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Machine-learning-accelerated Bose-Einstein condensation
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DOI：10.1103/PhysRevResearch.4.043216.png)
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Abstract
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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.
Keywords:
PHOTON RECOIL
LASER
COLLISIONS
ATOMS
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4.2
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2.7W
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PROCEEDINGS OF THE IEEEIF25.9
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