Accelerated Bayesian optimization in deep cooling atoms

TL;DR

This paper introduces an enhanced Bayesian optimization method, MHCS-BO, to significantly accelerate the process of laser cooling atoms to near absolute zero, achieving large cold atom samples efficiently.

Contribution

The paper presents a novel MHCS-BO algorithm that improves optimization speed and accuracy for laser cooling, enabling rapid preparation of ultracold atoms in quantum experiments.

Findings

Twofold increase in optimization efficiency

Achieved approximately 10^8 cold atoms at 0.4 μK within 15 minutes

Demonstrated generalizability to high-dimensional parameter optimization

Abstract

Laser cooling, which cools atomic and molecular gases to near absolute zero, is the crucial initial step for nearly all atomic gas experiments. However, fast achievement of numerous sub-$\mu$K cold atoms is challenging. To resolve the issue, we propose and experimentally validate an intelligent polarization gradient cooling approach enhanced by optical lattice, utilizing Maximum Hypersphere Compensation Sampling Bayesian Optimization (MHCS-BO). MHCS-BO demonstrates a twofold increase in optimization efficiency and superior prediction accuracy compared to conventional Bayesian optimization. Finally, approximate $10^8$ cold atoms at a temperature of 0.4$\pm$0.2 $\mu$K can be achieved given the optimal parameters within 15 minutes. Our work provides an intelligent protocol, which can be generalized to other high-dimension parameter optimization problems, and paves way for preparation of ultracold atom in quantum experiments.