用量子算法提升车辆路径优化的智能决策效率
Hybrid Quantum Reinforcement Learning with QAOA for Improved Vehicle Routing Optimization
- 将量子近似优化算法嵌入强化学习网络，替代传统变分层
- 在模拟器上收敛更快，能处理更大规模路径问题
- 适合研究量子机器学习与物流优化交叉领域的学者
车辆路径问题（VRP）是运输与物流中最为复杂的NP难组合优化问题之一，需动态求解。本文提出一种新型混合方法，将量子近似优化算法（QAOA）引入QRL策略网络，取代常规的变分层，采用QAOA的混合与代价哈密顿量层。该设计使智能体在学习策略时能利用问题特异性量子关联，实现更丰富的路径解空间探索。实验表明，该增强框架训练收敛更快，可求解超出格罗弗自适应搜索（GAS）和传统量子强化学习（QRL）能力范围的更大规模VRP实例。在标准VRP实例上的测试显示，该方法获得更优解、更少训练回合数，并保持良好内存效率。结果验证了集成QAOA的QRL在可扩展、高质量量子辅助组合优化中的可行性。
原文摘要 · Abstract (English)
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.
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