Metaheuristics are widely perceived optimization methods that provide optimal solutions to an expansive range of computational problems. This paper provides a survey of the quantum-inspired metaheuristics, which is a successful alternative to the classical approach for solving optimization problems and combines the principles of quantum computing and metaheuristic. The idea of employing the concept of quantum mechanics in classical computers for better working of the metaheuristic methods has been a flourishing area of research since the last few decades. This paper aims to provide details of current state-of-the-art quantum-inspired metaheuristics by explaining their working principles and the applications of it in order to do further research in this field.
Metaheuristics are widely perceived optimization methods that provide optimal solutions to an expansive range of computational problems. This paper provides a survey of the quantum-inspired metaheuristics, which is a successful alternative to the classical approach for solving optimization problems and combines the principles of quantum computing and metaheuristic. The idea of employing the concept of quantum mechanics in classical computers for better working of the metaheuristic methods has been a flourishing area of research since the last few decades. This paper aims to provide details of current state-of-the-art quantum-inspired metaheuristics by explaining their working principles and the applications of it in order to do further research in this field.
This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.
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.
The quantum meta heuristic algorithm is developed by applying quantum computing to the meta-heuristic algorithm.This kind of algorithm is good at solving combinatorial and numerical optimization problems,and has the characteristics of acce-lerated convergence,enhanced exploration and development capabilities,and can obtain higher performance results than traditional meta-heuristic algorithms.This paper mainly summarizes and reviews the theoretical methods and applications of quantum meta-heuristic algorithms.Firstly,this paper expounds the basic concepts and principles of quantum computing,and analyzes the challenging problems that need to be solved urgently in the field of quantum computing.Then,this paper expounds the basic principles of six classical quantum meta-heuristic algorithms,analyzes the latest research progress,gives their advantages and disadvantages in solving domain-specific problems,and demonstrates the application of quantum meta-heuristic algorithms in different disciplines and engineering scenarios.Finally,this paper analyzes and explores the existing problems in the theories and methods of quantum meta-heuristic algorithms,and summarizes the future development direction of the theory and application of quantum meta-heuristic algorithms.
Quantum-inspired metaheuristic approaches, often called QIMAs, basically try to mix quantum computing ideas with the usual metaheuristic search routines. In practice, they make it easier to go through tangled search spaces, and they also help maintain a broader population variety, thanks to probabilistic qubit style encodings. Because of that, these methods have drawn a lot of attention across multiple academic directions, like materials science and engineering optimization, plus areas such as drug development and cryptography. QIMAs deliver a decent alternative by stitching Quantum-inspired mechanisms into algorithms that can run on ordinary classical machines, even if actually building full universal quantum computers is still hard, because quantum states are fragile and need heavy error correction, also the hardware is just plain complicated. Theoretical underpinnings, algorithmic progress and real-world applications of QIMAs are all looked at in depth in this article, sort of in a grounded way. The literature covering primarily 2015 to 2025 is reviewed carefully, then organized by the kind of inspiration it uses, like evolutionary algorithms, swarm intelligence approaches, and also genetic algorithms that are inspired by quantum mechanics in a more direct sense. The studies we examine show quite clearly how QIMAs can be used effectively for things like structural design, image processing, flight control, feature selection, and predictive modeling. The intention behind this review is to offer scholars and practitioners a complete, operationally efficient resource on Quantum-inspired optimization methods, for tackling tricky optimization problems.
This paper explores the development and application of novel optimization algorithms inspired by the principles of quantum mechanics. Recognizing the potential of quantum phenomena like superposition and tunneling for enhancing computational efficiency, we propose a new algorithmic framework that mimics these behaviors within a classical computing environment. The core idea is to construct an optimization algorithm that incorporates probabilistic search strategies, analogous to quantum tunneling, and explores multiple solution possibilities concurrently, similar to superposition. This approach aims to overcome the limitations of traditional optimization methods, particularly in tackling complex, high-dimensional problems. We detail the algorithm's design, emphasizing the key mathematical formulations and probabilistic elements. The proposed algorithm's performance is evaluated through theoretical analysis and conceptual demonstrations, showcasing its potential to offer improved convergence rates and solution quality compared to established classical optimization techniques. The research contributes to the burgeoning field of quantum-inspired computing, providing a practical and accessible approach for leveraging quantum concepts for optimization challenges.
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.