Reviewerly/exHarmony huggingface dataset:
A Hybrid Classical Quantum Computing Approach to the Satellite Mission Planning Problem

Hundreds of satellites equipped with cameras orbit the Earth to capture images from locations for various purposes. Since the field of view of the cameras is usually very narrow, the optics have to be adjusted and rotated between single shots of different locations. This is even further complicated by the fixed speed-determined by the satellite's altitude-such that the decision what locations to select for imaging becomes even more complex. Therefore, classical algorithms for this Satellite Mission Planning Problem (SMPP) have already been proposed decades ago. However, corresponding classical solutions have only seen evolutionary enhancements since then. Quantum computing and its promises, on the other hand, provide the potential for revolutionary improvement. Therefore, in this work, we propose a hybrid classical quantum computing approach to solve the SMPP combining the advantages of quantum hardware with decades of classical optimizer development. Using the Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and its warm-start variant (W-QAOA), we demonstrate the applicability of solving the SMPP for up to 21 locations to choose from. This proof-of-concept-which is avail-able on GitHub (https://github.com/cda-tum/mqt-problemsolver) as part of the Munich Quantum Toolkit (MQT)-showcases the potential of quantum computing in this application domain and represents a first step toward competing with classical algorithms in the future.
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel

Anomaly detection, an important task in data analysis, involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden patterns and deviations in large-scale data. The potential of quantum computing for machine learning has been widely recognized, leading to extensive research efforts to develop suitable quantum machine learning (QML) algorithms. In particular, the search for QML algorithms for near-term NISQ devices is in full swing. However, NISQ devices pose additional challenges due to their limited qubit coherence times, low number of qubits, and high error rates. Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints. Especially support vector machines (SVM) utilizing quantum kernel estimation have shown success in various supervised learning tasks. However, in the context of anomaly detection, semisupervised learning is of great relevance, and yet there is limited research published in this area. This paper introduces an approach to semisupervised anomaly detection based on the reconstruction loss of a support vector regression (SVR) with quantum kernel. This novel model is an alternative to the variational quantum and quantum kernel one-class classifiers, and is compared to a quantum autoencoder as quantum baseline and a SVR with radial-basis-function (RBF) kernel as well as a classical autoencoder as classical baselines. The models are benchmarked extensively on 10 real-world anomaly detection data sets and one toy data set and it is shown that our SVR model with quantum kernel performs better than the SVR with RBF kernel as well as all other models, achieving highest mean AUC over all data sets. In addition, our QSVR outperforms the Quantum autoencoder on 9 out of 11 data sets.
Efficient Quantum Algorithms for Nonlinear Stochastic Dynamical Systems

In this paper, we propose efficient quantum algorithms for solving nonlinear stochastic differential equations (SDE) via the associated Fokker-Planck equation (FPE). We discretize the FPE in space and time using two well-known numerical schemes, namely Chang-Cooper and implicit finite difference. We then compute the solution of the resulting system of linear equations using the quantum linear systems algorithm. We present detailed error and complexity analyses for both these schemes and demonstrate that our proposed algorithms, under certain conditions, provably compute the solution to the FPE within prescribed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> error bounds with polynomial dependence on state dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> . Classical numerical methods scale exponentially with dimension, thus, our approach, under the aforementioned conditions, provides an exponential speed-up over traditional approaches.
Experimental Free-Space Quantum Key Distribution Over a Turbulent High-Loss Channel

Free-space quantum cryptography plays an integral role in realizing a global-scale quantum internet system. Compared to fiber-based communication networks, free-space networks experience significantly less decoherence and photon loss due to the absence of birefringent effects in the atmosphere. However, the atmospheric turbulence contributes to deviation in transmittance distribution, which introduces noise and channel loss. Several methods have been proposed to overcome the low signal-to-noise ratio. Active research is currently focused on establishing secure and practical quantum communication in a high-loss channel, and enhancing the secure key rate by implementing bit rejection strategies when the channel transmittance drops below a certain threshold. By simulating the atmospheric turbulence using an acousto-optical-modulator (AOM) and implementing the prefixed-threshold real-time selection (P-RTS) method, our group performed finite-size decoy-state Bennett-Brassard 1984 (BB84) quantum key distribution (QKD) protocol for 19 dB channel loss. With better optical calibration and efficient superconducting nano-wire single photon detector (SNSPD), we have extended our previous work to 40 dB channel loss characterizing the transmittance distribution of our system under upper moderate turbulence conditions.
Towards Distributed Quantum Computing by Qubit and Gate Graph Partitioning Techniques

