Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| author | Giang, Le Tung Viet, Vu Hoang Tung, Nguyen Xuan Van Chien, Trinh Hwang, Won-Joo |
| author_facet | Giang, Le Tung Viet, Vu Hoang Tung, Nguyen Xuan Van Chien, Trinh Hwang, Won-Joo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning Giang, Le Tung Viet, Vu Hoang Tung, Nguyen Xuan Van Chien, Trinh Hwang, Won-Joo Machine Learning Information Theory Quantum Physics 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. |
| title | Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning |
| topic | Machine Learning Information Theory Quantum Physics |
| url | https://arxiv.org/abs/2511.15175 |