Machine-learning-accelerated Bose-Einstein condensation

Zachary Vendeiro

doi:10.1103/PhysRevResearch.4.043216

2022-12-27

DOI：10.1103/PhysRevResearch.4.043216

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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 87Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of 2.8 x 103 optically trapped 87Rb 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.

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PHOTON RECOIL

LASER

COLLISIONS

ATOMS

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Physical Review Research

IF:

4.2

论文数:

7.6K

被引数:

2.7W

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