Improving the dynamics of quantum sensors with reinforcement learning

TL;DR

This paper demonstrates that reinforcement learning can optimize control pulses in quantum sensors, significantly improving measurement precision and decoherence resistance beyond traditional methods.

Contribution

It introduces the use of the cross-entropy reinforcement learning method to optimize control pulses in quantum-chaotic sensors, enhancing their performance.

Findings

Decoherence can be mitigated more effectively with optimized control.

Measurement sensitivity can be increased by over an order of magnitude.

The mechanism involves a spin-squeezing strategy adapted to damping effects.

Abstract

Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross-entropy method of reinforcement learning to optimize the strength and position of control pulses. Compared to the quantum-chaotic sensors with periodic control pulses in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the reinforcement learning method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.