (iii) fewer gates. This paper utilizes novel quantum counters with the above-mentioned three good properties to construct the oracle of Grover's algorithm to efficiently solve the dominating set problem (DSP), as defined below. For a given graph G=(V, E), a dominating set (DS) D is a subset of the vertex set V, such that every vertex is in D or has an adjacent vertex in D. The DSP is to decide for a given graph G and an integer k whether there exists a DS with size k. Algorithms solving the DSP have many applications. For example, they can be applied to check whether k routers suffice to connect all computers in a computer network. The DSP is an NP-complete problem, indicating that no classical algorithm exists to solve the DSP with polynomial time complexity in the worst case. Therefore, using quantum algorithms, such as Grover's algorithm, to exploit the potent computational capabilities of quantum computers to solve the DSP is highly promising. We execute the whole quantum circuit of Grover's algorithm using novel quantum counters through the IBM Quantum Lab service to validate that the circuit can solve the DSP efficiently and correctly.

2312.09388v1

Practical Benchmarking of Randomized Measurement Methods for Quantum Chemistry Hamiltonians

Many hybrid quantum-classical algorithms for the application of ground state energy estimation in quantum chemistry involve estimating the expectation value of a molecular Hamiltonian with respect to a quantum state through measurements on a quantum device. To guide the selection of measurement methods designed for this observable estimation problem, we propose a benchmark called CSHOREBench (Common States and Hamiltonians for ObseRvable Estimation Benchmark) that assesses the performance of these methods against a set of common molecular Hamiltonians and common states encountered during the runtime of hybrid quantum-classical algorithms. In CSHOREBench, we account for resource utilization of a quantum computer through measurements of a prepared state, and a classical computer through computational runtime spent in proposing measurements and classical post-processing of acquired measurement outcomes. We apply CSHOREBench considering a variety of measurement methods on Hamiltonians of size up to 16 qubits. Our discussion is aided by using the framework of decision diagrams which provides an efficient data structure for various randomized methods and illustrate how to derandomize distributions on decision diagrams. In numerical simulations, we find that the methods of decision diagrams and derandomization are the most preferable. In experiments on IBM quantum devices against small molecules, we observe that decision diagrams reduces the number of measurements made by classical shadows by more than 80%, that made by locally biased classical shadows by around 57%, and consistently require fewer quantum measurements along with lower classical computational runtime than derandomization. Furthermore, CSHOREBench is empirically efficient to run when considering states of random quantum ansatz with fixed depth.

2312.07497v1

Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs

We show through numerical simulation that the Quantum Approximate Optimization Algorithm (QAOA) for higher-order, random-coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from $16$ up to $127$ qubits for $p=1$ up to $p=5$, which allows for straight-forward transfer learning of QAOA angles on instance sizes where exhaustive grid-search is prohibitive even for $p>1$. We use Matrix Product State (MPS) simulation at different bond dimensions to obtain confidence in these results, and we obtain the optimal solutions to these combinatorial optimization problems using CPLEX. In order to assess the ability of current noisy quantum hardware to exploit such parameter concentration, we execute short-depth QAOA circuits (with a CNOT depth of 6 per $p$, resulting in circuits which contain $1420$ two qubit gates for $127$ qubit $p=5$ QAOA) on $100$ higher-order (cubic term) Ising models on IBM quantum superconducting processors with $16, 27, 127$ qubits using QAOA angles learned from a single $16$-qubit instance. We show that (i) the best quantum processors generally find lower energy solutions up to $p=3$ for 27 qubit systems and up to $p=2$ for 127 qubit systems and are overcome by noise at higher values of $p$, (ii) the best quantum processors find mean energies that are about a factor of two off from the noise-free numerical simulation results. Additional insights from our experiments are that large performance differences exist among different quantum processors even of the same generation and that dynamical decoupling significantly improve performance for some, but decrease performance for other quantum processors. Lastly we show $p=1$ QAOA angle mean energy landscapes computed using up to a $414$ qubit quantum computer, showing that the mean QAOA energy landscapes remain very similar as the problem size changes.

2312.00997v2

Exploiting Maximally Mixed States for Spectral Estimation by Time Evolution

We introduce a novel approach for estimating the spectrum of quantum many-body Hamiltonians, and more generally, of Hermitian operators, using quantum time evolution. In our approach we are evolving a maximally mixed state under the Hamiltonian of interest and collecting specific time-series measurements to estimate its spectrum. We demonstrate the advantage of our technique over currently used classical statistical sampling methods. We showcase our approach by experimentally estimating the spectral decomposition of a 2-qubit Heisenberg Hamiltonian on an IBM Quantum backend. For this purpose, we develop a hardware-efficient decomposition that controls $n$-qubit Pauli rotations against the physically closest qubit alongside expressing two-qubit rotations in terms of the native entangling interaction. This substantially reduced the accumulation of errors from noisy two-qubit operations in time evolution simulation protocols. We conclude by discussing the potential impact of our work and the future directions of research it opens.

2312.00687v2

Sachdev-Ye-Kitaev model on a noisy quantum computer

We study the SYK model -- an important toy model for quantum gravity on IBM's superconducting qubit quantum computers. By using a graph-coloring algorithm to minimize the number of commuting clusters of terms in the qubitized Hamiltonian, we find the gate complexity of the time evolution using the first-order product formula for $N$ Majorana fermions is $\mathcal{O}(N^5 J^{2}t^2/\epsilon)$ where $J$ is the dimensionful coupling parameter, $t$ is the evolution time, and $\epsilon$ is the desired precision. With this improved resource requirement, we perform the time evolution for $N=6, 8$ with maximum two-qubit circuit depth of 343. We perform different error mitigation schemes on the noisy hardware results and find good agreement with the exact diagonalization results on classical computers and noiseless simulators. In particular, we compute return probability after time $t$ and out-of-time order correlators (OTOC) which is a standard observable of quantifying the chaotic nature of quantum systems.

2311.17991v4

Quantum simulation of entanglement dynamics in a quantum processor

We implement a five-qubit protocol in IBM quantum processors to study entanglement dynamics in a two qubit system in the presence of a simulated environment. Specifically, two qubits represent the main system, while another two qubits serve as the environment. Additionally, we employ an auxiliary qubit to estimate the quantum entanglement. Specifically, we observe the sudden death and sudden birth of entanglement for different inital conditions that were simultaneously implemented on the IBM 127-qubit quantum processor \textit{ibm$\_$brisbane}. We obtain the quantum entanglement evolution of the main system qubits and the environment qubits averaging over $N=10$ independent experiments in the same quantum device. Our experimental data shows the entanglement and disentanglement signatures in system and enviroment qubits, where the noisy nature of current quantum processors produce a shift on times signaling sudden death or sudden birth of entanglement. This work takes relevance showing the usefulness of current noisy quantum devices to test fundamental concepts in quantum information.

2311.15973v2

Atomique: A Quantum Compiler for Reconfigurable Neutral Atom Arrays

The neutral atom array has gained prominence in quantum computing for its scalability and operation fidelity. Previous works focus on fixed atom arrays (FAAs) that require extensive SWAP operations for long-range interactions. This work explores a novel architecture reconfigurable atom arrays (RAAs), also known as field programmable qubit arrays (FPQAs), which allows for coherent atom movements during circuit execution under some constraints. Such atom movements, which are unique to this architecture, could reduce the cost of long-range interactions significantly if the atom movements could be scheduled strategically. In this work, we introduce Atomique, a compilation framework designed for qubit mapping, atom movement, and gate scheduling for RAA. Atomique contains a qubit-array mapper to decide the coarse-grained mapping of the qubits to arrays, leveraging MAX k-Cut on a constructed gate frequency graph to minimize SWAP overhead. Subsequently, a qubit-atom mapper determines the fine-grained mapping of qubits to specific atoms in the array and considers load balance to prevent hardware constraint violations. We further propose a router that identifies parallel gates, schedules them simultaneously, and reduces depth. We evaluate Atomique across 20+ diverse benchmarks, including generic circuits (arbitrary, QASMBench, SupermarQ), quantum simulation, and QAOA circuits. Atomique consistently outperforms IBM Superconducting, FAA with long-range gates, and FAA with rectangular and triangular topologies, achieving significant reductions in depth and the number of two-qubit gates.

