Philipp-Immanuel Schneider,1,2 Lin Zschiedrich,1,2 Martin Hammerschmidt,1,2 Lilli Kuen,1,2 Ivan Sekulic,1,2 Julien Kluge,3 Bastian Leykauf,3 Markus Krutzik,3 Sven Burgerhttps://orcid.org/0000-0002-3140-53801,2
1JCMwave GmbH (Germany) 2Zuse Institute Berlin (Germany) 3Humboldt-Univ. zu Berlin (Germany)
Manual optimization of experimental parameters can quickly become too complex and time-consuming if more than a few correlated parameters need to be adjusted. We discuss automating this process using Bayesian optimization. This machine learning-based method is particularly suitable because it can handle noisy measurements, performs a global search and requires relatively few experimental runs. We discuss the efficient, scalable implementation of Bayesian optimization, present practical applications for tuning experimental parameters, and compare it with other local and global heuristic methods to show its application range.
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Philipp-Immanuel Schneider, Lin Zschiedrich, Martin Hammerschmidt, Lilli Kuen, Ivan Sekulic, Julien Kluge, Bastian Leykauf, Markus Krutzik, Sven Burger, "Boosting quantum optics experiments with Bayesian optimization (Conference Presentation)," Proc. SPIE 12227, Applications of Machine Learning 2022, 122270F (3 October 2022); https://doi.org/10.1117/12.2632419