SAMOP 2023 – wissenschaftliches Programm
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Q: Fachverband Quantenoptik und Photonik
Q 22: Poster II
Q 22.85: Poster
Dienstag, 7. März 2023, 16:30–19:00, Empore Lichthof
Optical reservoir computing with incoherent optical memory — •Mingwei Yang1,2, Elizabeth Robertson1,2, Leon Meßner1,3, Norman Vincenz Ewald1, Luisa Esguerra1,2, and Janik Wolters1,2 — 1Deutsches Zentrum für Luft- und Raumfahrt, Institute of Optical Sensor Systems, Rutherfordstraße 2, Berlin, Germany. — 2Technische Universität Berlin, Berlin, Germany. — 3Humboldt-Universität zu Berlin, Berlin, Germany.
Reservoir computing is a machine learning method that is particularly suited for dynamic data processing. A fixed reservoir projects the input information to a high-dimensional feature space, and only the readout weights need to be trained, allowing fast data processing with low energy consumption [1]. In this work, we demonstrate an optical reservoir computing using incoherent memory in a cesium vapor cell to predict time-series data. The information is stored in the reservoir by controlling the pump and probe process on the Cs D2 transitions. The coupling between the reservoir and both the input and output data is realized by acousto-optic modulators. [1] G. Tanaka, T. Yamane, J. B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, *Recent advances in physical reservoir computing: A review,* Neural Networks 115, 100*123 (2019). [2] L. Jaurigue, E. Robertson, J. Wolters, and K. Lüdge, *Photonic reservoir computing with non-linear memory cells: interplay between topology, delay and delayed input,* in Emerging Topics in Artificial Intelligence (ETAI) 2022, vol. 12204 (SPIE, 2022), pp. 61*67.