Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
Abstract
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 large-scale graph data.
Cite
@article{arxiv.2411.14696,
title = {Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation},
author = {Jinglei Cheng and Ruilin Zhou and Yuhang Gan and Chen Qian and Junyu Liu},
journal= {arXiv preprint arXiv:2411.14696},
year = {2025}
}
Comments
DAC 2025