We propose a scheme leveraging reinforcement learning to engineer control fields for generating non-classical states. It is exemplified by the application to prepare spin-squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement this control proposal based on the comparison with the others. The extensions to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.

翻译：我们提出一种利用强化学习设计控制场以制备非经典量子态的方案。该方案以开放集体自旋模型中制备自旋压缩态为例，通过设计线性控制场来调控系统动力学。强化学习智能体在具有耗散和退相干特性的环境中，从相干自旋态出发，确定控制脉冲的时间序列。与恒定控制方案相比，该方法能产生多种保持集体自旋压缩和纠缠特性的控制序列。研究发现，更密集地施加控制脉冲可提升输出性能，但增加控制操作仅能带来微小的性能改善。所提策略在更大规模系统中表现出更高的有效性。热库的热激发会对控制效果产生不利影响。通过与其他方案的比较，提出了实施该控制方案的可实验方案。本文进一步讨论了向连续控制问题及其他量子系统的扩展，并强调了强化学习模块的可替换性。该研究为操纵其他量子系统开辟了新途径。