Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges
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 ai...
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| Format: | Article |
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844268/ |
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| author | Junaid Ur Rehman Muhammad Shohibul Ulum Abdurrahman Wachid Shaffar Amirul Adlil Hakim Mujirin Zaid Abdullah Hayder Al-Hraishawi Symeon Chatzinotas Hyundong Shin |
| author_facet | Junaid Ur Rehman Muhammad Shohibul Ulum Abdurrahman Wachid Shaffar Amirul Adlil Hakim Mujirin Zaid Abdullah Hayder Al-Hraishawi Symeon Chatzinotas Hyundong Shin |
| author_sort | Junaid Ur Rehman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fae356a52c054ce68a321c27e818282c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fae356a52c054ce68a321c27e818282c2025-01-31T00:01:02ZengIEEEIEEE Access2169-35362025-01-0113166491667010.1109/ACCESS.2025.353095210844268Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and ChallengesJunaid Ur Rehman0https://orcid.org/0000-0002-2933-8609Muhammad Shohibul Ulum1https://orcid.org/0000-0003-0769-1599Abdurrahman Wachid Shaffar2https://orcid.org/0000-0003-0765-1618Amirul Adlil Hakim3https://orcid.org/0009-0008-3063-6911 Mujirin4https://orcid.org/0009-0005-7358-7417Zaid Abdullah5https://orcid.org/0000-0002-1859-0729Hayder Al-Hraishawi6https://orcid.org/0000-0002-0977-9984Symeon Chatzinotas7https://orcid.org/0000-0001-5122-0001Hyundong Shin8https://orcid.org/0000-0003-3364-8084Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, South KoreaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, South KoreaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, South KoreaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, South KoreaInterdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg City, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg City, LuxembourgInterdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg City, LuxembourgDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeonggi-do, South KoreaQuantum 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.https://ieeexplore.ieee.org/document/10844268/Evolutionary algorithmsgenetic algorithmquantum-inspired algorithmsquantum computing |
| spellingShingle | Junaid Ur Rehman Muhammad Shohibul Ulum Abdurrahman Wachid Shaffar Amirul Adlil Hakim Mujirin Zaid Abdullah Hayder Al-Hraishawi Symeon Chatzinotas Hyundong Shin Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges IEEE Access Evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing |
| title | Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges |
| title_full | Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges |
| title_fullStr | Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges |
| title_full_unstemmed | Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges |
| title_short | Evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges |
| title_sort | evolutionary algorithms and quantum computing recent advances opportunities and challenges |
| topic | Evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing |
| url | https://ieeexplore.ieee.org/document/10844268/ |
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