论文标题
基于深度强化学习的量子控制
Quantum Control based on Deep Reinforcement Learning
论文作者
论文摘要
在本论文中，我们考虑了两个简单但典型的控制问题，并将深度加固学习应用于它们，即冷却和控制粒子，该粒子可能会在一维二次潜能或四维二维潜力或四分之一的潜力中进行连续的位置测量。我们将强化学习控制和传统控制策略的性能在这两个问题上进行了比较，并表明强化学习实现了与二次案例的最佳控制相当的性能，并且在最佳控制策略的四分之一案例中优于常规控制策略。据我们所知，这是第一次将深入的增强学习应用于连续真实空间中的量子控制问题。我们的研究表明，可以将深入的强化学习用作实际空间中的随机量子系统作为测量反馈闭环控制器，我们的研究还表明，AI能够发现不充分理解的量子系统的新控制策略和属性，并且我们可以通过对AI的学习来了解这些问题，从而对其进行了研究，以了解一项新的科学研究。
In this thesis, we consider two simple but typical control problems and apply deep reinforcement learning to them, i.e., to cool and control a particle which is subject to continuous position measurement in a one-dimensional quadratic potential or in a quartic potential. We compare the performance of reinforcement learning control and conventional control strategies on the two problems, and show that the reinforcement learning achieves a performance comparable to the optimal control for the quadratic case, and outperforms conventional control strategies for the quartic case for which the optimal control strategy is unknown. To our knowledge, this is the first time deep reinforcement learning is applied to quantum control problems in continuous real space. Our research demonstrates that deep reinforcement learning can be used to control a stochastic quantum system in real space effectively as a measurement-feedback closed-loop controller, and our research also shows the ability of AI to discover new control strategies and properties of the quantum systems that are not well understood, and we can gain insights into these problems by learning from the AI, which opens up a new regime for scientific research.
