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10th International Green and Sustainable Computing Conference (IGSC)

作者： Shaydulin, Ruslan Alexeev, Yuri Clemson Univ Sch Comp Clemson SC 29634 USA Argonne Natl Lab Computat Sci Div 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9781728154169

quantum approximate optimization algorithm (QAOA) is one of the most promising quantum algorithms for the Noisy Intermediate-Scale quantum (NISQ) era. Quantifying the performance of QAOA in the near-term regime is of utmost importance. We perform a large-scale numerical study of the approximation ratios attainable by QAOA is the low-to medium-depth regime. To find good QAOA parameters we perform 990 million 10-qubit QAOA circuit evaluations. We find that the approximation ratio increases only marginally as the depth is increased, and the gains are offset by the increasing complexity of optimizing variational parameters. We observe a high variation in approximation ratios attained by QAOA, including high variations within the same class of problem instances. We observe that the difference in approximation ratios between problem instances increases as the similarity between instances decreases. We find that optimal QAOA parameters concentrate for instances in out benchmark, confirming the previous findings for a different class of problems.

关键词： quantum approximate optimization algorithm max-cut quantum advantage

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A Reduced Complexity Method of Recursive quantum approximate optimization algorithm 14

A Reduced Complexity Method of Recursive Quantum Approximate...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Seo, Youngjin Heo, Jun Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

Among quantum algorithms, the quantum approximation optimization algorithm (QAOA) is an algorithm that finds approximate solutions. Recursive QAOA is proposed to overcome the limitation that QAOA has lower performance than Goemans-Williamson algorithm in the MAX-CUT problem [Physical Review Letter, 125, 260504, 2020]. In this paper, we introduce a method with lower complexity than RQAOA and compare its numerical results in the MAX-CUT problem. At the same level, the performance of the proposed method is lower than that of RQAOA, but the number of computations of quantum and classical parts is very small. © 2023 IEEE.

关键词： quantum algorithm quantum approximate optimization algorithm

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A quantum approximate optimization algorithm Based on Blockchain Heuristic Approach for Scalable and Secure Smart Logistics Systems

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2021年 11卷

作者： El Azzaoui, Abir Kim, Tae Woo Pan, Yi Park, Jong Hyuk Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul South Korea Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

Smart logistics and supply chain play can determine the success or failure of any business. The cost, time, and carbon footprint are critical elements to be considered. Smart logistics solely consume 53% of the company's income and produce up to 10% of its carbon footprint. Moreover, the time consumed in transportation and supply chains from the resource acquisition to the client contributes to the business profit. Enhancing smart logistics systems by selecting the optimal route is a hard problem even for today's supercomputers. On the other hand, quantum-based processing and quantum algorithms are proved to solve convoluted computation to attain a heuristic system swiftly compared with classical processing methods. Notably, quantum approximate optimization algorithm (QAOA), as a variational quantum algorithm for approximately solving discrete combinatorial optimization problems can be deployed into the smart logistics dilemma to improve the scalability of the system, decrease the time, thus reducing the carbon footprint and smart manufacturing system cost. Moreover, blockchain, as a secure distributed ledger, is capable of bringing the desired security to the smart logistic system. To this end, we propose an improved QAOA based on blockchain technology to improve the scalability and reduce the cost of smart logistics.

关键词： quantum approximate optimization algorithm Blockchain Smart Logistics Supply Chain Smart Transportation

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Lower bounds on circuit depth of the quantum approximate optimization algorithm

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quantum INFORMATION PROCESSING 2021年第2期20卷 59-59页

作者： Herrman, Rebekah Ostrowski, James 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

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. Here, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.

关键词： quantum approximate optimization algorithm Circuit depth Combinatorial optimization Chromatic index

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Improvement of quantum approximate optimization algorithm for Max-Cut Problems

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SENSORS 2022年第1期22卷 244-244页

作者： Villalba-Diez, Javier Gonzalez-Marcos, Ana Ordieres-Mere, Joaquin B. Hsch Heilbronn Fak Management & Vertrieb Campus Schwabisch Hall D-74523 Schwabisch Hall Germany Univ Politecn Madrid Complex Syst Grp Ave Puerta de Hierro 2 Madrid 28040 Spain Univ La Rioja Dept Mech Engn San Jose de Calasanz 31 Logrono 26004 Spain Univ Politecn Madrid Escuela Tecn Super Ingn Ind ETSII Jose Gutierrez Abascal 2 Madrid 28006 Spain

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value-stream network resilience. The main interest is to improve the Max-Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.

关键词： Industry 4 0 quantum approximate optimization algorithm value-stream networks optimization

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Unsupervised strategies for identifying optimal parameters in quantum approximate optimization algorithm

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EPJ quantum TECHNOLOGY 2022年第1期9卷 11-11页

作者： Moussa, Charles Wang, Hao Back, Thomas Dunjko, Vedran Leiden Univ LIACS Leiden Netherlands

As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of parameters are being tweaked to optimize a cost function out of the quantum circuit output. One of these algorithms, the quantum approximate optimization algorithm stands out as a promising approach to tackling combinatorial problems. However, finding the appropriate parameters is a difficult task. Although QAOA exhibits concentration properties, they can depend on instances characteristics that may not be easy to identify, but may nonetheless offer useful information to find good parameters. In this work, we study unsupervised Machine Learning approaches for setting these parameters without optimization. We perform clustering with the angle values but also instances encodings (using instance features or the output of a variational graph autoencoder), and compare different approaches. These angle-finding strategies can be used to reduce calls to quantum circuits when leveraging QAOA as a subroutine. We showcase them within Recursive-QAOA up to depth 3 where the number of QAOA parameters used per iteration is limited to 3, achieving a median approximation ratio of 0.94 for MaxCut over 200 Erdos-Renyi graphs. We obtain similar performances to the case where we extensively optimize the angles, hence saving numerous circuit calls.

