用量子电路替代传统模型，让车辆路径问题求解更快更省
Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning
- 用参数化量子电路替换图注意力网络的冗余全连接层
- 参数量减少超50%，收敛速度更快，路径成本降低约5%
- 适合追求高效低耗的物流优化与智能交通系统研究者
车辆路径问题（VRP）是智能交通系统中的基础性NP-hard任务，广泛应用于物流与配送领域。基于图神经网络（GNN）的深度强化学习（DRL）展现出潜力，但传统模型依赖大量多层感知机（MLPs），参数量大且内存占用高。本文在DRL框架中提出量子图注意力网络（Q-GAT），用参数化量子电路（PQC）替代关键读出阶段的常规MLPs。该混合模型在保持图注意力编码器表达能力的同时，可减少超过50%的可训练参数。采用近端策略优化（PPO）结合贪婪与随机解码，在VRP基准测试上，Q-GAT实现更快收敛，并使路径成本降低约5%。结果表明，增强型量子电路的GNN可作为大规模路径与物流优化的紧凑高效求解器。
原文摘要 · Abstract (English)
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet classical models rely on large multi-layer perceptrons (MLPs) that are parameter-heavy and memory-bound. We propose a Quantum Graph Attention Network (Q-GAT) within a DRL framework, where parameterized quantum circuits (PQCs) replace conventional MLPs at critical readout stages. The hybrid model maintains the expressive capacity of graph attention encoders while reducing trainable parameters by more than 50%. Using proximal policy optimization (PPO) with greedy and stochastic decoding, experiments on VRP benchmarks show that Q-GAT achieves faster convergence and reduces routing cost by about 5% compared with classical GAT baselines. These results demonstrate the potential of PQC-enhanced GNNs as compact and effective solvers for large-scale routing and logistics optimization.
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