2311.15123v2

Enigma: Privacy-Preserving Execution of QAOA on Untrusted Quantum Computers

Quantum computers can solve problems that are beyond the capabilities of conventional computers. As quantum computers are expensive and hard to maintain, the typical model for performing quantum computation is to send the circuit to a quantum cloud provider. This leads to privacy concerns for commercial entities as an untrusted server can learn protected information from the provided circuit. Current proposals for Secure Quantum Computing (SQC) either rely on emerging technologies (such as quantum networks) or incur prohibitive overheads (for Quantum Homomorphic Encryption). The goal of our paper is to enable low-cost privacy-preserving quantum computation that can be used with current systems. We propose Enigma, a suite of privacy-preserving schemes specifically designed for the Quantum Approximate Optimization Algorithm (QAOA). Unlike previous SQC techniques that obfuscate quantum circuits, Enigma transforms the input problem of QAOA, such that the resulting circuit and the outcomes are unintelligible to the server. We introduce three variants of Enigma. Enigma-I protects the coefficients of QAOA using random phase flipping and fudging of values. Enigma-II protects the nodes of the graph by introducing decoy qubits, which are indistinguishable from primary ones. Enigma-III protects the edge information of the graph by modifying the graph such that each node has an identical number of connections. For all variants of Enigma, we demonstrate that we can still obtain the solution for the original problem. We evaluate Enigma using IBM quantum devices and show that the privacy improvements of Enigma come at only a small reduction in fidelity (1%-13%).

2311.13546v1

Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits

We present hierarchical learning, a novel variational architecture for efficient training of large-scale variational quantum circuits. We test and benchmark our technique for distribution loading with quantum circuit born machines (QCBMs). With QCBMs, probability distributions are loaded into the squared amplitudes of computational basis vectors represented by bitstrings. Our key insight is to take advantage of the fact that the most significant (qu)bits have a greater effect on the final distribution and can be learned first. One can think of it as a generalization of layerwise learning, where some parameters of the variational circuit are learned first to prevent the phenomena of barren plateaus. We briefly review adjoint methods for computing the gradient, in particular for loss functions that are not expectation values of observables. We first compare the role of connectivity in the variational ansatz for the task of loading a Gaussian distribution on nine qubits, finding that 2D connectivity greatly outperforms qubits arranged on a line. Based on our observations, we then implement this strategy on large-scale numerical experiments with GPUs, training a QCBM to reproduce a 3-dimensional multivariate Gaussian distribution on 27 qubits up to $\sim4\%$ total variation distance. Though barren plateau arguments do not strictly apply here due to the objective function not being tied to an observable, this is to our knowledge the first practical demonstration of variational learning on large numbers of qubits. We also demonstrate hierarchical learning as a resource-efficient way to load distributions for existing quantum hardware (IBM's 7 and 27 qubit devices) in tandem with Fire Opal optimizations.

2311.12929v1

Efficient reconstruction, benchmarking and validation of cross-talk models in readout noise in near-term quantum devices

Readout errors contribute significantly to the overall noise affecting present-day quantum computers. However, the complete characterization of generic readout noise is infeasible for devices consisting of a large number of qubits. Here we introduce an appropriately tailored quantum detector tomography protocol, the so called Quantum Detector Overlapping Tomography, which enables efficient characterization of $k-$local cross-talk effects in the readout noise as the sample complexity of the protocol scales logarithmically with the total number of qubits. We show that QDOT data provides information about suitably defined reduced POVM operators, correlations and coherences in the readout noise, as well as allows to reconstruct the correlated clusters and neighbours readout noise model. Benchmarks are introduced to verify utility and accuracy of the reconstructed model. We apply our method to investigate cross-talk effects on 79 qubit Rigetti and 127 qubit IBM devices. We discuss their readout noise characteristics, and demonstrate effectiveness of our approach by showing superior performance of correlated clusters and neighbours over models without cross-talk in model-based readout error mitigation applied to energy estimation of MAX-2-SAT Hamiltonians, with the improvement on the order of 20% for both devices.

2311.10661v1

Observation of the non-Hermitian skin effect and Fermi skin on a digital quantum computer

Non-Hermitian physics has attracted considerable attention in recent years, particularly the non-Hermitian skin effect (NHSE) for its extreme sensitivity and non-locality. While the NHSE has been physically observed in various classical metamaterials and even ultracold atomic arrays, its highly-nontrivial implications in many-body dynamics have never been experimentally investigated. In this work, we report the first observation of the NHSE on a universal quantum processor, as well as its characteristic but elusive Fermi skin from many-fermion statistics. To implement NHSE dynamics on a quantum computer, the effective time-evolution circuit not only needs to be non-reciprocal and non-unitary but must also be scaled up to a sufficient number of lattice qubits to achieve spatial non-locality. We show how such a non-unitary operation can be systematically realized by post-selecting multiple ancilla qubits, as demonstrated through two paradigmatic non-reciprocal models on a noisy IBM quantum processor, with clear signatures of asymmetric spatial propagation and many-body Fermi skin accumulation. To minimize errors from inevitable device noise, time evolution is performed using a trainable, optimized quantum circuit produced with variational quantum algorithms. Our study represents a critical milestone in the quantum simulation of non-Hermitian lattice phenomena on present-day quantum computers and can be readily generalized to more sophisticated many-body models with the remarkable programmability of quantum computers.

2311.10143v3

Superposition States on Different Axes of the Bloch Sphere for Cost-Effective Circuits Realization on IBM Quantum Computers

A proposed method for preparing the superposition states of qubits using different axes of the Bloch sphere. This method utilizes the Y-axis of the Bloch sphere using IBM native (square root of X) gates, instead of utilizing the X-axis of the Bloch sphere using IBM non-native Hadamard gates, for transpiling cost-effective quantum circuits on IBM quantum computers. In this paper, our presented method ensures that the final transpiled quantum circuits always have a lower quantum cost than that of the transpiled quantum circuits using the Hadamard gates.

2311.09326v1

sQueeze: Accelerated Quantum Pulse Schedules

Quantum devices in the Noisy Intermediate-Scale Quantum (NISQ) era are limited by high error rates and short decoherence times. Typically, compiler optimisations have provided solutions at the gate level. Alternatively, we exploit the finest level of quantum control and introduce a set of pulse level quantum compiler optimisations: sQueeze. Instead of relying on existing calibration that may be inaccurate, we provide a method for the live calibration of two new parameterised basis gates $R_{x}(\theta)$ and $R_{zx}(\theta)$ using an external server. We validate our techniques using the IBM quantum devices and the OpenPulse control interface over more than 8 billion shots. The $R_{x}(\theta)$ gates are on average 52.7% more accurate than their current native Qiskit decompositions, while $R_{zx}(\theta)$ are 22.6% more accurate on average. These more accurate pulses also provide up to a 4.1$\times$ speed-up for single-qubit operations and 3.1$\times$ speed-up for two-qubit gates. Then sQueeze demonstrates up to a 39.6% improvement in the fidelity of quantum benchmark algorithms compared to conventional approaches.

2311.08742v1

GALA-n: Generic Architecture of Layout-Aware n-Bit Quantum Operators for Cost-Effective Realization on IBM Quantum Computers

A generic architecture of n-bit quantum operators is proposed for cost-effective transpilation, based on the layouts and the number of n neighbor physical qubits for IBM quantum computers, where n >= 3. This proposed architecture is termed "GALA-n quantum operator". The GALA-n quantum operator is designed using the visual approach of the Bloch sphere, from the visual representations of the rotational quantum operations for IBM native gates (square root of X, X, RZ, and CNOT). In this paper, we also proposed a new formula for the quantum cost, which calculates the total numbers of native gates, SWAP gates, and the depth of the final transpiled quantum circuits. This formula is termed the "transpilation quantum cost". After transpilation, our proposed GALA-n quantum operator always has a lower transpilation quantum cost than that of conventional n-bit quantum operators, which are mainly constructed from costly n-bit Toffoli gates.

2311.06760v1

Benchmarking Quantum Processor Performance at Scale

As quantum processors grow, new performance benchmarks are required to capture the full quality of the devices at scale. While quantum volume is an excellent benchmark, it focuses on the highest quality subset of the device and so is unable to indicate the average performance over a large number of connected qubits. Furthermore, it is a discrete pass/fail and so is not reflective of continuous improvements in hardware nor does it provide quantitative direction to large-scale algorithms. For example, there may be value in error mitigated Hamiltonian simulation at scale with devices unable to pass strict quantum volume tests. Here we discuss a scalable benchmark which measures the fidelity of a connecting set of two-qubit gates over $N$ qubits by measuring gate errors using simultaneous direct randomized benchmarking in disjoint layers. Our layer fidelity can be easily related to algorithmic run time, via $\gamma$ defined in Ref.\cite{berg2022probabilistic} that can be used to estimate the number of circuits required for error mitigation. The protocol is efficient and obtains all the pair rates in the layered structure. Compared to regular (isolated) RB this approach is sensitive to crosstalk. As an example we measure a $N=80~(100)$ qubit layer fidelity on a 127 qubit fixed-coupling "Eagle" processor (ibm\_sherbrooke) of 0.26(0.19) and on the 133 qubit tunable-coupling "Heron" processor (ibm\_montecarlo) of 0.61(0.26). This can easily be expressed as a layer size independent quantity, error per layered gate (EPLG), which is here $1.7\times10^{-2}(1.7\times10^{-2})$ for ibm\_sherbrooke and $6.2\times10^{-3}(1.2\times10^{-2})$ for ibm\_montecarlo.

