We study the thermalization of the transverse field Ising chain with a power law decaying interaction ∼1/r α following a global quantum quench of the transverse field in two different dynamical regimes. The thermalization behavior is quantified by comparing the full probability distribution function (PDF) of the evolving states with the corresponding thermal state given by the canonical Gibbs ensemble (CGE). To this end, we used the matrix product state (MPS)-based Time Dependent Variational Principle (TDVP) algorithm to simulate both real time evolution following a global quantum quench and the finite temperature density operator. We observe that thermalization is strongly suppressed in the region with strong confinement for all interaction strengths α, whereas thermalization occurs in the region with weak confinement.

Thermalization of long range Ising model in different dynamical regimes: A full counting statistics approach / Ranabhat, Nishan; Collura, Mario. - In: SCIPOST PHYSICS CORE. - ISSN 2666-9366. - 7:2(2024). [10.21468/scipostphyscore.7.2.017]

Thermalization of long range Ising model in different dynamical regimes: A full counting statistics approach

Ranabhat, Nishan;Collura, Mario
2024-01-01

Abstract

We study the thermalization of the transverse field Ising chain with a power law decaying interaction ∼1/r α following a global quantum quench of the transverse field in two different dynamical regimes. The thermalization behavior is quantified by comparing the full probability distribution function (PDF) of the evolving states with the corresponding thermal state given by the canonical Gibbs ensemble (CGE). To this end, we used the matrix product state (MPS)-based Time Dependent Variational Principle (TDVP) algorithm to simulate both real time evolution following a global quantum quench and the finite temperature density operator. We observe that thermalization is strongly suppressed in the region with strong confinement for all interaction strengths α, whereas thermalization occurs in the region with weak confinement.
2024
7
2
017
10.21468/scipostphyscore.7.2.017
https://arxiv.org/abs/2212.00533
Ranabhat, Nishan; Collura, Mario
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/138951
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