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.

翻译：车辆路径问题（VRP）是智能交通系统中的基础NP-hard任务，在物流与配送领域具有广泛应用。基于图神经网络（GNN）的深度强化学习（DRL）方法已展现出潜力，但经典模型依赖参数庞大且受内存限制的多层感知机（MLP）。我们提出一种在DRL框架内嵌入量子图注意力网络（Q-GAT）的方法，通过参数化量子电路（PQC）替代关键读出阶段的传统MLP。该混合模型在保持图注意力编码器表达能力的同时，将可训练参数减少50%以上。基于近端策略优化（PPO）配合贪婪与随机解码策略，在VRP基准测试中的实验表明，相较于经典GAT基线，Q-GAT实现了更快的收敛速度，并将路径成本降低约5%。这些结果证明了PQC增强的GNN作为紧凑高效求解器在大规模路径规划与物流优化中的潜力。