High-dimensional reinforcement learning for optimization and control of ultracold quantum gases

TL;DR

This paper demonstrates that reinforcement learning can effectively optimize the control of ultracold quantum gases, outperforming supervised learning and human control in consistency within dynamic experimental environments.

Contribution

It introduces a reinforcement learning approach for controlling ultracold quantum gases, achieving consistent results in complex, fluctuating conditions, which is a novel application in experimental physics.

Findings

Reinforcement learning outperforms supervised learning in consistency.

Both machine learning methods can predict atom numbers accurately.

Reinforcement learning achieves stable control in dynamic environments.

Abstract

Machine-learning techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply reinforcement learning to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This reinforcement learning agent determines an optimal set of thirty control parameters in a dynamically changing environment that is characterized by thirty sensed parameters. By comparing this method to that of training supervised-learning regression models, as well as to human-driven control schemes, we find that both machine learning approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the reinforcement learning method achieves consistent outcomes, even in the presence of a dynamic environment.