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

作者：Adam Barker, Hillary Style, Kathrin Luksch, Shinichi Sunami, David Garrick, Felix Hill, C. J. Foot, Elliot Bentine · 发表于：Machine Learning Science and Technology · 年份：2020 · DOI：10.1088/2632-2153/ab6432 · 被引用次数：36 · 研究领域：Cold Atom Physics and Bose-Einstein Condensates、Gaussian Processes and Bayesian Inference、Sports Analytics and Performance

Abstract 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.