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Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Kumpilov, D. A., Pershin, D. A., Cojocaru, I. S., Khlebnikov, V. A., Pyrkh, I. A., Rudnev, A. E., Fedotova, E. A., Khoruzhii, K. A., Aksentsev, P. A., Gaifutdinov, D. V., Zykova, A. K., Tsyganok, V. V., Akimov, A. V.
Format:	Preprint
Publié:	2023
Sujets:	Quantum Physics
Accès en ligne:	Acceder al recurso
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_version_	1866910380822888448
author	Kumpilov, D. A. Pershin, D. A. Cojocaru, I. S. Khlebnikov, V. A. Pyrkh, I. A. Rudnev, A. E. Fedotova, E. A. Khoruzhii, K. A. Aksentsev, P. A. Gaifutdinov, D. V. Zykova, A. K. Tsyganok, V. V. Akimov, A. V.
author_facet	Kumpilov, D. A. Pershin, D. A. Cojocaru, I. S. Khlebnikov, V. A. Pyrkh, I. A. Rudnev, A. E. Fedotova, E. A. Khoruzhii, K. A. Aksentsev, P. A. Gaifutdinov, D. V. Zykova, A. K. Tsyganok, V. V. Akimov, A. V.
contents	The number of atoms in Bose-Einstein condensate determines the scale of experiments that can be performed, making it crucial for quantum simulations. Optimization of the number of atoms in the condensate is a complex problem which could be efficiently solved using machine learning technique. Nevertheless, this approach usually does not give any insight in the underlying physics. Here we demonstrate possibility to learn physics from the machine learning on an example of condensation of thulium atoms at a 1064-nm dipole trap. Optimization of the number of condensed atoms revealed a saturation, which was explained as limitation imposed by a 3-body recombination process. This limitation was successfully overcome by leveraging Fano-Feshbach resonances.
format	Preprint
id	arxiv_https___arxiv_org_abs_2311_06795
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap Kumpilov, D. A. Pershin, D. A. Cojocaru, I. S. Khlebnikov, V. A. Pyrkh, I. A. Rudnev, A. E. Fedotova, E. A. Khoruzhii, K. A. Aksentsev, P. A. Gaifutdinov, D. V. Zykova, A. K. Tsyganok, V. V. Akimov, A. V. Quantum Physics The number of atoms in Bose-Einstein condensate determines the scale of experiments that can be performed, making it crucial for quantum simulations. Optimization of the number of atoms in the condensate is a complex problem which could be efficiently solved using machine learning technique. Nevertheless, this approach usually does not give any insight in the underlying physics. Here we demonstrate possibility to learn physics from the machine learning on an example of condensation of thulium atoms at a 1064-nm dipole trap. Optimization of the number of condensed atoms revealed a saturation, which was explained as limitation imposed by a 3-body recombination process. This limitation was successfully overcome by leveraging Fano-Feshbach resonances.
title	Inspiration from machine learning on example of optimization of the Bose-Einstein condensate of thulium atoms in a 1064-nm trap
topic	Quantum Physics
url	https://arxiv.org/abs/2311.06795