Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Springer eBooks

Enhancing Metaheuristics: The Role of Quantum-Inspired Soft Computing

Puja Das, Chitra Jain, Ansul, Kamal Kumar Gola, Naresh Kumar, Moutushi Singh

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1007/978-3-031-87931-9_8

Related papers

A review of recent advances in quantum-inspired metaheuristics

Shahin Hakemi, Mahboobeh Houshmand, Esmaeil Kheirkhah +1 more

Evolutionary Intelligence•Oct 23, 2022•70 citations

Quantum-inspired metaheuristic algorithms: comprehensive survey and classification

Farhad Soleimanian Gharehchopogh

Artificial Intelligence Review•Nov 2, 2022•257 citations

Quantum-inspired metaheuristics for drug design and discovery

A. Saranya, Rajiv Iyer, Vedprakash Chandrakant Maralapalle

Elsevier eBooks•Jan 1, 2026•1 citation

Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis

Pooja, Sandeep K. Sood

Engineering Applications of Artificial Intelligence•Nov 20, 2024•11 citations

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

Junaid ur Rehman, Muhammad Shohibul Ulum, Abdurrahman Wachid Shaffar +6 more

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.

IEEE Access•Jan 1, 2025•30 citations

A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers

Oscar Montiel

This paper presents a review of quantum-inspired population-based metaheuristics. Quantum-inspired algorithms were born when there were no quantum computers; they demonstrated to have interesting characteristics providing good results in classical computers. At present, when the first quantum computers are available, scientists are working to confirm the quantum supremacy in different fields. After almost 20 years that the first metaheuristic inspired in quantum phenomena was published, a large number of works have been proposed. This paper aims to look back to see which quantum-inspired metaheuristics could be translated to be used in the existing quantum computers based on the circuit model programming paradigm. Reviewed metaheuristics were classified according to their main source of inspiration; just some representative works of each classification were selected because of the vast number of existing works on each one. The analysis was done for the circuit model and metrics as width, size, and length were used to determine their viability of being implemented in a real quantum computer. Moreover, comparative results using metrics such as performance and running time for quantum-inspired metaheuristic were included.

IEEE Access•Dec 25, 2019•120 citations
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.
This paper presents a review of quantum-inspired population-based metaheuristics. Quantum-inspired algorithms were born when there were no quantum computers; they demonstrated to have interesting characteristics providing good results in classical computers. At present, when the first quantum computers are available, scientists are working to confirm the quantum supremacy in different fields. After almost 20 years that the first metaheuristic inspired in quantum phenomena was published, a large number of works have been proposed. This paper aims to look back to see which quantum-inspired metaheuristics could be translated to be used in the existing quantum computers based on the circuit model programming paradigm. Reviewed metaheuristics were classified according to their main source of inspiration; just some representative works of each classification were selected because of the vast number of existing works on each one. The analysis was done for the circuit model and metrics as width, size, and length were used to determine their viability of being implemented in a real quantum computer. Moreover, comparative results using metrics such as performance and running time for quantum-inspired metaheuristic were included.