Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation - Cheng, Jinglei; Zhou, Ruilin; Gan, Yuhang; Qian, Chen; Liu, Junyu (2025 62nd ACM/IEEE Design Automation Conference (DAC), 2025)
We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in largescale graph data.