Quantum-Powered Deep Learning for Advanced Community Detection in Complex Networks: A Systematic Literature Review

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.