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Nonequilibrium fluctuations of a driven quantum heat engine via machine learning.
Sajal Kumar Giri1, Himangshu Prabal Goswami1,2
1Finite Systems Division, Max-Planck-Institute for the Physics of Complex Systems, Nöthnitzer Str. 38, 01187 Dresden, Germany.
Physical Review. E
|April 3, 2019
Summary
We used artificial neural networks to study quantum heat engines. Geometric factors influence fluctuations, causing the Fano factor to oscillate with cavity temperature and phase difference.
Area of Science:
- Quantum thermodynamics
- Artificial intelligence in physics
- Quantum optics
Background:
- Quantum heat engines are crucial for understanding energy conversion at the quantum level.
- Nonequilibrium fluctuations and geometric contributions are key to engine performance.
- Cavity coupling introduces complex photon exchange dynamics.
Purpose of the Study:
- To investigate the role of geometric contributions in quantum heat engine fluctuations using machine learning.
- To explore the impact of photon statistics on engine performance.
- To analyze the validity of thermodynamic uncertainty relations under specific conditions.
Main Methods:
- Development of a machine-learning approach utilizing artificial neural networks.
- Analysis of photon exchange statistics (bunched and antibunched).
- Examination of Fano factor behavior with varying engine parameters and cavity temperatures.
Main Results:
- Artificial neural networks provided new insights into geometric contributions to fluctuations.
- Photon statistics significantly influence engine behavior.
- The Fano factor exhibits oscillations beyond a critical cavity temperature.
- Thermodynamic uncertainty relation is violated with finite geometric contributions but holds for zero phase difference.
Conclusions:
- Geometric contributions play a critical role in nonequilibrium fluctuations of quantum heat engines.
- Machine learning offers an efficient tool for exploring complex quantum systems.
- The findings advance the understanding of quantum thermodynamics and uncertainty relations.
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