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3 October 2022 Boosting quantum optics experiments with Bayesian optimization (Conference Presentation)
Philipp-Immanuel Schneider, Lin Zschiedrich, Martin Hammerschmidt, Lilli Kuen, Ivan Sekulic, Julien Kluge, Bastian Leykauf, Markus Krutzik, Sven Burger
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Proceedings Volume 12227, Applications of Machine Learning 2022; 122270F (2022) https://doi.org/10.1117/12.2632419
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States
Abstract
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, and 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
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KEYWORDS
Quantum optics
Stochastic processes
Optimization (mathematics)
Performance modeling
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