Challenges and opportunities in quantum optimization

IF 44.8 1区 物理与天体物理 Q1 PHYSICS, APPLIED Nature Reviews Physics Pub Date : 2024-10-28 DOI:10.1038/s42254-024-00770-9
Amira Abbas, Andris Ambainis, Brandon Augustino, Andreas Bärtschi, Harry Buhrman, Carleton Coffrin, Giorgio Cortiana, Vedran Dunjko, Daniel J. Egger, Bruce G. Elmegreen, Nicola Franco, Filippo Fratini, Bryce Fuller, Julien Gacon, Constantin Gonciulea, Sander Gribling, Swati Gupta, Stuart Hadfield, Raoul Heese, Gerhard Kircher, Thomas Kleinert, Thorsten Koch, Georgios Korpas, Steve Lenk, Jakub Marecek, Vanio Markov, Guglielmo Mazzola, Stefano Mensa, Naeimeh Mohseni, Giacomo Nannicini, Corey O’Meara, Elena Peña Tapia, Sebastian Pokutta, Manuel Proissl, Patrick Rebentrost, Emre Sahin, Benjamin C. B. Symons, Sabine Tornow, Víctor Valls, Stefan Woerner, Mira L. Wolf-Bauwens, Jon Yard, Sheir Yarkoni, Dirk Zechiel, Sergiy Zhuk, Christa Zoufal
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Abstract

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-force classical simulation. Interest in quantum algorithms has developed in many areas, particularly in relation to mathematical optimization — a broad field with links to computer science and physics. In this Review, we aim to give an overview of quantum optimization. Provably exact, provably approximate and heuristic settings are first explained using computational complexity theory, and we highlight where quantum advantage is possible in each context. Then, we outline the core building blocks for quantum optimization algorithms, define prominent problem classes and identify key open questions that should be addressed to advance the field. We underscore the importance of benchmarking by proposing clear metrics alongside suitable optimization problems, for appropriate comparisons with classical optimization techniques, and discuss next steps to accelerate progress towards quantum advantage in optimization. This Review discusses quantum optimization, focusing on the potential of exact, approximate and heuristic methods, core algorithmic building blocks, problem classes and benchmarking metrics. The challenges for quantum optimization are considered, and next steps are suggested for progress towards achieving quantum advantage.

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量子优化的挑战与机遇
量子计算机具有可证明的解决问题的能力,其规模超出了经典模拟的极限。许多领域都对量子算法产生了兴趣,尤其是与数学优化相关的领域,这是一个与计算机科学和物理学相关的广泛领域。在这篇综述中,我们的目的是给量子优化的概述。首先使用计算复杂性理论解释可证明的精确,可证明的近似和启发式设置,并强调在每种情况下量子优势可能存在的地方。然后,我们概述了量子优化算法的核心构建模块,定义了突出的问题类别,并确定了应该解决的关键开放问题,以推进该领域。我们通过提出明确的指标以及合适的优化问题来强调基准测试的重要性,以便与经典优化技术进行适当的比较,并讨论下一步加快优化中量子优势的进展。本综述讨论了量子优化,重点是精确、近似和启发式方法的潜力,核心算法构建块,问题类和基准度量。考虑了量子优化的挑战,并提出了实现量子优势的下一步步骤。
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来源期刊
CiteScore
47.80
自引率
0.50%
发文量
122
期刊介绍: Nature Reviews Physics is an online-only reviews journal, part of the Nature Reviews portfolio of journals. It publishes high-quality technical reference, review, and commentary articles in all areas of fundamental and applied physics. The journal offers a range of content types, including Reviews, Perspectives, Roadmaps, Technical Reviews, Expert Recommendations, Comments, Editorials, Research Highlights, Features, and News & Views, which cover significant advances in the field and topical issues. Nature Reviews Physics is published monthly from January 2019 and does not have external, academic editors. Instead, all editorial decisions are made by a dedicated team of full-time professional editors.
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