Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization

TL;DR

This paper introduces a hybrid quantum reinforcement learning method that integrates QAOA into the policy network to improve vehicle routing optimization, achieving faster convergence and better solutions.

Contribution

It presents a novel hybrid approach combining QAOA with QRL, enabling richer exploration and scalability in solving complex VRPs.

Findings

Quicker convergence in training compared to previous methods.

Ability to handle larger VRP instances beyond existing quantum approaches.

Achieves better solutions with fewer episodes and efficient memory usage.

Abstract

Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dynamic solution approach. In this paper we present a new hybrid approach that combines the Quantum Approximate Optimization Algorithm (QAOA) into the QRL policy network, instead of the usual variational layers, QAOA mixing and cost Hamiltonian layers. This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space. The QAOA-augmented QRL framework shows quicker convergence in training and can tackle larger VRP instances that are beyond the reach of Grover's Adaptive Search (GAS) and Quantum Reinforcement Learning (QRL) approaches. Experiments on standard VRP instances demonstrate better solutions, fewer episodes to converge and good memory usage on near term quantum hardware simulators. These findings demonstrate QAOA- integrated QRL as a viable approach to scalable, high quality quantum-assisted combinatorial optimization.