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Reinforcement learning assisted recursive QAOA

EPJ quantum TECHNOLOGY 2024年第1期11卷 6页

作者： Patel, Yash J. Jerbi, Sofiene Back, Thomas Dunjko, Vedran Leiden Univ LIACS Leiden Netherlands Leiden Univ Appl Quantum Algorithms Leiden Netherlands Univ Innsbruck Inst Theoret Phys Innsbruck Austria

In recent years, variational quantum algorithms such as the quantum Approximation optimization algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

关键词： quantum computing Combinatorial optimization quantum approximate optimization algorithm Reinforcement learning

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optimization of Reinforcement Learning Using quantum Computation

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IEEE ACCESS 2024年 12卷 179396-179417页

作者： Ravish, Roopa Bhat, Nischal R. Nandakumar, N. Sagar, S. Sunil, Prasad B. Honnavalli, Prasad B. PES Univ Dept Comp Sci & Engn Ring Rd Campus Bengaluru 560085 Karnataka India

Exploring the convergence of quantum computing and machine learning, this paper delves into quantum Reinforcement Learning (QRL) with a specific focus on Variational quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from quantum Computing, experiments reveal QRL's potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.

关键词： quantum computing Qubit quantum circuit optimization Training Q-learning Logic gates Encoding Decision making Computers Advantage actor critic deep Q network policy gradient Q learning quantum approximate optimization algorithm quantum computing reinforcement learning variational quantum circuit

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A quantum Approach to the Problem of Charging Electric Cars on a Motorway

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ENERGIES 2023年第1期16卷 442页

作者： Rozycki, Rafal Jozefowska, Joanna Kurowski, Krzysztof Lemanski, Tomasz Pecyna, Tomasz Subocz, Marek Waligora, Grzegorz Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Poznan Supercomp & Networking Ctr Inst Bioorgan Chem PL-61139 Poznan Poland

In this paper, the problem of charging electric motor vehicles on a motorway is considered. Charging points are located alongside the motorway. It is assumed that there are a number of vehicles on a given section of a motorway. In the motorway, there are several nodes, and for each vehicle, the entering and the leaving nodes are known, as well as the time of entrance. For each vehicle, we know the total capacity of its battery, and the current amount of energy in the battery when entering the motorway. It is also assumed that for each vehicle, there is a finite set of speeds it can use when traveling the motorway. The speed is chosen when entering the motorway, and cannot be changed before reaching the charging station. For each speed, there is given a corresponding power usage;the higher the speed, the larger the power usage. Each vehicle can only use one charger, and when its battery is full, the amount of energy is sufficient for reaching the outgoing node. We look for a feasible solution to the problem, i.e., a solution in which no vehicle has to wait for a charger. The problem is formulated as a problem of scheduling independent, nonpreemptable jobs in parallel, unrelated machines under an additional doubly constrained resource, which is power. quantum approaches to solve the defined problem are proposed. They use the quantum approximate optimization algorithm and the quantum annealing technique. A computational experiment is presented and discussed. Some conclusions and directions for future research are given.

关键词： electric motor vehicle battery charging power energy scheduling parallel unrelated machines quantum computing quantum approximate optimization algorithm quantum annealing

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Network Community Detection on Small quantum Computers

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ADVANCED quantum TECHNOLOGIES 2019年第9期2卷

作者： Shaydulin, Ruslan Ushijima-Mwesigwa, Hayato Safro, Ilya Mniszewski, Susan Alexeev, Yuri Clemson Univ Sch Comp Clemson SC 29634 USA Los Alamos Natl Lab Comp Computat & Stat Sci Div Los Alamos NM 87545 USA Argonne Natl Lab Computat Sci Div Lemont IL 60439 USA

In recent years, a number of quantum computing devices with small numbers of qubits have become available. A hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size is presented. The proposed approach is applied to the network community detection problem. QLS is hardware-agnostic and easily extendable to new quantum computing devices as they become available. It is demonstrated to solve the 2-community detection problem on graphs of sizes of up to 410 vertices using the 16-qubit IBM quantum computer and D-Wave 2000Q, and compare their performance with the optimal solutions. The results herein demonstrate that QLS performs similarly in terms of quality of the solution and the number of iterations to convergence on both types of quantum computers and it is capable of achieving results comparable to state-of-the-art solvers in terms of quality of the solution including reaching the optimal solutions.