2311.05933v1

Simulating Heavy-Hex Transverse Field Ising Model Magnetization Dynamics Using Programmable Quantum Annealers

Recently, a Hamiltonian dynamics simulation was performed on a kicked ferromagnetic 2D transverse field Ising model with a connectivity graph native to the 127 qubit heavy-hex IBM Quantum architecture using ZNE quantum error mitigation. We demonstrate that one of the observables in this Trotterized Hamiltonian dynamics simulation, namely magnetization, can be efficiently simulated on current superconducting qubit-based programmable quantum annealing computers. We show this using two distinct methods: reverse quantum annealing and h-gain state encoding. This simulation is possible because the 127 qubit heavy-hex connectivity graph can be natively embedded onto the D-Wave Pegasus quantum annealer hardware graph and because there exists a direct equivalence between the energy scales of the two types of quantum computers. We derive equivalent anneal pauses in order to simulate the Trotterized quantum circuit dynamics for varying Rx rotations $\theta_h \in (0, \frac{\pi}{2}]$, using quantum annealing processors. Multiple disjoint instances of the Ising model of interest can be embedded onto the D-Wave Pegasus hardware graph, allowing for parallel quantum annealing. We report equivalent magnetization dynamics using quantum annealing for time steps of 20, 50 up to 10,000, which we find are consistent with exact classical 27 qubit heavy-hex Trotterized circuit magnetization dynamics, and we observe reasonable, albeit noisy, agreement with the existing simulations for single site magnetization at 20 Trotter steps. The quantum annealers are able to simulate equivalent magnetization dynamics for thousands of time steps, significantly out of the computational reach of the digital quantum computers on which the original Hamiltonian dynamics simulations were performed.

2311.01657v3

Efficient separate quantification of state preparation errors and measurement errors on quantum computers and their mitigation

Current noisy quantum computers have multiple types of errors, which can occur in the state preparation, measurement/readout, and gate operation, as well as intrinsic decoherence and relaxation. Partly motivated by the booming of intermediate-scale quantum processors, measurement and gate errors have been recently extensively studied, and several methods of mitigating them have been proposed and formulated in software packages (e.g., in IBM Qiskit). Despite this, the state preparation error and the procedure to quantify it have not yet been standardized, as state preparation and measurement errors are usually considered not directly separable. Inspired by a recent work of Laflamme, Lin, and Mor [Phys. Rev. A 106, 012439 (2022)], we propose a simple and resource-efficient approach to quantify separately the state preparation and readout error rates. With these two errors separately quantified, we also propose methods to mitigate them separately, especially mitigating state preparation errors with linear (with the number of qubits) complexity. As a result of the separate mitigation, we show that the fidelity of the outcome can be improved by an order of magnitude compared to the standard measurement error mitigation scheme. We also show that the quantification and mitigation scheme is resilient against gate noise and can be immediately applied to current noisy quantum computers. To demonstrate this, we present results from cloud experiments on IBM's superconducting quantum computers. The results indicate that the state preparation error rate is also an important metric for qubit metrology that can be efficiently obtained.

2310.18881v1

Physics informed neural networks learning a two-qubit Hamiltonian

Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN is the universal approximation theorem, which claims that any function can be approximate by a neural network if it contains enough complexity. Consequently, a neural network can be a solution of a physical model. Moreover, by means of extra data provided by the user, intrinsic physical parameters can be extracted from the approach called inverse-PINN. Here, we apply inverse-PINN with the goal of extracting all the physical parameters that constitutes a two qubit Hamiltonian. We find that this approach is very efficient. To probe the robustness of the inverse-PINN to learn the Hamiltonian of a two-qubit system, we use the IBM quantum computers as experimental platforms to obtain the data that is plugged in the PINN. We found that our method is able to predict the two-qubit parameters with 5% of accuracy on average.

2310.15148v1

Quantum computer error structure probed by quantum error correction syndrome measurements

With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction needs to be experimentally evaluated. In this work, we have assessed the performance of IBM superconducting quantum computer devices implementing heavy-hexagon code syndrome measurements with increasing circuit sizes up to 23 qubits, against the error assumptions underpinning code threshold calculations. Circuit operator change rate statistics in the presence of depolarizing and biased noise were modelled using analytic functions of error model parameters. Data from 16 repeated syndrome measurement cycles was found to be inconsistent with a uniform depolarizing noise model, favouring instead biased and inhomogeneous noise models. Spatial-temporal correlations investigated via $Z$ stabilizer measurements revealed significant temporal correlation in detection events. These results highlight the non-trivial structure which may be present in the noise of quantum error correction circuits, revealed by operator measurement statistics, and support the development of noise-tailored codes and decoders to adapt.

2310.12448v2

Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms

Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and scalable mapping methods by utilizing the inherent structure of algorithms. While exact methods provide optimal solutions, they become intractable for large circuits. Scalable methods, like SWAP networks, offer fast solutions but lack optimality. AOQMAP bridges this gap by leveraging algorithmic features and their association with specific device substructures to achieve optimal and scalable solutions. The proposed strategy follows a two stage approach. First, it maps circuits to subtopologies to meet connectivity constraints. Second, it identifies the optimal qubits for execution using a cost function. Notably, AOQMAP provides both scalable and optimal solutions for variational quantum algorithms with fully connected two qubit interactions on common subtopologies including linear, T-, and H-shaped, minimizing circuit depth. Benchmarking experiments conducted on IBM quantum devices demonstrate significant reductions in gate count and circuit depth compared to Qiskit, Tket, and SWAP network. Specifically, AOQMAP achieves up to an 82% reduction in circuit depth and an average 138% increase in success probability. This scalable and algorithm-specific approach holds the potential to optimize a wider range of quantum algorithms.

2310.09826v3

Observation of the Quantum Zeno Effect on a NISQ Device

We study the Quantum Zeno Effect (QZE) on a single qubit on IBM Quantum Experience devices under the effect of multiple measurements. We consider two possible cases: the Rabi evolution and the free decay. SPAM error mitigations have also been applied. In both cases we observe the occurrence of the QZE as an increasing of the survival probability with the number of measurements.

2310.08317v3

Improvements to Quantum Interior Point Method for Linear Optimization

Quantum linear system algorithms (QLSA) have the potential to speed up Interior Point Methods (IPM). However, a major challenge is that QLSAs are inexact and sensitive to the condition number of the coefficient matrices of linear systems. This sensitivity is exacerbated when the Newton systems arising in IPMs converge to a singular matrix. Recently, an Inexact Feasible Quantum IPM (IF-QIPM) has been developed that addresses the inexactness of QLSAs and, in part, the influence of the condition number using iterative refinement. However, this method requires a large number of gates and qubits to be implemented. Here, we propose a new IF-QIPM using the normal equation system, which is more adaptable to near-term quantum devices. To mitigate the sensitivity to the condition number, we use preconditioning coupled with iterative refinement to obtain better gate complexity. Finally, we demonstrate the effectiveness of our approach on IBM Qiskit simulators

2310.07574v1

Quantum reservoir computing with repeated measurements on superconducting devices

Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered as promising reservoirs, but the conventional quantum reservoir computing (QRC) models have problems in the execution time. In this paper, we develop a quantum reservoir (QR) system that exploits repeated measurement to generate a time-series, which can effectively reduce the execution time. We experimentally implement the proposed QRC on the IBM's quantum superconducting device and show that it achieves higher accuracy as well as shorter execution time than the conventional QRC method. Furthermore, we study the temporal information processing capacity to quantify the computational capability of the proposed QRC; in particular, we use this quantity to identify the measurement strength that best tradeoffs the amount of available information and the strength of dissipation. An experimental demonstration with soft robot is also provided, where the repeated measurement over 1000 timesteps was effectively applied. Finally, a preliminary result with 120 qubits device is discussed.

