用量子行走模拟图结构，提升链接预测准确率。
QSearchNet: A Quantum Walk Search Framework for Link Prediction
- 基于离散时间量子行走与格罗弗放大的量子启发框架。
- 在真实网络上对复杂负样本表现优异，优于传统方法。
- 适合研究图神经网络与量子计算交叉应用的学者。
链接预测是图论中的基础问题，对理解与预测社交、生物等复杂系统演化至关重要。经典启发式方法虽能捕捉部分图拓扑特征，却难以最优融合局部与全局结构信息或适应复杂依赖关系。量子计算通过叠加态实现多路径并行探索，并利用干涉机制整合局部与全局图特征，提供新思路。本文提出QSearchNet，一种基于离散时间量子行走（DTQW）与格罗弗幅值放大原理的量子启发框架。该模型模拟拓扑感知的量子演化，同时在多个节点间传播振幅。通过量子反射与类预言机相位翻转操作对齐干涉模式，自适应优先处理多跳依赖关系，并增强对应潜在连接的结构相关路径。在多种真实世界网络上的实验表明，该方法在存在困难负样本的真实评估条件下表现竞争性。
原文摘要 · Abstract (English)
Link prediction is one of the fundamental problems in graph theory, critical for understanding and forecasting the evolution of complex systems like social and biological networks. While classical heuristics capture certain aspects of graph topology, they often struggle to optimally integrate local and global structural information or adapt to complex dependencies. Quantum computing offers a powerful alternative by leveraging superposition for simultaneous multi-path exploration and interference-driven integration of both local and global graph features. In this work, we introduce QSearchNet, a quantum-inspired framework based on Discrete-Time Quantum Walk (DTQW) dynamics and Grover's amplitude amplification. QSearchNet simulates a topology-aware quantum evolution to propagate amplitudes across multiple nodes simultaneously. By aligning interference patterns through quantum reflection and oracle-like phase-flip operation, it adaptively prioritizes multi-hop dependencies and amplifies structurally relevant paths corresponding to potential connections. Experiments on diverse real-world networks demonstrate competitive performance, particularly with hard negative samples under realistic evaluation conditions.
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