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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2308.05216 |
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| _version_ | 1866910284244844544 |
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| author | Milson, Nicholas Tashchilina, Arina Ooi, Tian Czarnecka, Anna Ahmad, Zaheen F. LeBlanc, Lindsay J. |
| author_facet | Milson, Nicholas Tashchilina, Arina Ooi, Tian Czarnecka, Anna Ahmad, Zaheen F. LeBlanc, Lindsay J. |
| contents | Machine-learning techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply reinforcement learning to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This reinforcement learning agent determines an optimal set of thirty control parameters in a dynamically changing environment that is characterized by thirty 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 machine learning approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the reinforcement learning method achieves consistent outcomes, even in the presence of a dynamic environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05216 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | High-dimensional reinforcement learning for optimization and control of ultracold quantum gases Milson, Nicholas Tashchilina, Arina Ooi, Tian Czarnecka, Anna Ahmad, Zaheen F. LeBlanc, Lindsay J. Quantum Gases Atomic Physics Machine-learning techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply reinforcement learning to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This reinforcement learning agent determines an optimal set of thirty control parameters in a dynamically changing environment that is characterized by thirty 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 machine learning approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the reinforcement learning method achieves consistent outcomes, even in the presence of a dynamic environment. |
| title | High-dimensional reinforcement learning for optimization and control of ultracold quantum gases |
| topic | Quantum Gases Atomic Physics |
| url | https://arxiv.org/abs/2308.05216 |