Machine-learning-Accelerated Bose-Einstein condensation

Abstract

Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of Rb87 atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of 2.8×103 optically trapped Rb87 atoms from a room-Temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach.