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Restricting to the chip architecture maintains the quantum neural network accuracy

quantum INFORMATION PROCESSING 2024年第4期23卷 131-131页

作者： Friedrich, Lucas Maziero, Jonas Univ Fed Santa Maria Ctr Nat & Exact Sci Phys Dept Roraima Ave 1000 BR-97105900 Santa Maria RS Brazil

In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum facet is characterized by a parametrization U, typically derived from the composition of various quantum gates. On the other hand, the classical component involves an optimizer that adjusts the parameters of U to minimize a cost function C. Despite the extensive applications of VQAs, several critical questions persist, such as determining the optimal gate sequence, devising efficient parameter optimization strategies, selecting appropriate cost functions, and understanding the influence of quantum chip architectures on the final results. This article aims to address the last question, emphasizing that, in general, the cost function tends to converge toward an average value as the utilized parameterization approaches a 2-design. Consequently, when the parameterization closely aligns with a 2-design, the quantum neural network model's outcome becomes less dependent on the specific parametrization. This insight leads to the possibility of leveraging the inherent architecture of quantum chips to define the parametrization for VQAs. By doing so, the need for additional swap gates is mitigated, consequently reducing the depth of VQAs and minimizing associated errors.

关键词： quantum neural networks variational quantum algorithms quantum chip architecture

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Optimizing quantum heuristics with meta-learning

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quantum MACHINE INTELLIGENCE 2021年第1期3卷 1-14页

作者： Wilson, Max Stromswold, Rachel Wudarski, Filip Hadfield, Stuart Tubman, Norm M. Rieffel, Eleanor G. NASA Ames Res Ctr Quantum Artificial Intelligence Lab QuAIL Explorat Technol Directorate Moffett Field CA 94035 USA Univ Bristol Quantum Engn CDT Bristol BS8 1TH Avon England Stinger Ghaffarian Technol Inc Greenbelt MD 20770 USA USRA Res Inst Adv Comp Sci RIACS Mountain View CA 94043 USA Nicolaus Copernicus Univ Fac Phys Astron & Informat Inst Phys Grudziadzka 5-7 PL-87100 Torun Poland

variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called "meta-learners." We compare the performance of a meta-learner to evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, for two quantum heuristics (quantum alternating operator ansatz and variational quantum eigensolver), on three problems, in three simulation environments. We show that the meta-learner comes near to the global optima more frequently than all other optimizers we tested in a noisy parameter setting environment. We also find that the meta-learner is generally more resistant to noise, for example, seeing a smaller reduction in performance in Noisy and Sampling environments, and performs better on average by a "gain" metric than its closest comparable competitor L-BFGS-B. Finally, we present evidence that indicates the meta-learner trained on small problems will generalize to larger problems. These results are an important indication that meta-learning and associated machine learning methods will be integral to the useful application of noisy near-term quantum computers.

关键词： variational quantum algorithms Meta-learning Optimization quantum alternating operator ansatz variational quantum eigensolver Long short-term memory

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quantum computing in power systems

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iEnergy 2022年第2期1卷 170-187页

作者： Yifan Zhou Zefan Tang Nima Nikmehr Pouya Babahajiani Fei Feng Tzu-Chieh Wei Honghao Zheng Peng Zhang Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Interdisciplinary Science Depart-ment Brookhaven National LaboratoryUptonNY 11973USA CN Yang Institute for Theoretical Physics and Department of Physics and Astronomy Stony Brook UniversityStony BrookNY 11794USA Smart Grid and Technology Commonwealth EdisonOakbrookIL 60523USA

Electric power systems provide the backbone of modern industrial *** scalable grid analytics is the keystone to successfully operating large transmission and distribution ***,today’s power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources,as well as managing huge volumes of data *** unprecedented challenges call for transformative analytics to support the resilient operations of power ***,the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system *** computing harnesses quantum mechanisms to solve traditionally intractable computational problems,which may lead to ultra-scalable and efficient power grid *** paper reviews the newly emerging application of quantum computing techniques in power *** present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives,including static analysis,transient analysis,stochastic analysis,optimization,stability,and *** thoroughly discuss the related quantum algorithms,their benefits and limitations,hardware implementations,and recommended *** also review the quantum networking techniques to ensure secure communication of power systems in the quantum ***,we discuss challenges and future research *** paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

关键词： quantum computing power system variational quantum algorithms quantum optimization quantum machine learning quantum security

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Expressive quantum supervised machine learning using Kerr-nonlinear parametric oscillators

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quantum MACHINE INTELLIGENCE 2024年第1期6卷 1-15页