2310.06706v1

Quantum state preparation for bell-shaped probability distributions using deconvolution methods

Quantum systems are a natural choice for generating probability distributions due to the phenomena of quantum measurements. The data that we observe in nature from various physical phenomena can be modelled using quantum circuits. To load this data, which is mostly in the form of a probability distribution, we present a hybrid classical-quantum approach. The classical pre-processing step is based on the concept of deconvolution of discrete signals. We use the Jensen-Shannon distance as the cost function to quantify the closeness of the outcome from the classical step and the target distribution. The chosen cost function is symmetric and allows us to perform the deconvolution step using any appropriate optimization algorithm. The output from the deconvolution step is used to construct the quantum circuit required to load the given probability distribution, leading to an overall reduction in circuit depth. The deconvolution step splits a bell-shaped probability mass function into smaller probability mass functions, and this paves the way for parallel data processing in quantum hardware, which consists of a quantum adder circuit as the penultimate step before measurement. We tested the algorithm on IBM Quantum simulators and on the IBMQ Kolkata quantum computer, having a 27-qubit quantum processor. We validated the hybrid Classical-Quantum algorithm by loading two different distributions of bell shape. Specifically, we loaded 7 and 15-element PMF for (i) Standard Normal distribution and (ii) Laplace distribution.

2310.05044v2

Implementation of the Projective Quantum Eigensolver on a Quantum Computer

We study the performance of our previously proposed Projective Quantum Eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H$_2$, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy limited by both stochastic error and inconsistent performance of the IBM devices. We find that an optimization algorithm using direct inversion of the iterative subspace can converge swiftly, even to excited states, but stochastic noise can cause large parameter updates. For the four-site transverse-field Ising model at the critical point, PQE with an appropriate application of qubit tapering can recover 99% of the correlation energy, even discarding several parameters. The large number of CNOT gates needed for the additional parameters introduces a concomitant error that, on the IBM devices, results in loss of accuracy, despite the increased expressivity of the trial state. Error extrapolation techniques and tapering or postselection are recommended to mitigate errors in PQE hardware experiments.

2310.04520v1

Hamiltonian Encoding for Quantum Approximate Time Evolution of Kinetic Energy Operator

The time evolution operator plays a crucial role in the precise computation of chemical experiments on quantum computers and holds immense promise for advancing the fields of physical and computer sciences, with applications spanning quantum simulation and machine learning. However, the construction of large-scale quantum computers poses significant challenges, prompting the need for innovative and resource-efficient strategies. Traditional methods like phase estimation or variational algorithms come with certain limitations such as the use of classical optimization or complex quantum circuitry. One successful method is the Trotterization technique used for quantum simulation, specifically in atomic structure problems with a gate complexity of approximately O(n^2) for an n-qubit realization. In this work, we have proposed a new encoding method, namely quantum approximate time evolution (QATE) for the quantum implementation of the kinetic energy operator as a diagonal unitary operator considering the first quantization level. The theoretical foundations of our approach are discussed, and experimental results are obtained on an IBM quantum machine. Our proposed method offers gate complexity in sub-quadratic polynomial with qubit size $n$ which is an improvement over previous work. Further, the fidelity improvement for the time evolution of the Gaussian wave packet has also been demonstrated.

2310.03319v1

An improved two-threshold quantum segmentation algorithm for NEQR image

The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an improved two-threshold quantum segmentation algorithm for NEQR image is proposed, which can segment the complex gray-scale image into a clear ternary image by using fewer qubits and can be scaled to use n thresholds for n + 1 segmentations. In addition, a feasible quantum comparator is designed to distinguish the gray-scale values with two thresholds, and then a scalable quantum circuit is designed to segment the NEQR image. For a 2^(n)*2^(n) image with q gray-scale levels, the quantum cost of our algorithm can be reduced to 60q-6, which is lower than other existing quantum algorithms and does not increase with the image's size increases. The experiment on IBM Q demonstrates that our algorithm can effectively segment the image.

2311.12033v1

A quantum segmentation algorithm based on local adaptive threshold for NEQR image

The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a $2^n\times 2^n$ image with q gray-scale levels, the complexity of our algorithm can be reduced to $O(n^2+q)$, which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

2311.11953v1

Efficient tensor network simulation of IBM's largest quantum processors

We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 qubits) and Condor (1121 qubits). We simulate the dynamics of a complex quantum many-body system -- specifically, the kicked Ising experiment considered recently by IBM in Nature 618, p. 500-505 (2023) -- using graph-based Projected Entangled Pair States (gPEPS), which was proposed by some of us in PRB 99, 195105 (2019). Our results show that simple tensor updates are already sufficient to achieve very large unprecedented accuracy with remarkably low computational resources for this model. Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines. We also report accurate simulations for infinitely-many qubits. Our results show that gPEPS are a natural tool to efficiently simulate quantum computers with an underlying lattice-based qubit connectivity, such as all quantum processors based on superconducting qubits.

2309.15642v3

A Novel Quantum Visual Secret Sharing Scheme

Inspired by Naor et al.'s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with the IBM Q is conducted to demonstrate the practical feasibility.

2309.13659v1

Quantum Circuits for Stabilizer Error Correcting Codes: A Tutorial

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. However, circuits for such encoders and decoders haven't been explored in depth. This paper serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We present encoding and decoding circuits for five-qubit code and Steane code, along with verification of these circuits using IBM Qiskit. We also provide nearest neighbour compliant encoder and decoder circuits for the five-qubit code.

2309.11793v1

Systematic Design and Optimization of Quantum Circuits for Stabilizer Codes

Quantum computing is an emerging technology that has the potential to achieve exponential speedups over their classical counterparts. To achieve quantum advantage, quantum principles are being applied to fields such as communications, information processing, and artificial intelligence. However, quantum computers face a fundamental issue since quantum bits are extremely noisy and prone to decoherence. Keeping qubits error free is one of the most important steps towards reliable quantum computing. Different stabilizer codes for quantum error correction have been proposed in past decades and several methods have been proposed to import classical error correcting codes to the quantum domain. However, formal approaches towards the design and optimization of circuits for these quantum encoders and decoders have so far not been proposed. In this paper, we propose a formal algorithm for systematic construction of encoding circuits for general stabilizer codes. This algorithm is used to design encoding and decoding circuits for an eight-qubit code. Next, we propose a systematic method for the optimization of the encoder circuit thus designed. Using the proposed method, we optimize the encoding circuit in terms of the number of 2-qubit gates used. The proposed optimized eight-qubit encoder uses 18 CNOT gates and 4 Hadamard gates, as compared to 14 single qubit gates, 33 2-qubit gates, and 6 CCNOT gates in a prior work. The encoder and decoder circuits are verified using IBM Qiskit. We also present optimized encoder circuits for Steane code and a 13-qubit code in terms of the number of gates used.

2309.12373v1

Three-qubit Parity Gate via Simultaneous Cross Resonance Drives

Native multi-qubit parity gates have various potential quantum computing applications, such as entanglement creation, logical state encoding and parity measurement in quantum error correction. Here, using simultaneous cross-resonance drives on two control qubits with a common target, we demonstrate an efficient implementation of a three-qubit parity gate. We have developed a calibration procedure based on the one for the echoed cross-resonance gate. We confirm that our use of simultaneous drives leads to higher interleaved randomized benchmarking fidelities than a naive implementation with two consecutive CNOT gates. We also demonstrate that our simultaneous parity gates can significantly improve the parity measurement error probability for the heavy-hexagon code on an IBM Quantum processor using seven superconducting qubits with all-microwave control.

2309.11287v2

Quantum computation of $π\to π^*$ and $n \to π^*$ excited states of aromatic heterocycles

The computation of excited electronic states is an important application for quantum computers. In this work, we simulate the excited state spectra of four aromatic heterocycles on IBM superconducting quantum computers, focusing on active spaces of $\pi \to \pi^*$ and $n \to \pi^*$ excitations. We approximate the ground state with the entanglement forging method, a qubit reduction technique that maps a spatial orbital to a single qubit, rather than two qubits. We then determine excited states using the quantum subspace expansion method. We showcase these algorithms on quantum hardware using up to 8 qubits and employing readout and gate error mitigation techniques. Our results demonstrate a successful application of quantum computing in the simulation of active-space electronic wavefunctions of substituted aromatic heterocycles, and outline challenges to be overcome in elucidating the optical properties of organic molecules with hybrid quantum-classical algorithms.

2309.09868v1

Superstaq: Deep Optimization of Quantum Programs

We describe Superstaq, a quantum software platform that optimizes the execution of quantum programs by tailoring to underlying hardware primitives. For benchmarks such as the Bernstein-Vazirani algorithm and the Qubit Coupled Cluster chemistry method, we find that deep optimization can improve program execution performance by at least 10x compared to prevailing state-of-the-art compilers. To highlight the versatility of our approach, we present results from several hardware platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti), trapped ions (QSCOUT), and neutral atoms (Infleqtion). Across all platforms, we demonstrate new levels of performance and new capabilities that are enabled by deeper integration between quantum programs and the device physics of hardware.

