用量子启发算法提升大规模图社区发现效率与精度。
Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
- 将社区发现转为QUBO问题，用量子哈密顿下降优化
- 相比经典方法，模体分数最高提升5.49%，耗时更少
- 适合处理大规模图数据的科研与工业场景
我们提出一种量子启发算法，利用量子哈密顿下降（QHD）实现高效的社区检测。将社区检测任务重构为无约束二次二值优化（QUBO）问题，并通过QHD求解最优社区结构。设计多级算法，通过交替执行QUBO建模与基于QHD的优化，迭代细化社区划分。基准测试表明，该方法在模体分数上相较经典优化方法最高提升5.49%，同时计算时间更短。本工作展示了混合量子启发方案在大规模图数据分析中推动社区检测的潜力。
原文摘要 · Abstract (English)
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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