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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Veröffentlicht in:Machine learning: science and technology Jg. 1; H. 1; S. 15007 - 15019
Hauptverfasser: Barker, A J, Style, H, Luksch, K, Sunami, S, Garrick, D, Hill, F, Foot, C J, Bentine, E
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.03.2020
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:MLST-100022.R3
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ab6432