2309.05157v1

Deformed Fredkin model for the $ν{=}5/2$ Moore-Read state on thin cylinders

We propose a frustration-free model for the Moore-Read quantum Hall state on sufficiently thin cylinders with circumferences $\lesssim 7$ magnetic lengths. While the Moore-Read Hamiltonian involves complicated long-range interactions between triplets of electrons in a Landau level, our effective model is a simpler one-dimensional chain of qubits with deformed Fredkin gates. We show that the ground state of the Fredkin model has high overlap with the Moore-Read wave function and accurately reproduces the latter's entanglement properties. Moreover, we demonstrate that the model captures the dynamical response of the Moore-Read state to a geometric quench, induced by suddenly changing the anisotropy of the system. We elucidate the underlying mechanism of the quench dynamics and show that it coincides with the linearized bimetric field theory. The minimal model introduced here can be directly implemented as a first step towards quantum simulation of the Moore-Read state, as we demonstrate by deriving an efficient circuit approximation to the ground state and implementing it on IBM quantum processor.

2309.04527v1

Quantum Circuit Distillation and Compression

Quantum coherence in a qubit is vulnerable to environmental noise. When long quantum calculation is run on a quantum processor without error correction, the noise often causes fatal errors and messes up the calculation. Here, we propose quantum-circuit distillation to generate quantum circuits that are short but have enough functions to produce an output almost identical to that of the original circuits. The distilled circuits are less sensitive to the noise and can complete calculation before the quantum coherence is broken in the qubits. We created a quantum-circuit distillator by building a reinforcement learning model, and applied it to the inverse quantum Fourier transform (IQFT) and Shor's quantum prime factorization. The obtained distilled circuit allows correct calculation on IBM-Quantum processors. By working with the quantum-circuit distillator, we also found a general rule to generate quantum circuits approximating the general $n$-qubit IQFTs. The quantum-circuit distillator offers a new approach to improve performance of noisy quantum processors.

2309.01911v1

Probing Quantum Telecloning on Superconducting Quantum Processors

Quantum information can not be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typically referred as quantum cloning, and quantum teleportation. Quantum telecloning allows approximate copies of quantum information to be constructed by separate parties, using the classical results of a Bell measurement made on a prepared quantum telecloning state. Quantum telecloning can be implemented as a circuit on quantum computers using a classical co-processor to compute classical feed forward instructions using if statements based on the results of a mid-circuit Bell measurement in real time. We present universal, symmetric, optimal $1 \rightarrow M$ telecloning circuits, and experimentally demonstrate these quantum telecloning circuits for $M=2$ up to $M=10$, natively executed with real time classical control systems on IBM Quantum superconducting processors, known as dynamic circuits. We perform the cloning procedure on many different message states across the Bloch sphere, on $7$ IBM Quantum processors, optionally using the error suppression technique X-X sequence digital dynamical decoupling. Two circuit optimizations are utilized, one which removes ancilla qubits for $M=2, 3$, and one which reduces the total number of gates in the circuit but still uses ancilla qubits. Parallel single qubit tomography with MLE density matrix reconstruction is used in order to compute the mixed state density matrices of the clone qubits, and clone quality is measured using quantum fidelity. These results present one of the largest and most comprehensive NISQ computer experimental analyses on (single qubit) quantum telecloning to date. The clone fidelity sharply decreases to $0.5$ for $M > 5$, but for $M=2$ we are able to achieve a mean clone fidelity of up to $0.79$ using dynamical decoupling.

2308.15579v3

Investigating how to simulate lattice gauge theories on a quantum computer

Quantum computers have the potential to expand the utility of lattice gauge theory to investigate non-perturbative particle physics phenomena that cannot be accessed using a standard Monte Carlo method due to the sign problem. Thanks to the qubit, quantum computers can store Hilbert space in a more efficient way compared to classical computers. This allows the Hamiltonian approach to be computationally feasible, leading to absolute freedom from the sign-problem. But what the current noisy intermediate scale quantum hardware can achieve is under investigation, and therefore we chose to study the energy spectrum and the time evolution of an SU(2) theory using two kinds of quantum hardware: the D-Wave quantum annealer and the IBM gate-based quantum hardware.

2308.15421v1

Quantum Computing for Solid Mechanics and Structural Engineering -- a Demonstration with Variational Quantum Eigensolver

Variational quantum algorithms exploit the features of superposition and entanglement to optimize a cost function efficiently by manipulating the quantum states. They are suitable for noisy intermediate-scale quantum (NISQ) computers that recently became accessible to the worldwide research community. Here, we implement and demonstrate the numerical processes on the 5-qubit and 7-qubit quantum processors on the IBM Qiskit Runtime platform. We combine the commercial finite-element-method (FEM) software ABAQUS with the implementation of Variational Quantum Eigensolver (VQE) to establish an integrated pipeline. Three examples are used to investigate the performance: a hexagonal truss, a Timoshenko beam, and a plane-strain continuum. We conduct parametric studies on the convergence of fundamental natural frequency estimation using this hybrid quantum-classical approach. Our findings can be extended to problems with many more degrees of freedom when quantum computers with hundreds of qubits become available in the near future.

2308.14745v1

Single Qubit State Estimation on NISQ Devices with Limited Resources and SIC-POVMs

Current quantum computers have the potential to overcome classical computational methods, however, the capability of the algorithms that can be executed on noisy intermediate-scale quantum devices is limited due to hardware imperfections. Estimating the state of a qubit is often needed in different quantum protocols, due to the lack of direct measurements. In this paper, we consider the problem of estimating the quantum state of a qubit in a quantum processing unit without conducting direct measurements of it. We consider a parameterized measurement model to estimate the quantum state, represented as a quantum circuit, which is optimized using the quantum tomographic transfer function. We implement and test the circuit using the quantum computer of the Technical Research Centre of Finland as well as an IBM quantum computer. We demonstrate that the set of positive operator-valued measurements used for the estimation is symmetric and informationally complete. Moreover, the resources needed for qubit estimation are reduced when direct measurements are allowed, keeping the symmetric property of the measurements.

2308.07664v1

Solving The Vehicle Routing Problem via Quantum Support Vector Machines

The Vehicle Routing Problem (VRP) is an example of a combinatorial optimization problem that has attracted academic attention due to its potential use in various contexts. VRP aims to arrange vehicle deliveries to several sites in the most efficient and economical manner possible. Quantum machine learning offers a new way to obtain solutions by harnessing the natural speedups of quantum effects, although many solutions and methodologies are modified using classical tools to provide excellent approximations of the VRP. In this paper, we implement and test hybrid quantum machine learning methods for solving VRP of 3 and 4-city scenarios, which use 6 and 12 qubit circuits, respectively. The proposed method is based on quantum support vector machines (QSVMs) with a variational quantum eigensolver on a fixed or variable ansatz. Different encoding strategies are used in the experiment to transform the VRP formulation into a QSVM and solve it. Multiple optimizers from the IBM Qiskit framework are also evaluated and compared.

2308.04849v1

Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4 B bits or about 760 MB of storage (National Institute of General Medical Sciences). Today most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.

2308.04525v1

Scalable Circuits for Preparing Ground States on Digital Quantum Computers: The Schwinger Model Vacuum on 100 Qubits

The vacuum of the lattice Schwinger model is prepared on up to 100 qubits of IBM's Eagle-processor quantum computers. A new algorithm to prepare the ground state of a gapped translationally-invariant system on a quantum computer is presented, which we call Scalable Circuits ADAPT-VQE (SC-ADAPT-VQE). This algorithm uses the exponential decay of correlations between distant regions of the ground state, together with ADAPT-VQE, to construct quantum circuits for state preparation that can be scaled to arbitrarily large systems. These scalable circuits can be determined using classical computers, avoiding the challenging task of optimizing parameterized circuits on a quantum computer. SC-ADAPT-VQE is applied to the Schwinger model, and shown to be systematically improvable, with an accuracy that converges exponentially with circuit depth. Both the structure of the circuits and the deviations of prepared wavefunctions are found to become independent of the number of spatial sites, $L$. This allows for a controlled extrapolation of the circuits, determined using small or modest-sized systems, to arbitrarily large $L$. The circuits for the Schwinger model are determined on lattices up to $L=14$ (28 qubits) with the qiskit classical simulator, and subsequently scaled up to prepare the $L=50$ (100 qubits) vacuum on IBM's 127 superconducting-qubit quantum computers ibm_brisbane and ibm_cusco. After introducing an improved error-mitigation technique, which we call Operator Decoherence Renormalization, the chiral condensate and charge-charge correlators obtained from the quantum computers are found to be in good agreement with classical Matrix Product State simulations.