Distributed quantum computing is motivated by the difficulty in building large-scale, individual quantum computers. To solve that problem, a large quantum circuit is partitioned and distributed to small quantum computers for execution. Partitions running on different quantum computers share quantum information using entangled Bell pairs. However, entanglement generation and purification introduces both a runtime and memory overhead on distributed quantum computing. In this paper we study that trade-off by proposing two techniques for partitioning large quantum circuits and for distribution to small quantum computers. Our techniques map a quantum circuit to a graph representation. We study two approaches: one that considers only gate teleportation, and another that considers both gate and state teleportation to achieve the distributed execution. Then we apply the METIS graph partitioning algorithm to obtain the partitions and the number of entanglement requests between them. We use the SeQUeNCe quantum communication simulator to measure the time required for generating all the entanglements required to execute the distributed circuit. We find that the best partitioning technique will depend on the specific circuit of interest.
Near-Minimal Gate Set Tomography Experiment Designs

Gate set tomography (GST) is a technique for self-consistently and precisely estimating the entire gate set of a quantum processor. GST carries nontrivial experimental cost, and as such the use of GST in practice has so far been limited to characterizing subsets of systems of at most two qubits at anyone time. In this work we present a new protocol for streamlining GST experiment designs by stripping away nearly all of the redundancy present in a traditional GST experiment. We do so by determining how GST base circuits (called “germs”) are sensitive to variations in particular gate set parameters, and leverage this information to identify and subsequently strip out redundancy across a germ set as a whole. We present the application of this new protocol to GST experiment designs for two-qubit systems and show that we can reliably produce experiment designs approaching information theoretic lower bounds in size. We demonstrate in both simulation and in a theoretical analysis based on the Fisher information that these streamlined experiment designs nonetheless maintain the Heisenberg-like precision scaling properties of traditional GST. Moreover, we show that in practice it is possible using this technique to achieve a final precision in our estimates comparable to those of much larger experiment designs using many times fewer circuits overall. We also discuss the impacts of this technique for the prospects of scaling up the practical use of GST for three-qubit systems.
Trapped Ion Quantum Repeaters with Entanglement Distillation based on Quantum LDPC Codes

Quantum repeaters are essential to realizing long-range entanglement distribution networks. To achieve enhanced rates of high-fidelity entanglement distribution, we investigate how entanglement distillation can be used on trapped-ion-based quantum repeater networks. Entanglement distillation is the process of distilling from a large number of copies of low-fidelity entangled qubits a fewer number of copies of higher-fidelity entangled qubits. It has been shown that quantum error-correcting codes (QECCs) can be used to devise protocols for entanglement distillation. In this paper, we consider entanglement distillation based on three lifted-product (LP) quantum low density parity check (QLPDC) codes ([[544, 80, 12], [[714, 100, 16]], and [20, 10, 136, 20]) on trapped-ion repeater networks with spatial and time multiplexing over various total distances with different inter-repeater spacing to calculate the end-to-end entanglement rates that they enable. The reported rates assume entanglement over each elementary link succeeds synchronously and do not assume any constraint on the number of ions present in each trap. We furthermore assume that distillation occurs at every elementary link and that the entanglement swaps are ideal. Our findings can be considered as groundwork for implementing more efficient distillation and communication protocols on trapped ion networks.
Folding-Free ZNE: A Comprehensive Quantum Zero-Noise Extrapolation Approach for Mitigating Depolarizing and Decoherence Noise

Quantum computers in the NISQ era are prone to noise. A range of quantum error mitigation techniques has been proposed to address this issue. Zero-noise extrapolation (ZNE) stands out as a promising one. ZNE involves increasing the noise levels in a circuit and then using extrapolation to infer the zero noise case from the noisy results obtained. This paper presents a novel ZNE approach that does not require circuit folding or noise scaling to mitigate depolarizing and/or decoherence noise. To mitigate depolarizing noise, we propose leveraging the extreme/infinite noisy case, which allows us to avoid circuit folding. Specifically, the circuit output with extreme noise becomes the maximally mixed state. We show that using circuit-reliability metrics, simple linear extrapolation can effectively mitigate depolarizing noise. With decoherence noise, different states decay into the all-zero state at a rate that depends on the number of excited states and time. Therefore, we propose a state- and latency-aware exponential extrapolation that does not involve folding or scaling. When dealing with a quantum system affected by both decoherence and depolarizing noise, we propose to use our two mitigation techniques in sequence: first applying decoherence error mitigation, followed by depolarizing error mitigation. A common limitation of ZNE schemes is that if the circuit of interest suffers from high noise, scaling-up noise levels could not provide useful data for extrapolation. We propose using circuit-cut techniques to break a large quantum circuit into smaller sub-circuits to overcome this limitation. This way, the noise levels of the sub-circuits are lower than the original circuit, and ZNE can become more effective in mitigating their noises.
Approximate State Preparation Using Tensor Network Optimization Techniques

