非马尔可夫量子控制：基于模型最大似然估计与强化学习
Non-Markovian Quantum Control via Model Maximum Likelihood Estimation and Reinforcement Learning
AI总结：
针对量子系统闭环控制中的模型偏差问题，提出将非马尔可夫环境纳入低维有效储库，结合强化学习与模型学习，以更高效地学习动力学并设计控制策略，从而抵消退相干。
摘要：
强化学习（Reinforcement Learning, RL）技术已越来越多地应用于优化控制系统。然而，由于量子系统难以测量，其在量子系统中的应用受到闭环控制执行挑战的阻碍。这常常导致依赖假设模型，引入模型偏差，这一问题在马尔可夫近似不成立的开量子动力学中尤为严重。为应对这些挑战，我们提出了一种新颖方法，将环境的非马尔可夫特性纳入低维有效储库中。通过初步采用一系列测量作为“数据集”，我们利用机器学习技术比传统层析方法更高效地学习有效量子动力学。我们的方法旨在证明，通过将强化学习与模型学习相结合，有可能设计出能够抵消自旋-玻色子系统中退相干的控制策略和模型。这种方法不仅可能缓解模型偏差问题，还能提供更精确的量子动力学表示，为更有效的量子控制策略铺平道路。
英文摘要：
Reinforcement Learning (RL) techniques have been increasingly applied in optimizing control systems. However, their application in quantum systems is hampered by the challenge of performing closed-loop control due to the difficulty in measuring these systems. This often leads to reliance on assumed models, introducing model bias, a problem that is exacerbated in open quantum dynamics where Markovian approximations are not valid. To address these challenges, we propose a novel approach that incorporates the non-Markovian nature of the environment into a low-dimensional effective reservoir. By initially employing a series of measurements as a 'dataset', we utilize machine learning techniques to learn the effective quantum dynamics more efficiently than traditional tomographic methods. Our methodology aims to demonstrates that by integrating reinforcement learning with model learning, it is possible to devise control policies and models that can counteract decoherence in a spin-boson system. This approach may not only mitigates the issues of model bias but also provides a more accurate representation of quantum dynamics, paving the way for more effective quantum control strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
