Freiburg 2024 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
Q: Fachverband Quantenoptik und Photonik
Q 67: Machine Learning
Q 67.4: Vortrag
Freitag, 15. März 2024, 15:30–15:45, Aula
Evalutation of machine learning algorithms for applications in quantum gas experiments — •Oliver Anton1, Victoria Henderson1, 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 this problem 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 presentation, we show the results of benchmarking various optimization algorithms and implementations, testing their performance in real-world experiment, subjected to inherent noise.
This work is partially supported by the German Space Agency (DLR) with funds provided by the Federal Ministry of Economic Affairs and Climate Action (BMWK) under grant numbers No. 50WM2067, 50WM2175 and 50WM2247.
Keywords: Machine learning; Mobile experiment; Automatisation; MOT; Molasse