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arXiv 2501.03060quant-phphysics.atom-ph
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使用深度学习预测基于电磁诱导透明的冷原子发动机

Predicting Electromagnetically Induced Transparency based Cold Atomic Engines using Deep Learning

Manash Jyoti Sarmah, Himangshu Prabal Goswami

AI总结:

本文构建人工神经网络预测电磁诱导透明框架下的Λ型碱金属冷原子量子热机性能,发现输出辐射温度不能单独表征发动机优劣,且ergotropy随控制Rabi频率呈饱和指数变化。

摘要:

我们开发了一种人工神经网络模型,用于预测在实验已实现的电磁诱导透明框架内工作的量子热机。我们特别关注基于碱金属原子的Λ型冷原子系统。该网络使我们能够分析所有基于碱金属原子的发动机性能。我们依据输出辐射温度、功和ergotropy(可用功提取量)这三个性能指标来预测和分析高性能发动机。与传统观念相反,该算法揭示了将输出辐射温度作为衡量发动机性能提升的单一指标所存在的局限性。在高输出温度区间,Cs基发动机虽然比Rb基发动机具有更高的输出温度,却表现出更低的功和ergotropy。对于在高、低输出温度区间具有共同预测状态的不同原子发动机,这一结论均成立。此外,研究发现ergotropy对控制Rabi频率呈现饱和指数依赖关系。

英文摘要:

We develop an artificial neural network model to predict quantum heat engines working within the experimentally realized framework of electromagnetically induced transparency. We specifically focus on Λ-type alkali-based cold atomic systems. This network allows us to analyze all the alkali atom-based engines' performance. High performance engines are predicted and analyzed based on three figures of merit output, radiation temperature, work and ergotropy. Contrary to traditional notion, the algorithm reveal the limitations of output radiation temperature as a stand alone metric for enhanced engine performance. In high output temperature regime, Cs based engine with a higher output temperature than Rb based engine is characterized by lower work and ergotropy. This is found to be true for different atomic engines with common predicted states in both high and low output temperature regimes. Additionally, the ergotropy is found to exhibit a saturating exponential dependency on the control Rabi frequency.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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