Bulletin of the American Physical Society
54th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Volume 68, Number 7
Monday–Friday, June 5–9, 2023; Spokane, Washington
Session F01: Poster Session I (4:00pm-6:00pm PT)
4:00 PM,
Tuesday, June 6, 2023
Room: Exhibit Hall C
Abstract: F01.00083 : Towards robust neutral-atom BEC production with the help of machine learning
Presenter:
Arina Tashchilina
(University of Alberta)
Authors:
Arina Tashchilina
(University of Alberta)
Nicholas Milson
(University of Alberta)
Tian Ooi
(University of Alberta)
Anna Prus-Czarnecka
(University of Alberta)
Lindsay J LeBlanc
(University of Alberta Department of Physics)
To address the problem of atom number drift in our system over varying time frames, we installed several sensors to track changes in the environment. The high dimensionality of the parameter space prompted us to use a multilayer neural network to identify all the underlying relationships. The trained neural network was successful in predicting atom number drifts.
With a neural network that predicts the atom number, we extend the work to correct for the instabilities.We created a second list of parameters, which could be controlled externally, and we started exploring it with Gaussian processes [1]. We concluded that it was crucial to combine two learning models: one of them has access to control parameters but does not account for drift (gaussian optimizer), while the second one was slower and thus not suited for real-time use, but could account for external parameter drift (neural network). We optimized this dual learning algorithm for our system and explored the parameter space to achieve optimal BEC production under a wide range of parameter settings, allowing our experiments to collect high-quality data continuously.
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