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arXiv:1908.08495 · Machine Learning Science and Technology

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

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

Published Feb 25, 2020• 37 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1088/2632-2153/ab6432

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Machine learning is emerging as a technology that can enhance physics experiment execution and data analysis. Here, we apply machine learning to accelerate the production of a Bose-Einstein condensate (BEC) of 87Rb^{87}\mathrm{Rb}87Rb atoms by Bayesian optimization of up to 55 control parameters. This approach enables us to prepare BECs of $2.8\ifmmode\times\else\texttimes\fi{}{10}^{3}$ optically trapped 87Rb^{87}\mathrm{Rb}87Rb atoms from a room-temperature gas in 575 ms. The algorithm achieves the fast BEC preparation by applying highly efficient Raman cooling to near quantum degeneracy, followed by a brief final evaporation. We anticipate that many other physics experiments with complex nonlinear system dynamics can be significantly enhanced by a similar machine-learning approach.
Rb atoms. The evaporation trajectory was optimized to maximize the number of atoms cooled down to a Bose-Einstein condensate using Bayesian optimization. After 300 trials within 3 hours, Bayesian optimization discovered trajectories that achieved atom numbers comparable with those of manual tuning by a human expert. Analysis of the machine-learned trajectories revealed minimum requirements for successful evaporative cooling. We found that the manually obtained curve and the machine-learned trajectories were quite similar in terms of evaporation efficiency, although the manual and machine-learned evaporation ramps were significantly different.
Cold atom experiments involve a large number of parameters, and there are even different degrees of coupling between these parameters. To achieve the optimal cooling effect, the optimization of experimental parameters is an indispensable step in the atomic cooling process. An online optimization scheme for atomic cooling experimental parameters based on artificial neural networks and genetic algorithms is proposed in this paper. First, the experimental setup and principle scheme used in the experiment are introduced, with emphasis on elaborating in detail on the laser system, cooling sequence, optimization steps, and algorithm flow. Then, the influence of different network structures on prediction performance is analyzed. Finally, the optimization scheme is verified on an actual atomic cooling experimental setup. Experimental results show that the optimization algorithm can accurately predict the optimal parameters of the experimental setup. Compared with manual optimization, the proposed scheme achieves a more significant cooling effect, with the temperature improved by approximately a factor of two in both horizontal and vertical directions.
Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which is related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered to be a difficult experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.
Abstract Machine-learning (ML) techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning (RL) offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply RL to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This RL agent determines an optimal set of 30 control parameters in a dynamically changing environment that is characterized by 30 sensed parameters. By comparing this method to that of training supervised-learning regression models, as well as to human-driven control schemes, we find that both ML approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the RL method achieves consistent outcomes, even in the presence of a dynamic environment.