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

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 optimiza...

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Published in:	Machine learning: science and technology Vol. 1; no. 1; pp. 15007 - 15019
Main Authors:	Barker, A J, Style, H, Luksch, K, Sunami, S, Garrick, D, Hill, F, Foot, C J, Bentine, E
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.03.2020
Subjects:	Artificial neural networks Bose-Einstein condensates Cooling Evaporative cooling Evolutionary computation Gaussian process Laser cooling Machine learning Optimization Process parameters Production methods ultracold quantum matter
ISSN:	2632-2153, 2632-2153
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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.
AbstractList	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.
Author	Hill, F Foot, C J Sunami, S Garrick, D Luksch, K Bentine, E Barker, A J Style, H
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SubjectTerms	Artificial neural networks Bose-Einstein condensates Cooling Evaporative cooling Evolutionary computation Gaussian process Laser cooling Machine learning Optimization Process parameters Production methods ultracold quantum matter
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