Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.
Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.
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 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.
Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area. Henceforth, we review some basic concepts in quantum computation and GAs and emphasize their inherent parallelism. Next, we review the application of GAs for learning quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.
This chapter offers a clear and accessible introduction to the field of quantum-inspired neural computing. This area combines ideas from quantum mechanics with traditional neural network models to create new possibilities in machine learning and optimization metaheuristic algorithms. Unlike actual quantum computing, quantum-inspired approaches work on classical computers but borrow key concepts like superposition, entanglement, and probabilistic behaviour to improve how neural networks learn and process information. The study begins by explaining why researchers are interested in using quantum principles in neural networks. It then introduces the basic mathematical ideas behind these concepts, such as quantum states and transformations, and shows how they can be used to design more powerful and flexible learning systems. It focuses on discussing current models, outlining the distinctions between quantum-inspired and true quantum systems, and evaluating the potential benefits and challenges of this approach. In addition, Quantum-inspired neural computing introduces a novel framework that bridges classical artificial intelligence with principles derived from quantum mechanics. By simulating quantum behaviours on classical hardware, it opens new avenues for developing more adaptive and efficient learning models. This emerging approach offers promising advantages in areas such as optimization, parallel processing, and high-dimensional data representation. Finally, despite its potential, the field faces notable limitations and challenges. These include the lack of limited theoretical formalization, and difficulties in scaling models for complex real-world applications. Moreover, distinguishing genuine quantum-inspired advantages from conservative algorithmic improvements remains an current research apprehension.
Abstract Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range of applications. Nature-inspired stochastic optimization algorithms have long been a research hotspot. The fusion of quantum mechanics with optimization methods can potentially address NP-hard problems more efficiently and exponentially faster. The potential advantages provided by the ground-breaking paradigm have expedited the scientific output of quantum-inspired optimization algorithms locale. Consequently, a pertinent investigation is required to explain how ground-breaking scientific advancements have evolved. The scientometric approach utilizes quantitative and qualitative techniques to analyze research publications to evaluate the structure of scientific knowledge. Henceforth, the current research presents a scientometric and systematic analysis of quantum-inspired metaheuristic algorithms (QiMs) literature from the Scopus database since its inception. The scientometric implications of the article offer a detailed exploration of the publication patterns, keyword co-occurrence network analysis, author co-citation analysis and country collaboration analysis corresponding to each opted category of QiMs. The analysis reveals that QiMs solely account to 26.66% of publication share in quantum computing and have experienced an impressive 42.59% growth rate in the past decade. Notably, power management, adiabatic quantum computation, and vehicle routing are prominent emerging application areas. An extensive systematic literature analysis identifies key insights and research gaps in the QiMs knowledge domain. Overall, the findings of the current article provide scientific cues to researchers and the academic fraternity for identifying the intellectual landscape and latest research trends of QiMs, thereby fostering innovation and informed decision-making.
Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.