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Quantum Physics

Title:Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Abstract: We propose a machine learning approach based on artificial neural network to gain faster insights on the role of geometric contributions to the nonequilibrium fluctuations of an adiabatically temperature-driven quantum heat engine coupled to a cavity. Using the artificial neural network we have explored the interplay between bunched and antibunched photon exchange statistics for different engine parameters. We report that beyond a pivotal cavity temperature, the Fano factor oscillates between giant and low values as a function of phase difference between the driving protocols. We further observe that the standard thermodynamic uncertainty relation is not valid when there are finite geometric contributions to the fluctuations, but holds true for zero phase difference even in presence of coherences.
Comments: 8 pages, 8 figures. keywords: artificial intelligence, quantum thermodynamics, geometric phase, counting statistics, fluctuation theorem, thermodynamic uncertainty relation
Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech)
Journal reference: Phys. Rev. E 99, 022104 (2019)
DOI: 10.1103/PhysRevE.99.022104
Cite as: arXiv:1810.05913 [quant-ph]
  (or arXiv:1810.05913v1 [quant-ph] for this version)

Submission history

From: Himangshu Prabal Goswami [view email]
[v1] Sat, 13 Oct 2018 19:09:04 UTC (995 KB)