X-Parsed-By: org.apache.tika.parser.DefaultParser
citation_title: Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
twitter:title: Nonequilibrium fluctuations of a driven quantum heat engine via...
og:site_name: arXiv.org
og:title: Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
citation_author: Giri, Sajal Kumar
citation_date: 2018/10/13
title: [1810.05913] Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
og:description: 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.
citation_arxiv_id: 1810.05913
citation_online_date: 2018/10/13
twitter:site: @arxiv
dc:title: [1810.05913] Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
citation_doi: 10.1103/PhysRevE.99.022104
twitter:description: 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...
Content-Encoding: ISO-8859-1
citation_pdf_url: https://arxiv.org/pdf/1810.05913
og:url: https://arxiv.org/abs/1810.05913v1
Content-Language: en
Content-Type: application/xhtml+xml; charset=ISO-8859-1