Reinschmidt Malte, Fortágh József, Günther Andreas, Volchkov Valentin V
Center for Quantum Science, Physikalisches Institut, Eberhard Karls Universität Tübingen, Tübingen, Germany.
Max Planck Institute for Intelligent Systems, Tübingen, Germany.
Nat Commun. 2024 Oct 2;15(1):8532. doi: 10.1038/s41467-024-52775-8.
Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In this work, we introduce reinforcement learning to cold atom experiments and demonstrate a flexible and adaptive approach to control a magneto-optical trap. Instead of following a set of predetermined rules to accomplish a specific task, the objectives are defined by a reward function. This approach not only optimizes the cooling of atoms just as an experimentalist would do, but also enables new operational modes such as the preparation of pre-defined numbers of atoms in a cloud. The machine control is trained to be robust against external perturbations and able to react to situations not seen during the training. Finally, we show that the time consuming training can be performed in-silico using a generic simulation and demonstrate successful transfer to the real world experiment.
冷原子阱是许多科技领域量子应用的核心。原子云的制备和控制涉及复杂的优化过程，而机器学习可以为这些过程提供支持并加速。在这项工作中，我们将强化学习引入冷原子实验，并展示了一种灵活且自适应的方法来控制磁光阱。不是遵循一组预定规则来完成特定任务，而是通过奖励函数来定义目标。这种方法不仅能像实验人员那样优化原子冷却，还能实现新的操作模式，比如在原子云中制备预定义数量的原子。机器控制经过训练，能够抵御外部干扰，并对训练期间未遇到的情况做出反应。最后，我们表明耗时的训练可以使用通用模拟在计算机上进行，并证明成功应用于实际实验。