关键词： quantum computing Combinatorial optimization quantum approximate optimization algorithm Clustering

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Classical symmetries and the quantum approximate optimization algorithm

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quantum INFORMATION PROCESSING 2021年第11期20卷 359-359页

作者： Shaydulin, Ruslan Hadfield, Stuart Hogg, Tad Safro, Ilya Argonne Natl Lab Lemont IL 60439 USA NASA Quantum Artificial Intelligence Lab QuAIL Ames Res Ctr Moffett Field CA 94035 USA KBR Houston TX 77002 USA USRA Res Inst Adv Comp Sci RIACS Mountain View CA 94043 USA Univ Delaware Comp & Informat Sci Newark DE 19716 USA

We study the relationship between the quantum approximate optimization algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function. The connection is general and includes but is not limited to problems defined on graphs. We show a series of results exploring the connection and highlight examples of hard problem classes where a nontrivial symmetry subgroup can be obtained efficiently. In particular, we show how classical objective function symmetries lead to invariant measurement outcome probabilities across states connected by such symmetries, independent of the choice of algorithm parameters or number of layers. To illustrate the power of the developed connection, we apply machine learning techniques toward predicting QAOA performance based on symmetry considerations. We provide numerical evidence that a small set of graph symmetry properties suffices to predict the minimum QAOA depth required to achieve a target approximation ratio on the MaxCut problem, in a practically important setting where QAOA parameter schedules are constrained to be linear and hence easier to optimize.

关键词： quantum approximate optimization algorithm quantum optimization

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Robust Control optimization for quantum approximate optimization algorithms 21st

Robust Control Optimization for Quantum Approximate Optimiza...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Dong, Yulong Meng, Xiang Lin, Lin Kosut, Robert Whaley, K. Birgitta Berkeley Ctr Quantum Informat & Computat Berkeley CA 94720 USA Univ Calif Berkeley Dept Chem Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA SC Solut Sunnyvale CA 94085 USA

quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation. Copyright (C) 2020 The Authors.

关键词： quantum approximate optimization algorithm robust control sequential convex programming error mitigation

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quantum computing and quantum-inspired techniques for feature subset selection: a review

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年第3期67卷 2019-2061页

作者： Mandal, Ashis Kumar Chakraborty, Basabi Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 5C9 Canada Hajee Mohammad Danesh Sci & Technol Univ Dept Comp Sci & Engn Dinajpur 5200 Bangladesh Madanapalle Inst Technol & Sci MITS Sch Comp Sci Madanapalle AP India Iwate Prefectural Univ Reg Res Cooperat Ctr Takizawa Iwate 0200693 Japan

Feature subset selection is essential for identifying relevant and non-redundant features, which enhances classification accuracy and simplifies machine learning models. Given the computational difficulties of determining optimal feature subsets, heuristic and metaheuristic algorithms have been widely used. Recently, the rise of quantum computing has led to the exploration of quantum-inspired metaheuristics and quantum-based approaches for this task. Although various studies have explored quantum-inspired and quantum-based approaches for feature subset selection, a comprehensive review that critically examines their significance, limitations, underlying mechanisms, and future directions remains lacking in the literature. This paper addresses this gap by presenting the first in-depth survey of these approaches. We systematically selected and analyzed relevant studies from prominent research databases, providing a detailed evaluation of quantum-inspired metaheuristics and quantum computing paradigms applied to feature subset selection. Our findings indicate that quantum-inspired metaheuristic approaches often deliver superior performance compared to traditional metaheuristic methods for feature subset selection. Nevertheless, their reliance on classical computing limits their ability to fully realize the advantages offered by quantum computing. The quantum-based feature subset selection methods, on the other hand, show considerable promise but are frequently constrained by the current limitations of quantum hardware, making large-scale feature subset selection challenging. Given the rapid evolution of quantum computing, research on both quantum-inspired and quantum-based feature subset selection remains insufficient to draw definitive conclusions. We are optimistic that this review will provide a foundation for future advancements in feature subset selection as quantum computing resources become more accessible.

关键词： quantum computing Feature subset selection quantum-inspired metaheuristic quantum annealing quantum approximate optimization algorithm

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QUBO Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem

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IEEE ACCESS 2025年 13卷 47086-47098页

作者： Guney, Evren Ehrenthal, Joachim Hanne, Thomas MEF Univ Dept Ind Engn TR-34396 Istanbul Turkiye Univ Appl Sci & Arts Northwestern Switzerland FHNW Inst Business Informat Syst Sch Business CH-5210 Windisch Switzerland

The Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: quantum approximate optimization algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.

关键词： optimization quantum computing Logic gates quantum annealing Annealing Qubit Noise Approximation algorithms Tuning quantum state Multi-dimensional knapsack problem multiple knapsack problem quadratic unconstrained binary optimization quantum annealing quantum approximate optimization algorithm penalty parameters

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