作者： Mori, Yuichiro Nakaji, Kouhei Matsuzaki, Yuichiro Kawabata, Shiro Natl Inst Adv Ind Sci & Technol Global Res & Dev Ctr Business Quantum AI Technol G 1-1-1 Umezono Tsukuba Ibaraki 3058568 Japan Univ Toronto Dept Chem Chem Phys Theory Grp Toronto ON Canada Keio Univ Quantum Comp Ctr 3-14-1 HiyoshiKohoku Ku Yokohama Kanagawa 2238522 Japan Natl Inst Adv Ind Sci & Technol AIST Quantum Technol Cooperat Res Lab NEC Tsukuba Ibaraki 3058568 Japan

quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model in the conventional quantum computing architecture. However, the data reuploading tends to require a large amount of quantum resources, which motivates us to find an alternative strategy for realizing the expressive quantum machine learning efficiently. In this paper, we propose quantum machine learning with Kerr-nonlinear parametric oscillators (KPOs), as another promising quantum computing device. We use not only the ground state and first excited state but also higher excited states, which allows us to use a large Hilbert space even if we have a single KPO. Our numerical simulations show that the expressibility of our method with only one mode of the KPO is much higher than that of the conventional method with six qubits. Our results pave the way towards resource-efficient quantum machine learning, which is essential for the practical applications in the NISQ era.

关键词： Machine learning KPO Expressibility NISQ variational quantum algorithms

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Distributed quantum neural networks via partitioned features encoding

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quantum MACHINE INTELLIGENCE 2024年第1期6卷 1-6页

作者： Kawase, Yoshiaki Univ Tokyo Grad Sch Informat Sci & Technol 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan

quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To mitigate these challenges, an approach using distributed quantum neural networks has been proposed to make a prediction by approximating outputs of a large circuit using multiple small circuits. However, the approximation of a large circuit requires an exponential number of small circuit evaluations. Here, we instead propose to distribute partitioned features over multiple small quantum neural networks and use the ensemble of their expectation values to generate predictions. To verify our distributed approach, we demonstrate ten class classification of the Semeion and MNIST handwritten digit datasets. The results of the Semeion dataset imply that while our distributed approach may outperform a single quantum neural network in classification performance, excessive partitioning reduces performance. Nevertheless, for the MNIST dataset, we succeeded in ten class classification with exceeding 96% accuracy. Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network compared to a large single quantum neural network. Our results highlight distributed quantum neural networks as a promising direction for practical quantum machine learning algorithms compatible with near-term quantum devices. We hope that our approach is useful for exploring quantum machine learning applications.

关键词： quantum machine learning quantum neural networks variational quantum algorithms Distributed quantum neural networks Distributed quantum machine learning

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Q-GRID: quantum Optimization for the Future Energy Grid

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KUNSTLICHE INTELLIGENZ 2024年第4期38卷 339-349页

作者： Blenninger, Jonas Bucher, David Cortiana, Giorgio Ghosh, Kumar Mohseni, Naeimeh Nuesslein, Jonas O'Meara, Corey Porawski, Daniel Wimmer, Benedikt EON Digital Technol GmbH Hannover Germany Aqarios GmbH Munich Germany Ludwig Maximilians Univ Munchen Mobile & Distributed Syst Chair Munich Germany

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes.

关键词： quantum optimization Graph theory Renewable energy distribution Benchmarking quantum annealing variational quantum algorithms

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Computing graph edit distance on quantum devices

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quantum MACHINE INTELLIGENCE 2022年第2期4卷 1-21页

作者： Incudini, Massimiliano Tarocco, Fabio Mengoni, Riccardo Di Pierro, Alessandra Mandarino, Antonio Univ Verona Dipartimento Informat I-34137 Verona Italy Int Ctr Theory Quantum Technol CINECA I-40033 Casalecchio Di Reno Italy Univ Gdansk Int Ctr Theory Quantum Technol PL-80309 Gdansk Poland

Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an important notion is the Graph Edit Distance (GED) that measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. As the complexity of computing GED is the same as NP-hard problems, it is reasonable to consider approximate solutions. In this paper, we present a QUBO formulation of the GED problem. This allows us to implement two different approaches, namely quantum annealing and variational quantum algorithms, that run on the two types of quantum hardware currently available: quantum annealer and gate-based quantum computer, respectively. Considering the current state of noisy intermediate-scale quantum computers, we base our study on proof-of-principle tests of their performance.

