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.

翻译：本文提出一种量子启发式算法，利用量子哈密顿下降（QHD）实现高效的社区检测。该方法将社区检测任务重新表述为二次无约束二元优化（QUBO）问题，并部署QHD来识别最优社区结构。我们实现了一种多层级算法，通过在QUBO问题构建与基于QHD的优化之间交替迭代，逐步优化社区分配。基准测试表明，相较于经典优化方法，本方法在减少计算时间的同时，模块度得分最高可提升5.49%。这项工作展示了混合量子启发式解决方案在推进大规模图数据社区检测方面的潜力。