A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-10-18 DOI:10.1145/3700874
Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao, Soummya Kar, Sridhar Tayur
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Abstract

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.
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运筹学中的量子算法和量子启发算法系统映射研究
量子和量子启发算法在运筹学(OR)的潜在应用中尚未得到系统分类。我们的系统图谱是为快速咨询而设计的,它显示了哪些算法在运筹学背景下得到了大量探索,以及哪些算法在运筹学背景下得到了模糊处理。这项研究为有兴趣在各自领域应用和/或进一步开发这些算法的手术室专业人员(包括从业人员和研究人员)提供了快速通道。我们编写了一份可复制的协议,作为这项系统图谱研究的基础,明确了研究问题,建立了有效的搜索和选择方法,定义了评估质量指标,并指导了对所选研究的分析。共找到 2000 多项研究,对其中 149 项进行了详细分析。读者可以通过网站上的开放源码资源库对收集到的数据进行交互式实践体验。我们在分类过程中采用了国际标准,使世界各地的专业人士和研究人员能够轻松识别哪些算法已应用于任何行业领域。我们的努力还产生了一系列丰富的启示,可以帮助读者确定未来工作的潜在路径。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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