关键词： Graph edit distance quantum annealing variational quantum algorithms

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Iteration-Free quantum approximate optimization algorithm using neural networks

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quantum MACHINE INTELLIGENCE 2024年第2期6卷 1-12页

作者： Amosy, Ohad Danzig, Tamuz Lev, Ohad Porat, Ely Chechik, Gal Makmal, Adi Bar Ilan Univ Fac Comp Sci IL-52900 Ramat Gan Israel Bar Ilan Univ Fac Engn IL-52900 Ramat Gan Israel NVIDIA Res Tel Aviv Israel

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on finding better initial circuit parameters, which would reduce the number of required iterations and hence the overall execution time. While existing methods for parameter initialization have shown great success, they often offer a single set of parameters for all problem instances. We propose a practical method that uses a simple, fully connected neural network that leverages previous executions of QAOA to find better initialization parameters tailored to a new given problem instance. We benchmark state-of-the-art initialization methods for solving the MaxCut problem of Erd & odblac;s-R & eacute;nyi graphs using QAOA and show that our method is consistently the fastest to converge while also yielding the best final result. Furthermore, the parameters predicted by the neural network are shown to match very well with the fully optimized parameters, to the extent that no iterative steps are required, thereby effectively realizing an iteration-free QAOA scheme.

关键词： Maximum cut problem quantum computing variational quantum circuits variational quantum algorithms Parametrized quantum circuits Neural network Combinatorial optimization problem QAOA

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Optimizing Parameterized quantum Circuits With Free-Axis Single-Qubit Gates

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON quantum ENGINEERING 2023年第1期4卷 1页

作者： Watanabe, Hiroshi C. Raymond, Rudy Ohnishi, Yu-Ya Kaminishi, Eriko Sugawara, Michihiko Kyushu Univ Fac Sci Dept Chem Fukuoka 8190395 Japan Keio Univ Quantum Comp Ctr Yokohama 2238522 Japan JST PRESTO Kawaguchi 3320012 Japan IBM Japan Ltd IBM Quantum Kawasaki 1038510 Japan Univ Tokyo Dept Comp Sci Tokyo 1130033 Japan JSR Corp RD Technol & Digital Transformat Ctr JSR Biosci & informat R&D Ctr Mat Informat Initiat Kawasaki 2100821 Japan

variational quantum algorithms, which utilize parameterized quantum circuits (PQCs), are promising tools to achieve quantum advantage for optimization problems on near-term quantum devices. Their PQCs have been conventionally constructed from parameterized rotational angles of single-qubit gates around a predetermined set of axes and two-qubit entangling gates, such as cnot gates. We propose a method to construct a PQC by continuous parameterization of both the angles and the axes of its single-qubit rotation gates. The method is based on the observation that when rotational angles are fixed, optimal axes of rotations can be computed by solving a system of linear equations whose coefficients can be determined from the PQC with small computational overhead. The method can be further simplified to select axes freely from continuous parameters with rotational angles fixed to $\pi$. We show the simplified free-axis selection method has better expressibility against other structural optimization methods when measured with Kullback-Leibler divergence. We also demonstrate PQCs with free-axis selection are more effective to search the ground states of Hamiltonians for condensed matter physics, quantum chemistry, and combinatorial optimization. Because free-axis selection allows designing PQCs without specifying their single-qubit rotational axes, it may significantly improve the handiness of PQCs.

关键词： Combinatorial optimization coordinate descent gradient-free method parameterized quantum circuits (PQCs) quantum chemistry quantum circuit quantum computing quantum gate variational quantum algorithms gradient-free method parameterized quantum circuits (PQCs) quantum chemistry quantum circuit quantum computing quantum gate variational quantum algorithms

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Error-Mitigation-Aided Optimization of Parameterized quantum Circuits: Convergence Analysis

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON quantum ENGINEERING 2022年第1期3卷 1页

作者： Jose, Sharu Theresa Simeone, Osvaldo Univ Birmingham Dept Comp Sci Birmingham B15 2TT England Kings Coll London Dept Engn London WC2R 2LS England

variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy intermediate-scale quantum (NISQ) processors. Such systems leverage classical optimization to tune the parameters of a parameterized quantum circuit (PQC). The goal is minimizing a cost function that depends on measurement outputs obtained from the PQC. Optimization is typically implemented via stochastic gradient descent (SGD). On NISQ computers, gate noise due to imperfections and decoherence affects the stochastic gradient estimates by introducing a bias. quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits, but they in turn cause an increase in the variance of the gradient estimates. This work studies the impact of quantum gate noise on the convergence of SGD for the variational eigensolver (VQE), a fundamental instance of VQAs. The main goal is ascertaining conditions under which QEM can enhance the performance of SGD for VQEs. It is shown that quantum gate noise induces a nonzero error-floor on the convergence error of SGD (evaluated with respect to a reference noiseless PQC), which depends on the number of noisy gates, the strength of the noise, as well as the eigenspectrum of the observable being measured and minimized. In contrast, with QEM, any arbitrarily small error can be obtained. Furthermore, for error levels attainable with or without QEM, QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently large, and a sufficiently large number of measurements is allowed at each SGD iteration. Numerical examples for a max-cut problem corroborate the main theoretical findings.

关键词： Noise measurement Qubit Convergence quantum computing quantum algorithm Optimization quantum error mitigation quantum gate noise variational quantum algorithms

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