2019
DOI: 10.1088/1367-2630/ab25a4
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Describing many-body localized systems in thermal environments

Abstract: In this work we formulate an efficient method for the description of fully many-body localized systems in weak contact with thermal environments at temperature T. The key idea is to exploit the representation of the system in terms of quasi-local integrals of motion (l-bits) to efficiently derive the generator for the quantum master equation in Born-Markov approximation. We, moreover, show how to compute the steady state of this equation efficiently by using quantum-jump Monte-Carlo techniques as well as by de… Show more

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Cited by 24 publications
(15 citation statements)
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References 74 publications
(143 reference statements)
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“…Here we consider a specific approximation to obtain an effective model which takes all interactions between localized orbitals into account but assumes that these orbitals η n are the localized Anderson (V = 0) orbitals [34,36]. I.e., the renormalization of the η n due to interactions is neglected.…”
Section: Effective Models and Particle Fluctuations In A Partitionmentioning
confidence: 99%
“…Here we consider a specific approximation to obtain an effective model which takes all interactions between localized orbitals into account but assumes that these orbitals η n are the localized Anderson (V = 0) orbitals [34,36]. I.e., the renormalization of the η n due to interactions is neglected.…”
Section: Effective Models and Particle Fluctuations In A Partitionmentioning
confidence: 99%
“…with jump operators L kq = |k q| between many-body eigenstates |k of energy ε k [46,58]. The corresponding transition rates read…”
mentioning
confidence: 99%
“…Here we consider a specific approximation to obtain an effective model which takes all interactions between localized orbitals into account but assumes that these orbitals η n are the localized Anderson (V = 0) orbitals [32,34]. I.e., the renormalization of the η n due to interactions is neglected.…”
Section: Effective Models and Particle Fluctuations In A Partitionmentioning
confidence: 99%