Machine learning optimal control pulses in an optical quantum memory experiment
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914636984483840 |
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| author | Robertson, Elizabeth Esguerra, Luisa Messner, Leon Gallego, Guillermo Wolters, Janik |
| author_facet | Robertson, Elizabeth Esguerra, Luisa Messner, Leon Gallego, Guillermo Wolters, Janik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning optimal control pulses in an optical quantum memory experiment Robertson, Elizabeth Esguerra, Luisa Messner, Leon Gallego, Guillermo Wolters, Janik Quantum Physics 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. |
| title | Machine learning optimal control pulses in an optical quantum memory experiment |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2401.05077 |