Machine learning optimal control pulses in an optical quantum memory experiment

• URL: http://arxiv.org/abs/2401.05077v1
• Date: Wed, 10 Jan 2024 11:19:57 GMT
• Title: Machine learning optimal control pulses in an optical quantum memory experiment
• Authors: Elizabeth Robertson, Luisa Esguerra, Leon Messner, Guillermo Gallego, Janik Wolters
• Abstract summary: We present an efficiency of an optical electromagnetically induced transparency (EIT) memory experiment in a warm cesium vapor. We show an energy-based optimization giving a 30% reduction in energy, with minimal efficiency loss.
• Score: 9.603053472399047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient optical quantum memories are a milestone required for several quantum technologies including repeater-based quantum key distribution and on-demand multi-photon generation. We present an efficiency optimization of an optical electromagnetically induced transparency (EIT) memory experiment in a warm cesium vapor using a genetic algorithm and analyze the resulting waveforms. The control pulse is represented either as a Gaussian or free-form pulse, and the results from the optimization are compared. We see an improvement factor of 3(7)\% when using optimized free-form pulses. By limiting the allowed pulse energy in a solution, we show an energy-based optimization giving a 30% reduction in energy, with minimal efficiency loss.

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