Converting classical information into a quantum circuit is an inevitable step in utilizing quantum computing. Current quantum computers are referred to as NISQ devices, which have a high level of noise without error correction, imposing strict limitations on the number of qubits and quantum gates. Therefore, when constructing quantum circuits, it becomes crucial to generate shallower circuits. Specifically, when amplitude encoding is employed for classical-quantum information conversion, analytical methods often necessitate an exponential number of quantum gates relative to the number of qubits, rendering them unsuitable for current NISQ devices. Here, we explore a method for constructing an approximately improved circuit for amplitude encoding, considering classical information from an array composed of floating-point numbers. In our method, we optimize the matrix elements of a two-qubit unitary gate using a technique similar to that used in the tensor network method. We then analytically decompose the obtained unitary matrix into a product of elementary unitary gates. Furthermore, using this as an element of the basic optimization technique, we explore a method for selectively adapting better quantum gates. We apply this method to the problem of constructing a circuit that approximately generates the ground state of the quantum spin model. Consequently, we find that better circuits can be automatically constructed without prior knowledge of classical information.
Teaching Quantum Computing Using Microsoft Quantum Development Kit and Azure Quantum

This report describes my experience teaching a graduate-level quantum computing course at Northeastern University in the academic year 2022–23. The course takes a practical, software-driven approach to the course, teaching basic quantum concepts and algorithms through hands-on programming assignments and a software-focused final project. The course guides learners through all stages of the quantum software development process, from solving quantum computing problems and implementing solutions to debugging quantum programs, optimizing the code, and running the code on quantum hardware. This report offers instructors who want to adopt a similar practical approach to teaching quantum computing a comprehensive guide to getting started.
Quantum Eigenfaces: Linear Feature Mapping and Nearest Neighbor Classification with Outlier Detection

We propose a quantum machine learning algorithm for data classification, inspired by the seminal computer vision approach of eigenfaces for face recognition. The algorithm enhances nearest neighbor/centroid classifiers with concepts from principal component analysis, enabling the automatic detection of outliers and finding use in anomaly detection domains beyond face recognition. Assuming classical input data, we formalize how to implement the algorithm using a quantum random access memory and state-of-the-art quantum linear algebra, discussing the complexity of performing the classification algorithm on a fault-tolerant quantum device. The asymptotic time complexity analysis shows that the quantum classification algorithm can be more efficient than its classical counterpart. We showcase an application of this algorithm for face recognition and image classification datasets with anomalies, obtaining promising results for the running time parameters. This work contributes to the growing field of quantum machine learning applications, and the algorithm's simplicity makes it easily adoptable by future quantum machine learning practitioners.
Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem

Many relevant problems in industrial settings result in NP-hard optimization problems, such as the Capacitated Vehicle Routing Problem (CVRP) or its reduced variant, the Travelling Salesperson Problem (TSP). Even with today's most powerful classical algorithms, the CVRP is challenging to solve classically. Quantum computing may offer a way to improve the time to solution, although the question remains open as to whether Noisy Intermediate-Scale Quantum (NISQ) devices can achieve a practical advantage compared to classical heuristics. The most prominent algorithms proposed to solve combinatorial optimization problems in the NISQ era are the Quantum Approximate Optimization Algorithm (QAOA) and the more general Variational Quantum Eigensolver (VQE). However, implementing them in a way that reliably provides high-quality solutions is challenging, even for toy examples. In this work, we discuss decomposition and formulation aspects of the CVRP and propose an application-driven way to measure solution quality. Considering current hardware constraints, we reduce the CVRP to a clustering phase and a set of TSPs. For the TSP, we extensively test both QAOA and VQE and investigate the influence of various hyperparameters, such as the classical optimizer choice and strength of constraint penalization. Results of QAOA are generally of limited quality because the algorithm does not reach the energy threshold for feasible TSP solutions, even when considering various extensions such as recursive and constraint-preserving mixer QAOA. On the other hand, the VQE reaches the energy threshold and shows a better performance. Our work outlines the obstacles to quantum-assisted solutions for real-world optimization problems and proposes perspectives on how to overcome them.
QPCC: A Quantum Programming Course for Inhomogeneous Cohorts of Professional Learners

