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Quantifying non-Markovianity due to driving and a finite-size environment in an open quantum system

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posted on 19.01.2018, 09:34 authored by Rui Sampaio, Samu Suomela, Rebecca Schmidt, Tapio Ala-NissilaTapio Ala-Nissila
© 2017 American Physical Society.We study non-Markovian effects present in a driven qubit coupled to a finite environment using a recently proposed model developed in the context of calorimetric measurements of open quantum systems. To quantify the degree of non-Markovianity we use the Breuer-Laine-Piilo (BLP) measure [H.-P. Breuer, Phys. Rev. Lett. 103, 210401 (2009)PRLTAO0031-900710.1103/PhysRevLett.103.210401]. We show that information backflow only occurs in the case of driving, in which case we investigate the dependence of memory effects on the environment size, driving amplitude, and coupling to the environment. We show that the degree of non-Markovianity strongly depends on the ratio between the driving amplitude and the coupling strength. We also show that the degree of non-Markovianity does not decrease monotonically as a function of the environment size.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Physical Review A: Atomic, Molecular and Optical Physics

Volume

95

Issue

2

Citation

SAMPAIO, R., 2017. Quantifying non-Markovianity due to driving and a finite-size environment in an open quantum system. Physical Review A: Atomic, Molecular and Optical Physics, 95: 022120.

Publisher

© American Physical Society

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2017-02-21

Notes

This paper was accepted for publication in the journal Physical Review A and the definitive published version is available at https://doi.org/10.1103/PhysRevA.95.022120

ISSN

1094-1622

eISSN

1094-1622

Language

en

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