Hybrid Quantum Inspired Meta-heuristics can be described as an integrative algorithmic structure that are designed by exploiting the basics of Quantum Computing (QC) and meta-heuristics. This paper presents an outline of different categories of hybrid quantum inspired meta-heuristics. A brief summary of entanglement induced optimization is explored in this chapter. Over and above, this paper also provides a concise review ofW-state encoding based optimization algorithms. Thereafter, an overview of quantum system based optimization is studied in this chapter. Moreover, this chapter throws light on a variety of application areas of this field. Finally, the chapter concludes by enlightening certain things related to the theme of the paper.
Hybrid Quantum Inspired Meta-heuristics can be described as an integrative algorithmic structure that are designed by exploiting the basics of Quantum Computing (QC) and meta-heuristics. This paper presents an outline of different categories of hybrid quantum inspired meta-heuristics. A brief summary of entanglement induced optimization is explored in this chapter. Over and above, this paper also provides a concise review ofW-state encoding based optimization algorithms. Thereafter, an overview of quantum system based optimization is studied in this chapter. Moreover, this chapter throws light on a variety of application areas of this field. Finally, the chapter concludes by enlightening certain things related to the theme of the paper.
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 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.
This review provides a comprehensive introduction to the current state of optimization through quantum computing, as well as its main methods and highlights. It begins with an overview of the history and principles of quantum computing, followed by a concise explanation of classical optimization techniques. References to key mathematical tools essential for understanding quantum optimization are introduced to assist readers new to the field. The review then explores various quantum optimization techniques, categorized by hardware requirements, pipelining strategies, kernel methods, order, continuity, heuristic and metaheuristic approaches. Graphs and charts are included to illustrate the interconnections between these techniques, enhancing understanding of their complementarity. Finally, the review concludes with some considerations about societal, economic and environmental impacts and a detailed table summarizing the different methods and their applications.
By violating the extended Church-Turing thesis, the quantum computing (QC) paradigm has emerged as a serious contender to classical computing. We now know that certain classical exponential problems (NP (nondeterministic polynomial time)-complete) can be solved in polynomial time by QC. Shor’s integer factorization and Grover’s method for unstructured search are perhaps the most well-known quantum algorithms. QC has been successfully applied to a variety of domains including optimization problems. Optimization can be solved by using deterministic/numerical as well as stochastic methods. Genetic algorithms (GA) belong to the latter category. Incorporating quantum mechanical properties such as superposition, entanglement, interference into the structure of GA has resulted in a more efficient evolutionary computing method called the quantum-inspired genetic algorithm. This method has been successfully applied to image processing, signal processing, economic load dispatch, coalition formation, task scheduling, etc. This chapter presents an overview of the current trends and developments in the domain of quantum-inspired genetic algorithms with the aim of investigating areas of further research in this sphere.
Quantum-Inspired Evolutionary Algorithms (QIEAs) represent a novel fusion of quantum computing principles and classical evolutionary strategies, offering a powerful approach to solving complex optimization problems. This chapter provides a comprehensive overview, detailing foundational concepts, implementation techniques, and potential applications of QIEAs. By leveraging quantum phenomena such as superposition and entanglement, QIEAs enhance the exploitation and exploration capabilities of traditional evolutionary algorithms (EAs), thus enabling an efficient search of vast solution spaces. The study examines key components of QIEA, including quantum encoding, rotation gates, and entanglement operations, and compares its performance with classical EAs. By analyzing real-life examples of combinatorial optimization problems (COPs) such as the minimum spanning tree (MST), traveling salesman problem (TSP), and graph coloring, the chapter demonstrates QIEA’s superior efficiency and solution quality. Despite implementation challenges, QIEA shows promise in various domains, including machine learning, finance, healthcare, telecommunications, and e-commerce. Future research directions are outlined, emphasizing the need for simplified quantum-inspired operators, hybrid quantum-classical systems, and interdisciplinary collaboration. This chapter highlights the transformative potential of QIEA, setting the stage for further advancements and applications in optimization and computational efficiency.