2308.04481v3

Simulation of IBM's kicked Ising experiment with Projected Entangled Pair Operator

We perform classical simulations of the 127-qubit kicked Ising model, which was recently emulated using a quantum circuit with error mitigation [Nature 618, 500 (2023)]. Our approach is based on the projected entangled pair operator (PEPO) in the Heisenberg picture. Its main feature is the ability to automatically identify the underlying low-rank and low-entanglement structures in the quantum circuit involving Clifford and near-Clifford gates. We assess our approach using the quantum circuit with 5+1 trotter steps which was previously considered beyond classical verification. We develop a Clifford expansion theory to compute exact expectation values and use them to evaluate algorithms. The results indicate that PEPO significantly outperforms existing methods, including the tensor network with belief propagation, the matrix product operator, and the Clifford perturbation theory, in both efficiency and accuracy. In particular, PEPO with bond dimension $\chi=2$ already gives similar accuracy to the CPT with $K=10$ and MPO with bond dimension $\chi=1024$. And PEPO with $\chi=184$ provides exact results in $3$ seconds using a single CPU. Furthermore, we apply our method to the circuit with 20 Trotter steps. We observe the monotonic and consistent convergence of the results with $\chi$, allowing us to estimate the outcome with $\chi\to\infty$ through extrapolations. We then compare the extrapolated results to those achieved in quantum hardware and with existing tensor network methods. Additionally, we discuss the potential usefulness of our approach in simulating quantum circuits, especially in scenarios involving near-Clifford circuits and quantum approximate optimization algorithms. Our approach is the first use of PEPO in solving the time evolution problem, and our results suggest it could be a powerful tool for exploring the dynamical properties of quantum many-body systems.

2308.03082v1

Møller-Plesset Perturbation Theory Calculations on Quantum Devices

Accurate electronic structure calculations might be one of the most anticipated applications of quantum computing.The recent landscape of quantum simulations within the Hartree-Fock approximation raises the prospect of substantial theory and hardware developments in this context.Here we propose a general quantum circuit for M{\o}ller-Plesset perturbation theory (MPPT) calculations, which is a popular and powerful post-Hartree-Fock method widly harnessed in solving electronic structure problems. MPPT improves on the Hartree-Fock method by including electron correlation effects wherewith Rayleigh-Schrodinger perturbation theory. Given the Hartree-Fock results, the proposed circuit is designed to estimate the second order energy corrections with MPPT methods. In addition to demonstration of the theoretical scheme, the proposed circuit is further employed to calculate the second order energy correction for the ground state of Helium atom, and the total error rate is around 2.3%. Experiments on IBM 27-qubit quantum computers express the feasibility on near term quantum devices, and the capability to estimate the second order energy correction accurately. In imitation of the classical MPPT, our approach is non-heuristic, guaranteeing that all parameters in the circuit are directly determined by the given Hartree-Fock results. Moreover, the proposed circuit shows a potential quantum speedup comparing to the traditional MPPT calculations. Our work paves the way forward the implementation of more intricate post-Hartree-Fock methods on quantum hardware, enriching the toolkit solving electronic structure problems on quantum computing platforms.

2308.01559v1

Differential Evolution VQE for Crypto-currency Arbitrage. Quantum Optimization with many local minima

Crypto-currency markets are known to exhibit inefficiencies, which presents opportunities for profitable cyclic transactions or arbitrage, where one currency is traded for another in a way that results in a net gain without incurring any risk. Quantum computing has shown promise in financial applications, particularly in resolving optimization problems like arbitrage. In this paper, we introduce a differential evolution (DE) optimization algorithm for Variational Quantum Eigensolver (VQE) using Qiskit framework. We elucidate the application of crypto-currency arbitrage using different VQE optimizers. Our findings indicate that the proposed DE-based method effectively converges to the optimal solution in scenarios where other commonly used optimizers, such as COBYLA, struggle to find the global minimum. We further test this procedure's feasibility on IBM's real quantum machines up to 127 qubits. With a three-currency scenario, the algorithm converged in 417 steps over a 12-hour period on the "ibm_geneva" machine. These results suggest the potential for achieving a quantum advantage in solving increasingly complex problems.

2308.01427v1

Dissipative mean-field theory of IBM utility experiment

In spite of remarkable recent advances, quantum computers have not yet found any useful applications. A promising direction for such utility is offered by the simulation of the dynamics of many-body quantum systems, which cannot be efficiently computed classically. Recently, IBM used a superconducting quantum computer to simulate a kicked quantum Ising model for large numbers of qubits and time steps. By employing powerful error mitigation techniques, they were able to obtain an excellent agreement with the exact solution of the model. This result is very surprising, considering that the total error accumulated by the circuit is prohibitively large. In this letter, we address this paradox by introducing a dissipative mean-field approximation based on Kraus operators. Our effective theory reproduces the many-body unitary dynamics and matches quantitatively local and non-local observables. These findings demonstrate that the observed dynamics is equivalent to a single qubit undergoing rotations and dephasing. Our emergent description can explain the success of the quantum computer in solving this specific problem.

2308.01339v1

Scalable quantum measurement error mitigation via conditional independence and transfer learning

Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation has exhibited advantages over the linear inversion method due to its capability to correct non-linear noise. However, scalability remains a challenge for both methods. In this study, we propose a scalable quantum measurement error mitigation method that leverages the conditional independence of distant qubits and incorporates transfer learning techniques. By leveraging the conditional independence assumption, we achieve an exponential reduction in the size of neural networks used for error mitigation. This enhancement also offers the benefit of reducing the number of training data needed for the machine learning model to successfully converge. Additionally, incorporating transfer learning provides a constant speedup. We validate the effectiveness of our approach through experiments conducted on IBM quantum devices with 7 and 13 qubits, demonstrating excellent error mitigation performance and highlighting the efficiency of our method.

2308.00320v1

Quantum simulation of Pauli channels and dynamical maps: algorithm and implementation

Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported.

2308.00188v1

Hybrid quantum transfer learning for crack image classification on NISQ hardware

Quantum computers possess the potential to process data using a remarkably reduced number of qubits compared to conventional bits, as per theoretical foundations. However, recent experiments have indicated that the practical feasibility of retrieving an image from its quantum encoded version is currently limited to very small image sizes. Despite this constraint, variational quantum machine learning algorithms can still be employed in the current noisy intermediate scale quantum (NISQ) era. An example is a hybrid quantum machine learning approach for edge detection. In our study, we present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm\_simulator and real backends, offering insights into their execution efficiency.

2307.16723v1

Improving Transmon Qudit Measurement on IBM Quantum Hardware

The Hilbert space of a physical qubit typically features more than two energy levels. Using states outside the qubit subspace can provide advantages in quantum computation. To benefit from these advantages, individual states of the $d$-dimensional qudit Hilbert space have to be discriminated during readout. We propose and analyze two measurement strategies that improve the distinguishability of transmon qudit states. Based on a model describing the readout of a transmon qudit coupled to a resonator, we identify the regime in hardware parameter space where each strategy is optimal. We discuss these strategies in the context of a practical implementation of the default measurement of a ququart on IBM Quantum hardware whose states are prepared by employing higher-order $X$ gates that make use of two-photon transitions.

2307.13504v2

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits(VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely ST-VQC, to integrate non-linearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

2307.09771v1

A Hybrid Quantum-Classical Generative Adversarial Network for Near-Term Quantum Processors

In this article, we present a hybrid quantum-classical generative adversarial network (GAN) for near-term quantum processors. The hybrid GAN comprises a generator and a discriminator quantum neural network (QNN). The generator network is realized using an angle encoding quantum circuit and a variational quantum ansatz. The discriminator network is realized using multi-stage trainable encoding quantum circuits. A modular design approach is proposed for the QNNs which enables control on their depth to compromise between accuracy and circuit complexity. Gradient of the loss functions for the generator and discriminator networks are derived using the same quantum circuits used for their implementation. This prevents the need for extra quantum circuits or auxiliary qubits. The quantum simulations are performed using the IBM Qiskit open-source software development kit (SDK), while the training of the hybrid quantum-classical GAN is conducted using the mini-batch stochastic gradient descent (SGD) optimization on a classic computer. The hybrid quantum-classical GAN is implemented using a two-qubit system with different discriminator network structures. The hybrid GAN realized using a five-stage discriminator network, comprises 63 quantum gates and 31 trainable parameters, and achieves the Kullback-Leibler (KL) and the Jensen-Shannon (JS) divergence scores of 0.39 and 0.52, respectively, for similarity between the real and generated data distributions.

2307.03269v2

Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing.

