Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
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 beyo...
| Authors | T. Satyanarayana Murthy, B. Swathi Sowmya, Santhosh Voruganti et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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