论文标题
通过学习辅助最佳控制的旋转挤压产生和存储
Generation and storage of spin squeezing via learning-assisted optimal control
论文作者
论文摘要
旋转挤压的产生和存储是量子计量学和量子力学基础上的一个吸引人的话题。实现自旋挤压的主要模型是单轴和两轴扭曲模型。在这里,我们考虑了一个耦合到骨磁场的集体自旋系统,并表明该模型中适当的恒定值控制可以模拟这两个模型的动态行为。更有趣的是,当控制时间变化时,可以获得更好的挤压性能,这是通过增强学习算法生成的。但是,如果涉及集体噪声,该优势将受到限制。为了处理它,我们提出了一种四步策略,用于构建新型的组合控件,其中包括恒定值和随时间变化的控件,但在不同的时间间隔进行。与全日制反向控件相比,合并的控件不仅可以随着时间的推移给出挤压参数的最小值,而且还提供了更好的寿命和更大量的挤压。此外,组合控制的幅度形式比全日制控制的控制更简单,更稳定。因此,我们的计划非常有希望在实践中应用,以改善挤压的产生和存储性能。
The generation and storage of spin squeezing is an attracting topic in quantum metrology and the foundations of quantum mechanics. The major models to realize the spin squeezing are the one- and two-axis twisting models. Here, we consider a collective spin system coupled to a bosonic field, and show that proper constant-value controls in this model can simulate the dynamical behaviors of these two models. More interestingly, a better performance of squeezing can be obtained when the control is time-varying, which is generated via a reinforcement learning algorithm. However, this advantage becomes limited if the collective noise is involved. To deal with it, we propose a four-step strategy for the construction of a new type of combined controls, which include both constant-value and time-varying controls, but performed at different time intervals. Compared to the full time-varying controls, the combined controls not only give a comparable minimum value of the squeezing parameter over time, but also provides a better lifetime and larger full amount of squeezing. Moreover, the amplitude form of a combined control is simpler and more stable than the full time-varying control. Therefore, our scheme is very promising to be applied in practice to improve the generation and storage performance of squeezing.