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arXiv · 2411.14696

Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation

Jinglei Cheng·Ruilin Zhou·Yuhang Gan·Chen Qian·Junyu Liu
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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.

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Jinglei Cheng, Ruilin Zhou, Yuhang Gan, Chen Qian, Junyu Liu. 2024-11-22. Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation. https://doi.org/10.1109/dac63849.2025.11133263

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