关键词： graph clustering quantum annealing quantum approximate optimization algorithm

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Ising Hamiltonians for Constrained Combinatorial optimization Problems and the Metropolis-Hastings Warm-Starting algorithm

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ADVANCED quantum TECHNOLOGIES 2023年第9期6卷

作者： Li, Hui-Min Liang, Jin-Min Wang, Zhi-Xi Fei, Shao-Ming Capital Normal Univ Sch Math Sci Beijing 100048 Peoples R China

quantum approximate optimization algorithm (QAOA) is a promising variational quantum algorithm for combinatorial optimization problems. However, the implementation of QAOA is limited due to the requirement that the problems be mapped to Ising Hamiltonians and the nonconvex optimization landscapes. Although the Ising Hamiltonians for many NP hard problems have been obtained, a general method to obtain the Ising Hamiltonians for constrained combinatorial optimization problems (CCOPs) has not yet been investigated. In this paper, a general method is introduced to obtain the Ising Hamiltonians for CCOPs and the Metropolis-Hastings warm-starting algorithm for QAOA is presented which can provably converge to the global optimal solutions. The effectiveness of this method is demonstrated by tackling the minimum weight vertex cover (MWVC) problem, the minimum vertex cover (MVC) problem, and the maximal independent set problem as examples. The Ising Hamiltonian for the MWVC problem is obtained first time by using this method. The advantages of the Metropolis-Hastings warm-starting algorithm presented here is numerically analyzed through solving 30 randomly generated MVC cases with 1-depth QAOA.

关键词： constrained combinatorial optimization Metropolis-Hastings minimum weight vertex cover quantum approximate optimization algorithm

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Improving quantum optimization algorithms by Constraint Relaxation

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8099-8099页

作者： Pecyna, Tomasz Rozycki, Rafal IBCH PAS Poznan Supercomp & Networking Ctr PL-61139 Poznan Poland Poznan Univ Tech Inst Comp Sci Poznan Poland

quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existing techniques attempt to mitigate these issues through employing different Hamiltonian encodings or Hamiltonian clause pruning, but they often rely on optimistic assumptions rather than a deep analysis of the problem structure. We demonstrate how to formulate the problem Hamiltonian for a quantum approximate optimization algorithm that satisfies all the requirements to correctly describe the considered tactical aircraft deconfliction problem, achieving higher probabilities for finding solutions compared to previous works. Our results indicate that constructing Hamiltonians from an unconventional, quantum-specific perspective with a high degree of entanglement results in a linear instead of exponential number of entanglement gates instead and superior performance compared to standard formulations. Specifically, we achieve a higher probability of finding feasible solutions: finding solutions in nine out of nine instances compared to standard Hamiltonian formulations and quadratic programming formulations known from quantum annealers, which only found solutions in seven out of nine instances. These findings suggest that there is substantial potential for further research in quantum Hamiltonian design and that gate-based approaches may offer superior optimization performance over quantum annealers in the future.

关键词： quantum computing quantum optimization quantum approximate optimization algorithm tactical aircraft deconfliction problem quadratic unconstrained binary optimization Hamiltonian noisy intermediate-scale quantum era

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Secure and Reliable Big-Data-Based Decision Making Using quantum Approach in IIoT Systems

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SENSORS 2023年第10期23卷 4852-4852页

作者： EL Azzaoui, Abir Salim, Mikail Mohammed Park, Jong Hyuk Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul 01811 South Korea

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs;instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

关键词： smart factory IIoT quantum algorithms quantum neural network quantum approximate optimization algorithm

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Opportunities and Challenges of quantum Computing for Engineering optimization

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第6期23卷 060817-060817页

作者： Wang, Yan Kim, Jungin E. Suresh, Krishnan Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA Univ Wisconsin Dept Mech Engn Madison WI 53706 USA

quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be utilized in solving optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.

关键词： quantum algorithm Grover search quantum phase estimation quantum annealing quantum approximate optimization algorithm variational quantum eigensolver quantum walk computational foundations for engineering optimization topology and shape optimization

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Globally Optimizing QAOA Circuit Depth for Constrained optimization Problems

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algorithmS 2021年第10期14卷 294-294页

作者： Herrman, Rebekah Treffert, Lorna Ostrowski, James Lotshaw, Phillip C. Humble, Travis S. Siopsis, George Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Oak Ridge Natl Lab Quantum Comp Inst Oak Ridge TN 37830 USA Univ Tennessee Dept Phys & Astron Knoxville TN 37996 USA

We develop a global variable substitution method that reduces n-variable monomials in combinatorial optimization problems to equivalent instances with monomials in fewer variables. We apply this technique to 3-SAT and analyze the optimal quantum unitary circuit depth needed to solve the reduced problem using the quantum approximate optimization algorithm. For benchmark 3-SAT problems, we find that the upper bound of the unitary circuit depth is smaller when the problem is formulated as a product and uses the substitution method to decompose gates than when the problem is written in the linear formulation, which requires no decomposition.

关键词： quantum approximate optimization algorithm circuit depth 3-SAT

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