Advances in Bayesian optimization for photonics and quantum atom optics applications

Philipp-Immanuel Schneider; Philipp-Immanuel Schneider; Xavier Garcia-Santiago; Benjamin Wiegand; Oliver Anton; Markus Krutzik; Carsten Rockstuhl; Carsten Rockstuhl; Sven Burger; Sven Burger

doi:10.1364/IPRSN.2021.JTh1E.2

OSA Advanced Photonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTh1E.2
•https://doi.org/10.1364/IPRSN.2021.JTh1E.2

Advances in Bayesian optimization for photonics and quantum atom optics applications

Philipp-Immanuel Schneider, Xavier Garcia-Santiago, Benjamin Wiegand, Oliver Anton, Markus Krutzik, Carsten Rockstuhl, and Sven Burger

Integrated Photonics Research, Silicon and Nanophotonics 2021

Washington, DC United States
26–29 July 2021
ISBN: 978-1-943580-94-1

From the session
Symposium: Machine Learning with Photonic Systems I (JTh1E)

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P. Schneider, X. Garcia-Santiago, B. Wiegand, O. Anton, M. Krutzik, C. Rockstuhl, and S. Burger, "Advances in Bayesian optimization for photonics and quantum atom optics applications," in OSA Advanced Photonics Congress 2021, B. Yang, F. Leo, A. Tauke-Pedretti, S. Arafin, M. Clerici, L. Caspani, L. Busse, M. Kats, J. Fan, W. Ming (Steve) Lee, D. Caplan, N. Parsons, W. Freude, C. Tremblay, D. Hillerkuss, K. Igarashi, H. Elgala, N. Guerrero Gonzalez, R. "Patty" Stabile, K. Jaeger, R. Saive, H. Aziz, and H. Tan, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTh1E.2.

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Abstract

Bayesian optimization is an efficient numerical tool. We review approaches to improve its scalability and to handle noisy inputs, and we demonstrate applications in pho- tonics design optimization and in control of quantum experiments.

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