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arXiv 1908.08495cond-mat.quant-gasphysics.atom-phphysics.comp-ph

Applying machine learning optimization methods to the production of a quantum gas

Adam J. Barker, Harry Style, Kathrin Luksch, Shinichi Sunami, David Garrick, Felix Hill, Christopher J. Foot, Elliot Bentine

英文摘要:

We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose-Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (Differential Evolution), a method based on non-parametric inference (Gaussian Process regression) and a gradient-based function approximator (Artificial Neural Network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.

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