Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges

作     者：Ur Rehman, Junaid Ulum, Muhammad Shohibul Shaffar, Abdurrahman Wachid Hakim, Amirul Adlil Mujirin, Zaid Abdullah, Zaid Al-Hraishawi, Hayder Chatzinotas, Symeon Shin, Hyundong 

作者机构：King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg City Luxembourg King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia Kyung Hee Univ Dept Elect & Informat Convergence Engn Yongin 17104 Gyeonggi Do South Korea Univ S Florida Dept Elect Engn Tampa FL 33620 USA 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：16649-16670页

核心收录：

基　　金：National Research Foundation of Korea (NRF) - Korean Government (MSIT) [NRF-2022R1A4A3033401] Ministry of Science and ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) Support Program Institute for Information & Communications Technology Planning & Evaluation (IITP) [IITP-2024-2021-0-02046] 

主　　题：Quantum computing Evolutionary computation Qubit Quantum mechanics Metaheuristics Heuristic algorithms Hardware Terminology Computers Recommender systems Evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing 

摘      要：Quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.