The ongoing accelerated development and exploration of Quantum Computing (QC) - systems, software, algorithms and applications - has generated a huge amount of interest and need among a wide range of organisations and user communities to better understand the technology paradigm, its maturity, limitations and opportunities, and positioning alongside classical high-performance computing (HPC) methods and tools. This interest and need to learn about QC has been accentuated by the rapidness and breadth of developments in QC that is being disseminated in both mainstream and specialist scien-tific/technical media as well as the accessibility to early quantum computing systems and tools. It has become a challenge for the (potential) user community to sift through the material catering to developing bottom-up fundamental scientific/mathematical skills as well as top-down exploitation oriented knowledge. As the National and one among the European Competence Centres in HPC, the Irish Centre for High-End Computing (ICHEC) has worked with an international advisory board composed of quantum computing technology developers, the user community and the Irish State Agency's network that is responsible for national enterprise skills development in ICT to create and deliver a blended course which is customisable, modular and stackable, thereby offering different learning path-ways to professional learners from the academic, enterprise and public sector communities with inhomogeneous backgrounds. Particular emphasis is laid on the stakes of engaging learners and organisations with different knowledge basis, skills and learning objectives, as well as making the content suitably-positioned and approachable to all (e.g. varied levels of mathematical knowledge required for basic understanding of quantum computing versus developing quantum algorithms). In this paper, we discuss the Quantum Programming Certifi-cation Course (QPCC) by summarising the 4-year design process with the advisory board, drawing from ICHEC's experience in national and European HPC and QC R&D projects as well as preparation of strategies and joint white papers, and methods to address gaps between mainstream academic programmes in quantum technologies and existing training courses (such as MOOCs, tutorials, workshops). We discuss the approaches in QPCC to target professional learners from inhomogeneous backgrounds, tools and pedagogical approaches in preparing and delivering the training material, opportunities and challenges in positioning QPCC within the academic framework, and sum-marise open opportunities for developing collaborative solutions.
Developing Workforce in Quantum Industry: The Wond'ry Quantum Studio

The Wondry Quantum Studio (TWQS) at Vanderbilt University has been a pioneering force in the arena of quantum education and learning. Our concerted efforts have provided a fresh perspective on quantum education. Strategically collaborating with industry leaders, such as IBM Quantum, Zapata Computing, and D-Wave, TWQS has been able to amalgamate and optimize a variety of pedagogical techniques into an impactful and highly effective curriculum. This synergy not only imparts practical knowledge but also provides learners with crucial exposure to real-world quantum opportunities, thereby nurturing a competent quantum workforce. In this poster, we exhibit our noteworthy initiatives and the resulting achievements, all of which have significantly contributed to the nurturing of a robust quantum ecosystem. The highlights include our unique pedagogical approach, successful collaborations that yielded unique learning outcomes, and direct exposure of students to varied quantum opportunities. Furthermore, we underscore the importance of our core values, primarily inclusivity and collaboration. Lastly, we share the key insights and experiences gathered, shedding light on the quantum leap that has been achieved in terms of quantum education and the path that lies ahead for TWQS.
Continuous Variables Quantum Algorithm for Solving Ordinary Differential Equations

Solving Ordinary Differential Equations (ODE) is important for a broad range of domains, such as engineering, weather forecast, and finance. Most quantum algorithms proposed to solve them revolve around transforming ODEs into a system of linear equations, in order to benefit from the exponential advantage promised by the HHL algorithm. However, there are known limitations to this subroutine such as the linear scaling with condition number. In particular, for HHL-based ODE solvers, this dependency yields a complexity that generally grows exponentially with the integration time. In this paper, we present a scheme that does not rely on the HHL subroutine. We use the Koopman Von Neumann (KvN) formalism that maps arbitrary nonlinear dynamics to a Hilbert space where the dynamics are unitary. This effectively reduces the ODE problem to a Hamiltonian time evolution, which is a well-known problem in quantum computing. However, this comes at a cost as the KvN formalism is expressed in an infinite-dimensional Hilbert space, and involves nonphysical states with infinite energies. Previous works have tackled this by truncating the Hilbert space, and by approximating all the relevant states and operations on qubit-based systems. Instead of mapping to qubit-based computations, in this paper, we investigate the direct use of continuous-variable quantum computers for this problem. We provide an algorithm to compile a sequence of Gaussian and non-Gaussian continuous variables gates to solve an arbitrary one-dimensional polynomial ODE. We analyze the algorithm and propose that it is intrinsically better suited for solving so-called initial distribution problems, rather than initial condition problems. We propose the first steps towards a comprehensive complexity and specify which steps need to be developed for a complete analysis.