光量子记忆实验中的机器学习优化控制脉冲

IF 3.8 2区物理与天体物理 Q2 PHYSICS, APPLIED

Physical Review Applied Pub Date : 2024-08-08 DOI:10.1103/physrevapplied.22.024026

Elizabeth Robertson, Luisa Esguerra, Leon Meßner, Guillermo Gallego, Janik Wolters

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引用次数: 0

摘要

高效光量子存储器是若干量子技术（包括基于中继器的量子密钥分发和按需多光子生成）所需的一个里程碑。我们利用遗传算法对暖铯蒸汽中的光电磁诱导透明（EIT）存储器实验进行了效率优化，并分析了由此产生的波形。控制脉冲可表示为高斯脉冲或自由形式脉冲，并对优化结果进行比较。我们发现，使用优化后的自由形态脉冲时，改进系数为 3(7)%。通过限制解决方案中允许的脉冲能量，我们展示了一种基于能量的优化方法，可将能量减少 30%，同时将效率损失降至最低。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine-learning optimal control pulses in an optical quantum memory experiment

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Machine-learning optimal control pulses in an optical quantum memory experiment

Efficient optical quantum memories are a milestone required for several quantum technologies, including repeater-based quantum key distribution and on-demand multiphoton 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 wave forms. 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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来源期刊

Physical Review Applied PHYSICS, APPLIED-

CiteScore

7.80

自引率

8.70%

发文量

760

审稿时长

2.5 months

期刊介绍： Physical Review Applied (PRApplied) publishes high-quality papers that bridge the gap between engineering and physics, and between current and future technologies. PRApplied welcomes papers from both the engineering and physics communities, in academia and industry. PRApplied focuses on topics including: Biophysics, bioelectronics, and biomedical engineering, Device physics, Electronics, Technology to harvest, store, and transmit energy, focusing on renewable energy technologies, Geophysics and space science, Industrial physics, Magnetism and spintronics, Metamaterials, Microfluidics, Nonlinear dynamics and pattern formation in natural or manufactured systems, Nanoscience and nanotechnology, Optics, optoelectronics, photonics, and photonic devices, Quantum information processing, both algorithms and hardware, Soft matter physics, including granular and complex fluids and active matter.