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Preparation of ordered states in ultra-cold gases using Bayesian optimization

Abstract : Ultra-cold atomic gases are unique in terms of the degree of controllability, both for internal and external degrees of freedom. This makes it possible to use them for the study of complex quantum many-body phenomena. However in many scenarios, the prerequisite condition of faithfully preparing a desired quantum state despite decoherence and system imperfections is not always adequately met. To pave the way to a specific target state, we implement quantum optimal control based on Bayesian optimization. The probabilistic modeling and broad exploration aspects of Bayesian optimization are particularly suitable for quantum experiments where data acquisition can be expensive. Using numerical simulations for the superfluid to Mott-insulator transition for bosons in a lattice as well as for the formation of Rydberg crystals as explicit examples, we demonstrate that Bayesian optimization is capable of finding better control solutions with regards to finite and noisy data compared to existing methods of optimal control.
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https://hal-u-picardie.archives-ouvertes.fr/hal-03612957
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Soumis le : vendredi 18 mars 2022 - 10:28:41
Dernière modification le : mardi 27 septembre 2022 - 04:17:39

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Rick Mukherjee, Frederic Sauvage, Harry Xie, Robert Loew, Florian Mintert. Preparation of ordered states in ultra-cold gases using Bayesian optimization. New Journal of Physics, Institute of Physics: Open Access Journals, 2020, 22 (7), ⟨10.1088/1367-2630/ab8677⟩. ⟨hal-03612957⟩

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