Applying machine learning optimization methods to the production of a quantum gas

TL;DR

This paper demonstrates the use of three machine learning strategies to optimize atomic cooling processes for Bose-Einstein condensate production, achieving significant improvements and stability without prior knowledge.

Contribution

It introduces simultaneous optimization of laser and evaporative cooling using machine learning, a novel approach in quantum gas production.

Findings

Fourfold increase in BEC atom number with optimization

Successful online optimization without prior apparatus knowledge

Identification of instability sources via machine learning

Abstract

We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (Differential Evolution), a method based on non-parametric inference (Gaussian Process regression) and a gradient-based function approximator (Artificial Neural Network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.