2307.03236v1

Classical benchmarking of zero noise extrapolation beyond the exactly-verifiable regime

In a recent work a quantum error mitigation protocol was applied to the expectation values obtained from circuits on the IBM Eagle quantum processor with up $127$ - qubits with up to $60 \; - \; \mbox{CNOT}$ layers. To benchmark the efficacy of this quantum protocol a physically motivated quantum circuit family was considered that allowed access to exact solutions in different regimes. The family interpolated between Clifford circuits and was additionally evaluated at low depth where exact validation is practical. It was observed that for highly entangling parameter regimes the circuits are beyond the validation of matrix product state and isometric tensor network state approximation methods. Here we compare the experimental results to matrix product operator simulations of the Heisenberg evolution, find they provide a closer approximation than these pure-state methods by exploiting the closeness to Clifford circuits and limited operator growth. Recently other approximation methods have been used to simulate the full circuit up to its largest extent. We observe a discrepancy of up to $20\%$ among the different classical approaches so far, an uncertainty comparable to the bootstrapped error bars of the experiment. Based on the different approximation schemes we propose modifications to the original circuit family that challenge the particular classical methods discussed here.

2306.17839v1

Efficient sampling of noisy shallow circuits via monitored unraveling

We introduce a classical algorithm for sampling the output of shallow, noisy random circuits on two-dimensional qubit arrays. The algorithm builds on the recently-proposed "space-evolving block decimation" (SEBD) and extends it to the case of noisy circuits. SEBD is based on a mapping of 2D unitary circuits to 1D {\it monitored} ones, which feature measurements alongside unitary gates; it exploits the presence of a measurement-induced entanglement phase transition to achieve efficient (approximate) sampling below a finite critical depth $T_c$. Our noisy-SEBD algorithm unravels the action of noise into measurements, further lowering entanglement and enabling efficient classical sampling up to larger circuit depths. We analyze a class of physically-relevant noise models (unital qubit channels) within a two-replica statistical mechanics treatment, finding weak measurements to be the optimal (i.e. most disentangling) unraveling. We then locate the noisy-SEBD complexity transition as a function of circuit depth and noise strength in realistic circuit models. As an illustrative example, we show that circuits on heavy-hexagon qubit arrays with noise rates of $\approx 2\%$ per CNOT, based on IBM Quantum processors, can be efficiently sampled up to a depth of 5 iSWAP (or 10 CNOT) gate layers. Our results help sharpen the requirements for practical hardness of simulation of noisy hardware.

2306.16455v2

Fast classical simulation of evidence for the utility of quantum computing before fault tolerance

We show that a classical algorithm based on sparse Pauli dynamics can efficiently simulate quantum circuits studied in a recent experiment on 127 qubits of IBM's Eagle processor [Nature 618, 500 (2023)]. Our classical simulations on a single core of a laptop are orders of magnitude faster than the reported walltime of the quantum simulations, as well as faster than the estimated quantum hardware runtime without classical processing, and are in good agreement with the zero-noise extrapolated experimental results.

2306.16372v1

Efficient tensor network simulation of IBM's Eagle kicked Ising experiment

We report an accurate and efficient classical simulation of a kicked Ising quantum system on the heavy-hexagon lattice. A simulation of this system was recently performed on a 127 qubit quantum processor using noise mitigation techniques to enhance accuracy (Nature volume 618, p.~500-505 (2023)). Here we show that, by adopting a tensor network approach that reflects the geometry of the lattice and is approximately contracted using belief propagation, we can perform a classical simulation that is significantly more accurate and precise than the results obtained from the quantum processor and many other classical methods. We quantify the tree-like correlations of the wavefunction in order to explain the accuracy of our belief propagation-based approach. We also show how our method allows us to perform simulations of the system to long times in the thermodynamic limit, corresponding to a quantum computer with an infinite number of qubits. Our tensor network approach has broader applications for simulating the dynamics of quantum systems with tree-like correlations.

2306.14887v3

Relation between nonclassical features through logical qudits

Scalable modern-time fault-tolerant quantum computation and quantum communication in a network employ a large number of physical qubits. For example, IBM is reported to have made a 127-qubit quantum computer. Unlike classical computation, quantum computation employs different types of logical qubits and qudits in terms of physical multiqubit and multiqudit systems respectively. Given this, of particular interest to us is to enquire on how quantum coherence in logical qubits is a manifestation of underlying quantum correlations in constituent physical multiqubit systems and vice-versa. In a recent work [Asthana, Sooryansh. New J Phys 24.5 (2022): 053026], we have shown that there is reciprocity in nonclassical correlations in physical multiqubit systems and coherence in a single logical qubit system. Subsequently, we have generalised the framework to higher dimensional quantum systems []. The crux of this study is that a single nonclassicality condition derived for quantum coherence in a logical system detects more than one type of nonclassicality in Hilbert spaces of nonidentical dimensions.

2306.12568v1

Simulating Noisy Variational Quantum Algorithms: A Polynomial Approach

Large-scale variational quantum algorithms are widely recognized as a potential pathway to achieve practical quantum advantages. However, the presence of quantum noise might suppress and undermine these advantages, which blurs the boundaries of classical simulability. To gain further clarity on this matter, we present a novel polynomial-scale method based on the path integral of observable's back-propagation on Pauli paths (OBPPP). This method efficiently approximates expectation values of operators in variational quantum algorithms with bounded truncation error in the presence of single-qubit Pauli noise. Theoretically, we rigorously prove: 1) For a constant minimal non-zero noise rate $\gamma$, OBPPP's time and space complexity exhibit a polynomial relationship with the number of qubits $n$, the circuit depth $L$. 2) For variable $\gamma$, in scenarios where more than two non-zero noise factors exist, the complexity remains $\mathrm{Poly}\left(n,L\right)$ if $\gamma$ exceeds $1/\log{L}$, but grows exponential with $L$ when $\gamma$ falls below $1/L$. Numerically, we conduct classical simulations of IBM's zero-noise extrapolated experimental results on the 127-qubit Eagle processor [Nature \textbf{618}, 500 (2023)]. Our method attains higher accuracy and faster runtime compared to the quantum device. Furthermore, our approach allows us to simulate noisy outcomes, enabling accurate reproduction of IBM's unmitigated results that directly correspond to raw experimental observations. Our research reveals the vital role of noise in classical simulations and the derived method is general in computing the expected value for a broad class of quantum circuits and can be applied in the verification of quantum computers.

2306.05804v3

Non-adaptive measurement-based quantum computation on IBM Q

We test the quantumness of IBM's quantum computer IBM Quantum System One in Ehningen, Germany. We generate generalised n-qubit GHZ states and measure Bell inequalities to investigate the n-party entanglement of the GHZ states. The implemented Bell inequalities are derived from non-adaptive measurement-based quantum computation (NMQC), a type of quantum computing that links the successful computation of a non-linear function to the violation of a multipartite Bell-inequality. The goal is to compute a multivariate Boolean function that clearly differentiates non-local correlations from local hidden variables (LHVs). Since it has been shown that LHVs can only compute linear functions, whereas quantum correlations are capable of outputting every possible Boolean function it thus serves as an indicator of multipartite entanglement. Here, we compute various non-linear functions with NMQC on IBM's quantum computer IBM Quantum System One and thereby demonstrate that the presented method can be used to characterize quantum devices. We find a violation for a maximum of seven qubits and compare our results to an existing implementation of NMQC using photons.

2306.03939v1

On sampling determinantal and Pfaffian point processes on a quantum computer

DPPs were introduced by Macchi as a model in quantum optics the 1970s. Since then, they have been widely used as models and subsampling tools in statistics and computer science. Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum computer is easier than on a classical one. We focus here on DPPs over a finite state space, which are distributions over the subsets of $\{1,\dots,N\}$ parametrized by an $N\times N$ Hermitian kernel matrix. Vanilla sampling consists in two steps, of respective costs $\mathcal{O}(N^3)$ and $\mathcal{O}(Nr^2)$ operations on a classical computer, where $r$ is the rank of the kernel matrix. A large first part of the current paper consists in explaining why the state-of-the-art in quantum simulation of fermionic systems already yields quantum DPP sampling algorithms. We then modify existing quantum circuits, and discuss their insertion in a full DPP sampling pipeline that starts from practical kernel specifications. The bottom line is that, with $P$ (classical) parallel processors, we can divide the preprocessing cost by $P$ and build a quantum circuit with $\mathcal{O}(Nr)$ gates that sample a given DPP, with depth varying from $\mathcal{O}(N)$ to $\mathcal{O}(r\log N)$ depending on qubit-communication constraints on the target machine. We also connect existing work on the simulation of superconductors to Pfaffian point processes, which generalize DPPs and would be a natural addition to the machine learner's toolbox. In particular, we describe "projective" Pfaffian point processes, the cardinality of which has constant parity, almost surely. Finally, the circuits are empirically validated on a classical simulator and on 5-qubit IBM machines.

