量子与经典强化学习融合，提升自动驾驶路径规划效率与适应性。
Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems
- 结合量子并行性生成鲁棒Q表和转向成本估计，融合经典RL流程。
- 训练时间显著缩短，动态障碍场景下路径效率提升30%以上。
- 已在真实地图数据（如IIT德里校园）验证，适合复杂环境实时导航。
本文提出一种新型量子-经典混合框架，将量子计算的固有并行性与经典强化学习相结合。该方法利用量子计算生成稳健的Q表和专用转向成本估算，并将其集成到经典强化学习流程中。量子与经典融合使训练快速收敛，显著减少训练时间，并在静态、动态及移动障碍物场景中提升适应性。基于模拟器的评估显示，路径效率、轨迹平滑度和任务成功率均有显著提升，证实了该框架在复杂且不可预测环境中实现实时自主导航的潜力。此外，该框架在真实世界地图数据（如印度理工学院德里校区）上进行了实际测试，进一步验证其在真实场景中的可行性。
原文摘要 · Abstract (English)
In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.
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