Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning

2025-11-19

AI Summary

To address the NP-hard Vehicle Routing Problem (VRP) in intelligent transportation systems, this paper proposes a novel hybrid framework integrating quantum computing with deep reinforcement learning. Methodologically, we introduce parameterized quantum circuits into a Graph Attention Network (termed Q-GAT), replacing conventional multi-layer perceptrons to drastically reduce model parameters (>50%) and alleviate memory bottlenecks. The framework is trained and inferred end-to-end using the Proximal Policy Optimization (PPO) algorithm coupled with greedy and stochastic decoding strategies. On standard VRP benchmarks, Q-GAT achieves approximately 5% lower route costs than classical GAT while demonstrating significantly faster convergence. Experimental results confirm that the proposed approach delivers superior efficiency, lightweight architecture, and enhanced representational capacity—making it particularly suitable for large-scale logistics optimization.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

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.

Problem

• Solving NP-hard vehicle routing problems in intelligent transportation systems
• Reducing parameter-heavy classical models using quantum graph attention networks
• Improving routing optimization convergence speed and cost efficiency

Innovation

• Quantum Graph Attention Network replaces classical MLPs
• Parameterized quantum circuits reduce trainable parameters by 50%
• Hybrid quantum-classical model achieves faster convergence and lower routing costs