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

Journal article

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.

Authors: Barker, AJ; Style, H; Luksch, K; Sunami, S; Garrick, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Machine Learning: Science and Technology
• Volume: 1
• Issue: 1
• Article number: 015007
• Publication date: 2020-02-25
• Acceptance date: 2019-12-19
• DOI: 10.1088/2632-2153/ab6432
• EISSN: 2632-2153
• Language: English
• Keywords: ultracold quantum matter; artificial neural networks; Bose–Einstein condensates; machine learning