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Q: Fachverband Quantenoptik und Photonik
Q 43: Quantum Technologies
Q 43.3: Poster
Mittwoch, 16. März 2022, 16:30–18:30, P
Machine learning optimal control pulses in an optical quantum memory — •Elizabeth Robertson1,2, Luisa Esguerra1, Guillermo Gallego2,3, and Janik Wolters1,2,3 — 1Deutsches Zentrum für Luft- und Raumfahrt, Institute for Optical Sensor Systems, Rutherfordstraße 2, 12489 Berlin, Germany — 2Technische Universität Berlin, Str. des 17. Junis 135, 10623 Berlin, Germany — 3Einstein Center Digital Future Robert-Koch-Forum, Wilhelmstraße 67, 10117 Berlin, Germany
Optical quantum memories are key components for quantum communication systems, and improving their storage and retrieval efficiency is key for the adoption of the technology [1]. We present a method for machine learning the shape of the optical control pulses used in a hot cesium vapor EIT memory, to maximize the efficiency [2]. Using a genetic algorithm [4], with genes encoded as weighted coefficients of Legendre polynomials, we generate a variety of waveforms, which are given as input into the memory experiment simulation [3]. The retrieval efficiency evaluated, which serves as the fitness function, and subsequent populations are chosen by tournament selection. In the memory simulation, the optimal efficiency could be improved to be 0.51, starting with 0.12 for a unoptimized gaussian control pulse. We will give an outline of the experimental implementation of the method.
[1] Gündoğan, M., et al., npj Quantum Inf 7, 128 (2021).
[2] Wolters, J., et al., Phys. Rev. Lett. 119, (2017)
[3] Rakher, M., et al., Phys. Rev. A 88, (2013)
[4] Katoch, S.,et al., Multimed Tools Appl 80, (2021).