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 performanceof 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 the control proposal. The extension 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：我们提出了一种借助强化学习设计控制场来生成非经典态的方案，并通过将其应用于开放集体自旋模型中自旋压缩态的制备加以说明。在该模型中，线性控制场被用于调控系统动力学。强化学习智能体在存在耗散和退相位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，该方法提供了能够维持集体自旋压缩和纠缠的多种控制序列。研究发现，更密集地施加控制脉冲可提升结果性能，但增加控制动作所带来的性能提升较小。所提出的策略对于更大规模的系统表现出更高的有效性。储库的热激发对控制结果具有不利影响。文中提出了可实现该控制方案的实验建议，并讨论了将其扩展至连续控制问题及其他量子系统的可能性，同时强调了强化学习模块的可替换性。这项研究为将该方法应用于操控其他量子系统铺平了道路。