Towards quantum computing based community detection

Wei Li, Yi-Lun Du, Nan Su +3 more

Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema +1 more

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.

Sebastian Feld

Samya Muhuri, Shashank Sheshar Singh

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.

Waqar Hassan, Mujahid Tabassum, Mohammad Asif Nawaz

khawla khalfi, Wala Rebhi, Nesrine Ben Yahia

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.