# Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2511.15175 · HTML: https://mlsanity.com/paper/2511.15175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Solve routing problems with a residual edge-graph attention neural network](https://mlsanity.com/paper/2105.02730/) (2021)
- [What is Quantum Parallelism, Anyhow?](https://mlsanity.com/paper/2405.07222/) (2024)
- [Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2507.06585/) (2025)
- [Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems](https://mlsanity.com/paper/2507.09425/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization](https://mlsanity.com/paper/2610.11759/) (2026)

## Abstract (arXiv)

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.