Rising quantum technologies undertake to provide new strategies for solving hard combinatorial problems. Community detection is a fundamental issue in network studies with applications in various complex systems. A review of methods proposed in the last years in the context of quantum optimization and quantum-inspired metaheuristics to detect communities in networks is presented. Quantum optimization is introduced by recalling the main available computation models and the most popular hybridization approaches. The advantages and limitations of methods based on quantum computing and evolutionary computing are discussed.
Rising quantum technologies undertake to provide new strategies for solving hard combinatorial problems. Community detection is a fundamental issue in network studies with applications in various complex systems. A review of methods proposed in the last years in the context of quantum optimization and quantum-inspired metaheuristics to detect communities in networks is presented. Quantum optimization is introduced by recalling the main available computation models and the most popular hybridization approaches. The advantages and limitations of methods based on quantum computing and evolutionary computing are discussed.
The dynamics and underlying structure of complex social networks (SNs) can be discovered using community detection. Considering how quantum computing might improve community detection techniques is becoming more and more popular in light of recent developments in computing technology. In this article, the rapidly developing topic of quantum-SN analysis for community discovery is thoroughly reviewed. Community detection is studied in the context of several quantum-inspired techniques, including quantum annealing and quantum-inspired optimization. To make use of the strengths of both conventional SN research methods and quantum computing techniques, hybrid quantum-classical approaches are being investigated. Case studies and applications that have made use of quantum-SN analysis methodologies are reviewed to highlight the practical consequences and potential advantages over conventional methods. The article also highlights the challenges and limitations of using quantum computing for SN analysis, including technical constraints and ethical issues. Finally, prospects and future research objectives in the area of quantum-SN analysis are highlighted. This covers possible developments in quantum algorithms for community detection, the incorporation of quantum computing with other SN research tasks, and the significance of multidisciplinary collaborations.
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.
Community detection in complex networks remains a fundamental challenge in network science, with applications across social and biological systems. Various approaches, ranging from optimization techniques to deep learning, have been explored to address this issue. More recently, the emergence of quantum methods has further enhanced computational efficiency, offering new possibilities for improving these existing techniques. In this context, numerous reviews have explored various community detection approaches. However, while some studies have focused individually on deep learning-based methods and quantum-powered techniques, their combined potential remains unexplored. This paper conducts a systematic literature review (SLR) on Quantum-Powered Deep Learning for advanced community detection in complex networks. Adopting a structured methodology, it defines key research questions, selects relevant studies from academic databases, and applies rigorous inclusion/exclusion criteria. The review provides a comprehensive analysis of deep learning-based community detection and quantum computing techniques, evaluating their effectiveness in optimizing community assignments. Selected studies are compared in terms of performance, computational efficiency, and scalability, revealing key advancements and research gaps. Finally, the paper discusses emerging challenges and opportunities, guiding researchers in leveraging the synergy between deep learning and quantum computing for more effective community detection within complex networks.
The chapter expands upon the concepts of using hybrid QIEAs in enhancing the existing community detection networks. Thus, as communities are becoming more digital the untold need for sophisticated algorithms that can handle and coordinate interactions is becoming critical. In this chapter, the reader will be introduced to the general concepts of quantum computing and an overview of the technique of evolutionary algorithms, which will allow the subsequent detailed elaboration of the interconnection between the two fields. In this chapter, we first introduce the idea of the development and usage of the hybrid QIEAs, focusing on the theoretical methods to integrate the classical and quantum information for solving problems that cannot be approached by the traditional EA.
Abstract Emerging quantum technologies represent a promising alternative for solving hard combinatorial problems in the post-Moore’s law era. For practical purposes, however, the current number of qubits limits the direct applicability to larger real-world instances in the near-term future. Therefore, a promising strategy to overcome this issue is represented by hybrid quantum classical algorithms which leverage classical as well as quantum devices. One prominent example of a hard computational problem is the community detection problem: a partition of a graph into distinct communities such that the ratio between intra-community and inter-community connectivity is maximized. In this paper, we explore the current potential of quantum annealing and gate-based quantum technologies to solve the community detection problem for an arbitrary number of communities. For this purpose, existing algorithms are (re-)implemented and new hybrid algorithms, that can be run on gate-model devices, are proposed. Their performance on standardized benchmark graphs has been evaluated and compared to the one of a state-of-the-art classical heuristic algorithm. Although no quantum speed-up has been achieved, the existing quantum annealing-based methods as well as the novel hybrid algorithms for gate-based quantum computers yield modularity values, which are similar to those of the classical heuristic. However, the modular architecture of the used algorithms allows for fast utilization of more powerful quantum technologies once they become available.Reproducibility:Our code and data are publicly available (Github in Quantum Modularization. https://github.com/jku-win se/quantum_modularization 2021).
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.