Freiburg 2024 – wissenschaftliches Programm
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Q: Fachverband Quantenoptik und Photonik
Q 67: Machine Learning
Q 67.5: Vortrag
Freitag, 15. März 2024, 15:45–16:00, Aula
Machine learning optimal control pulses in an optical quantum memory — •Elizabeth Robertson1, Luisa Esguerra1,2, Leon Messner1, Guillermo Gallego3, and Janik Wolters1,2 — 1Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Rutherfordstr. 2, 12489 Berlin, Germany — 2Technische Universiät Berlin, Institute for Optics and Atomic Physics, Hardenbergstr. 36, 10623 Berlin, Germany — 3Einstein Center Digital Future and Science of Intelligence Excellence Cluster 10117 Berlin, Germany
Efficient optical quantum memories are a milestone required for several quantum technologies including repeater-based quantum key distribution and on-demand multi-photon generation [1,2]. We present an optimization of the storage efficiency of an optical electromagnetically induced transparency (EIT) memory experiment in a warm cesium vapor using a genetic algorithm to update the control laser waveform. The write pulse is represented either as a Gaussian or free-form pulse, and the results from the optimization are analyzed and compared. We find that the free-form pulses offer a 3%(7) improvement in efficiency, over the learned Gaussian. By limiting the allowed pulse power in a solution, we show a power-based optimization giving a 30% reduction in power, with minimal efficiency loss.
[1] M. Gündoğan et al., Topical white paper: A case for quantum memories in space (2021), arXiv:2111.09595 [2] J. Nunn et al., Multimode memories in atomic ensembles, Physical Review Letters 101, 260502 (2008).
Keywords: Quantum Memory; Machine Learning