Growth of R\'enyi Entropies in Interacting Integrable Models and the
Breakdown of the Quasiparticle Picture
- URL: http://arxiv.org/abs/2203.17264v3
- Date: Wed, 3 Aug 2022 09:36:56 GMT
- Title: Growth of R\'enyi Entropies in Interacting Integrable Models and the
Breakdown of the Quasiparticle Picture
- Authors: Bruno Bertini, Katja Klobas, Vincenzo Alba, Gianluca Lagnese, and
Pasquale Calabrese
- Abstract summary: We show that the slope of R'enyi entropies can be determined by means of a spacetime duality transformation.
We use this observation to find an explicit exact formula for the slope of R'enyi entropies in all integrable models treatable by thermodynamic Bethe ansatz.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: R\'enyi entropies are conceptually valuable and experimentally relevant
generalisations of the celebrated von Neumann entanglement entropy. After a
quantum quench in a clean quantum many-body system they generically display a
universal linear growth in time followed by saturation. While a finite
subsystem is essentially at local equilibrium when the entanglement saturates,
it is genuinely out-of-equilibrium in the growth phase. In particular, the
slope of the growth carries vital information on the nature of the system's
dynamics, and its characterisation is a key objective of current research. Here
we show that the slope of R\'enyi entropies can be determined by means of a
spacetime duality transformation. In essence, we argue that the slope coincides
with the stationary density of entropy of the model obtained by exchanging the
roles of space and time. Therefore, very surprisingly, the slope of the
entanglement is expressed as an equilibrium quantity. We use this observation
to find an explicit exact formula for the slope of R\'enyi entropies in all
integrable models treatable by thermodynamic Bethe ansatz and evolving from
integrable initial states. Interestingly, this formula can be understood in
terms of a quasiparticle picture only in the von Neumann limit.
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