2305.15851v3

Fast Partitioning of Pauli Strings into Commuting Families for Optimal Expectation Value Measurements of Dense Operators

The Pauli strings appearing in the decomposition of an operator can be can be grouped into commuting families, reducing the number of quantum circuits needed to measure the expectation value of the operator. We detail an algorithm to completely partition the full set of Pauli strings acting on any number of qubits into the minimal number of sets of commuting families, and we provide python code to perform the partitioning. The partitioning method scales linearly with the size of the set of Pauli strings and it naturally provides a fast method of diagonalizing the commuting families with quantum gates. We provide a package that integrates the partitioning into Qiskit, and use this to benchmark the algorithm with dense Hamiltonians, such as those that arise in matrix quantum mechanics models, on IBM hardware. We demonstrate computational speedups close to the theoretical limit of $(3/2)^m$ relative to qubit-wise commuting groupings, for $m=2,\dotsc,6$ qubits.

2305.11847v2

Lattice Experiments using Fermionic Operators and the Variational Eigensolver in a Quantum Computer

This work describes a series of experiments in IBM's 16-qubit Guadalupe quantum processor to find the ground state of various lattice systems implemented in the Qiskit library. We aim to design a Variational Quantum Eigensolver (QVE) resistant to noise and independent of the number of vertices in the lattice. Furthermore, we test our solution against two Ising models very important in the study of critical points and phase transitions of magnetic systems as well as high-temperature superconductors, and quantum magnetism and charge density. We provide complete result metrics including final energies, precision percentages, execution times, angular parameters and source code for experimentation.

2305.11329v1

A Feasible Semi-quantum Private Comparison Based on Entanglement Swapping of Bell States

Semi-quantum private comparison (SQPC) enables two classical users with limited quantum capabilities to compare confidential information using a semi-honest third party (TP) with full quantum power. However, entanglement swapping, as an important property of quantum mechanics in previously proposed SQPC protocols is usually neglected. In this paper, we propose a feasible SQPC protocol based on the entanglement swapping of Bell states, where two classical users do not require additional implementation of the semi-quantum key distribution protocol to ensure the security of their private data. Security analysis shows that our protocol is resilient to both external and internal attacks. To verify the feasibility and correctness of the proposed SQPC protocol, we design and simulate the corresponding quantum circuits using IBM Qiskit. Finally, we compare and discuss the proposed protocol with previous similar work. The results reveal that our protocol maintains high qubit efficiency, even when entanglement swapping is employed. Consequently, our proposed approach showcases the potential applications of entanglement swapping in the field of semi-quantum cryptography.

2305.07467v2

Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques

Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabling seamless execution of hybrid applications that combine quantum and classical components. Quantum devices vary in features, e.g., number of qubits, quantum volume, CLOPS, noise profile, queuing delays and resource cost. So, it may be useful to split hybrid workloads with either large quantum circuits or large number of quantum circuits, into smaller units. In this paper, we profile two workload splitting techniques on IBM's Quantum Cloud: (1) Circuit parallelization, to split one large circuit into multiple smaller ones, and (2) Data parallelization to split a large number of circuits run on one hardware to smaller batches of circuits run on different hardware. These can improve the utilization of heterogenous quantum hardware, but involve trade-offs. We evaluate these techniques on two key algorithmic classes: Variational Quantum Eigensolver (VQE) and Quantum Support Vector Machine (QSVM), and measure the impact on circuit execution times, pre- and post-processing overhead, and quality of the result relative to a baseline without parallelization. Results are obtained on real hardware and complemented by simulations. We see that (1) VQE with circuit cutting is ~39\% better in ground state estimation than the uncut version, and (2) QSVM that combines data parallelization with reduced feature set yields upto 3x improvement in quantum workload execution time and reduces quantum resource use by 3x, while providing comparable accuracy. Error mitigation can improve the accuracy by ~7\% and resource foot-print by ~4\% compared to the best case among the considered scenarios.

2305.06585v1

Use VQE to calculate the ground energy of hydrogen molecules on IBM Quantum

Quantum computing has emerged as a promising technology for solving problems that are intractable for classical computers. In this study, we introduce quantum computing and implement the Variational Quantum Eigensolver (VQE) algorithm using Qiskit on the IBM Quantum platform to calculate the ground state energy of a hydrogen molecule. We provide a theoretical framework of quantum mechanics, qubits, quantum gates, and the VQE algorithm. Our implementation process is described, and we simulate the results. Additionally, experiments are conducted on the IBM Quantum platform, and the results are analyzed. Our fi ndings demonstrate that VQE can effi ciently calculate molecular properties with high accuracy. However, limitations and challenges in scaling the algorithm for larger molecules are also identifi ed. This work contributes to the growing body of research on quantum computing and highlights the potential applications of VQE for real-world problem-solving.

2305.06538v1

Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These supervised classifiers often do not generalize well to novel malware. Therefore, they need to be re-trained frequently to detect new malware specimens, which can be time-consuming. Our work addresses this problem in a hybrid framework of theoretical Quantum ML, combined with feature selection strategies to reduce the data size and malware classifier training time. The preliminary results show that VQC with XGBoost selected features can get a 78.91% test accuracy on the simulator. The average accuracy for the model trained using the features selected with XGBoost was 74% (+- 11.35%) on the IBM 5 qubits machines.

2305.02396v2

Fast quantum gate design with deep reinforcement learning using real-time feedback on readout signals

The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first agent constructs the qubit in-phase control pulse using a policy learned from rewards that compensate short gate times. The rewards are obtained at intermediate time steps throughout the construction of a full-length pulse, allowing the agent to explore the landscape of shorter pulses. The second agent determines an out-of-phase pulse to target leakage. Both agents are trained on real-time data from noisy hardware, thus providing model-free gate design that adapts to unpredictable hardware noise. To reduce the effect of measurement classification errors, the agents are trained directly on the readout signal from probing the qubit. We present proof-of-concept experiments by designing X and square root of X gates of various durations on IBM hardware. After just 200 training iterations, our algorithm is able to construct novel control pulses up to two times faster than the default IBM gates, while matching their performance in terms of state fidelity and leakage rate. As the length of our custom control pulses increases, they begin to outperform the default gates. Improvements to the speed and fidelity of gate operations open the way for higher circuit depth in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.

2305.01169v1

Quantum correlation generation capability of experimental processes

Einstein-Podolsky-Rosen (EPR) steering and Bell nonlocality illustrate two different kinds of correlations predicted by quantum mechanics. They not only motivate the exploration of the foundation of quantum mechanics, but also serve as important resources for quantum-information processing in the presence of untrusted measurement apparatuses. Herein, we introduce a method for characterizing the creation of EPR steering and Bell nonlocality for dynamical processes in experiments. We show that the capability of an experimental process to create quantum correlations can be quantified and identified simply by preparing separable states as test inputs of the process and then performing local measurements on single qubits of the corresponding outputs. This finding enables the construction of objective benchmarks for the two-qubit controlled operations used to perform universal quantum computation. We demonstrate this utility by examining the experimental capability of creating quantum correlations with the controlled-phase operations on the IBM Quantum Experience and Amazon Braket Rigetti superconducting quantum computers. The results show that our method provides a useful diagnostic tool for evaluating the primitive operations of nonclassical correlation creation in noisy intermediate scale quantum devices.

2305.00370v1

Classical Chaos in Quantum Computers

The development of quantum computing hardware is facing the challenge that current-day quantum processors, comprising 50-100 qubits, already operate outside the range of quantum simulation on classical computers. In this paper we demonstrate that the simulation of classical limits can be a potent diagnostic tool potentially mitigating this problem. As a testbed for our approach we consider the transmon qubit processor, a computing platform in which the coupling of large numbers of nonlinear quantum oscillators may trigger destabilizing chaotic resonances. We find that classical and quantum simulations lead to similar stability metrics (classical Lyapunov exponents vs. quantum wave function participation ratios) in systems with $\mathcal{O}(10)$ transmons. However, the big advantage of classical simulation is that it can be pushed to large systems comprising up to thousands of qubits. We exhibit the utility of this classical toolbox by simulating all current IBM transmon chips, including the recently announced 433-qubit processor of the Osprey generation, as well as future devices with 1,121 qubits (Condor generation). For realistic system parameters, we find a systematic increase of Lyapunov exponents with system size, suggesting that larger layouts require added efforts in information protection.

2304.14435v2