Improving the dynamics of quantum sensors with reinforcement learning

38Citations
Citations of this article
45Readers
Mendeley users who have this article in their library.

This article is free to access.

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 (RL) 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 RL method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.

Cite

CITATION STYLE

APA

Schuff, J., Fiderer, L. J., & Braun, D. (2020). Improving the dynamics of quantum sensors with reinforcement learning. New Journal of Physics, 22(3). https://doi.org/10.1088/1367-2630/ab6f1f

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free