Fast machine-learning online optimization of ultra-cold-atom experiments

TL;DR

This paper introduces an online Gaussian process-based optimization algorithm that efficiently improves Bose-Einstein condensate production by reducing experimental iterations and providing insights into key parameters.

Contribution

It presents a novel online machine learning method that optimizes BEC experiments faster and offers interpretability of parameter importance.

Findings

Achieved high-quality BECs in 10 times fewer experiments.

Identified key parameters influencing BEC production.

Demonstrated the method's superiority over previous optimization techniques.

Abstract

Machine-designed control of complex devices or experiments can discover strategies superior to those developed via simplified models. We describe an online optimization algorithm based on Gaussian processes and apply it to optimization of the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is approximately optimal for s-wave, ergodic dynamics with two-body interactions and no other loss rates, but likely sub-optimal for many real experiments. Machine learning using a Gaussian process, in contrast, develops a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. This is an online process, and an active one, as the Gaussian process model updates on the basis of each subsequent experiment and proposes a new set of parameters as a result. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process.