Speaker: Konrad Tywoniuk, University of Bergen
Abstract: Community detection, also known as graph partitioning, is a well-known NP-hard combinatorial optimization problem with applications in diverse fields such as complex network theory, transportation, and smart power grids. The problem's solution space grows drastically with the number of vertices and subgroups, making efficient algorithms crucial. In recent years, quantum computing has emerged as a promising approach to tackling NP-hard problems. This study explores the use of a quantum-inspired algorithm, Simulated Bifurcation (SB), for community detection. Experimental results demonstrate that SB effectively identifies community structures in benchmark networks such as Zachary's Karate Club and the IEEE 33-bus system. SB achieved high modularity, matching the performance of Fujitsu's Digital Annealer, and surpassing results obtained from two quantum machines, D-Wave and IBM. These findings highlight the potential of SB as a powerful tool for solving community detection problems.