Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits to approximate the policy function. This paper addresses the robustness and generalization of quantum reinforcement learning by combining principles from quantum computing and control theory. Leveraging recent results on robust quantum machine learning, we utilize Lipschitz bounds to propose a regularized version of a quantum policy gradient approach, named the RegQPG algorithm. We show that training with RegQPG improves the robustness and generalization of the resulting policies. Furthermore, we introduce an algorithmic variant that incorporates curriculum learning, which minimizes failures during training. Our findings are validated through numerical experiments, demonstrating the practical benefits of our approach.

翻译：相较于经典方法，量子机器学习利用量子计算在提升精度与降低模型复杂度方面展现出优势，有望推动多个领域的重大进展。在该领域中，量子强化学习受到广泛关注，其通常通过变分量子电路逼近策略函数来实现。本文结合量子计算与控制理论的基本原理，系统研究了量子强化学习的鲁棒性与泛化性问题。基于近期鲁棒量子机器学习的研究成果，我们利用Lipschitz边界提出了一种正则化的量子策略梯度方法，命名为RegQPG算法。研究表明，采用RegQPG进行训练能够有效提升所得策略的鲁棒性与泛化能力。此外，我们进一步提出融合课程学习的算法变体，该设计能显著减少训练过程中的失败案例。通过数值实验验证了本方法的实际优势，证实了其有效性。