Bonn 2025 – scientific programme
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A: Fachverband Atomphysik
A 11: Poster – Cold Atoms and Molecules, Matter Waves (joint session Q/A/MO)
A 11.7: Poster
Tuesday, March 11, 2025, 14:00–16:00, Tent
Evalutation of machine learning algorithms for applications in quantum gas experiments — •Oliver Anton1, Elisa Da Ros1, Philipp-Immanuel Schneider3,4, Ivan Sekulic3,4, Sven Burger3,4, and Markus Krutzik1,2 — 1Institut für Physik and IRIS, Humboldt-Universität zu Berlin — 2Ferdinand-Braun-Institut, Berlin — 3JCMwave GmbH, Berlin — 4Zuse Institute Berlin (ZIB), Berlin
The generation of clouds containing cold and ultra-cold atoms is a complex process that requires the optimization of noisy data in multi dimensional parameter spaces.
Optimization of such problems can present challenges both in and outside of the lab due to constrains in time, expertise, or access for lengthy manual optimization.
Machine learning offers a solution thanks to its ability to efficiently optimize high dimensional problems without the need for knowledge of the experiment itself.
In this poster, we present the results of benchmarking various optimization algorithms and implementations. Their performance is tested in a cold atom experiment, subjected to inherent noise [1].
Current research aims towards the preparation of the cloud for quantum memory applications [2], by engineering the optical density using the tested algorithms.
[1] O. Anton et al., Machine Learning: Science and Technology 5 025022, 2024
[2] E. Da Ros et al., Physical Review Research 5 033003, 2023
Keywords: Machine learning; BEC; Mobile experiment; Automatisation