Predicting electromagnetically induced transparency based cold atomic engines using deep learning
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 the performance of all t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-06-01
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| Series: | APL Quantum |
| Online Access: | http://dx.doi.org/10.1063/5.0255830 |
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| Summary: | 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 the performance of all the alkali atom-based engines. 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 reveals the limitations of output radiation temperature as a standalone metric for enhanced engine performance. In high-output radiation temperature regime, a Cs-based engine with a higher output-temperature than a 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 radiation temperature regimes. In addition, the ergotropy is found to exhibit a saturating exponential dependency on the control Rabi frequency. |
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| ISSN: | 2835-0103 |