Generation and storage of spin squeezing via learning-assisted optimal control

TL;DR

This paper introduces a learning-assisted optimal control method to generate and store spin squeezing in quantum systems, improving performance and stability over traditional approaches.

Contribution

It proposes a combined control strategy using both constant and time-varying controls optimized via reinforcement learning for enhanced spin squeezing.

Findings

Combined controls achieve comparable squeezing with better lifetime.

Reinforcement learning improves control performance.

Combined controls are simpler and more stable.

Abstract

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.