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

作者：Junaid ur Rehman, Muhammad Shohibul Ulum, Abdurrahman Wachid Shaffar, Amirul Adlil Hakim, Mujirin, Zaid Abdullah, Hayder Al-Hraishawi, Symeon Chatzinotas, Hyundong Shin · 发表于：IEEE Access · 年份：2025 · DOI：10.1109/access.2025.3530952 · 被引用次数：21 · 研究领域：Evolutionary Algorithms and Applications、Quantum Computing Algorithms and Architecture、Metaheuristic Optimization Algorithms Research

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 tw...