Rule 54: Exactly solvable model of nonequilibrium statistical mechanics

Berislav Buča, Katja Klobas*, Tomaz Prosen

*Corresponding author for this work

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Abstract

We review recent results on an exactly solvable model of nonequilibrium statistical mechanics, specifically the classical rule 54 reversible cellular automaton and some of its quantum extensions. We discuss the exact microscopic description of nonequilibrium dynamics as well as the equilibrium and nonequilibrium stationary states. This allows us to obtain a rigorous handle on the corresponding emergent hydrodynamic description, which is treated as well. Specifically, we focus on two different paradigms of rule 54 dynamics. Firstly, we consider a finite chain driven by stochastic boundaries, where we provide exact matrix product descriptions of the nonequilibrium steady state, most relevant decay modes, as well as the eigenvector of the tilted Markov chain yielding exact large deviations for a broad class of local and extensive observables. Secondly, we treat the explicit dynamics of macro-states on an infinite lattice and discuss exact closed form results for dynamical structure factor, multi-time-correlation functions and inhomogeneous quenches. Remarkably, these results prove that the model, despite its simplicity, behaves like a regular fluid with coexistence of ballistic (sound) and diffusive (heat) transport. Finally, we briefly discuss quantum interpretation of rule 54 dynamics and explicit results on dynamical spreading of local operators and operator entanglement.

Original languageEnglish
Article number074001
Number of pages70
JournalJournal of Statistical Mechanics: Theory and Experiment
Volume2021
Issue number7
DOIs
Publication statusPublished - 16 Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 IOP Publishing Ltd and SISSA Medialab srl.

Keywords

  • cellular automata
  • exact results
  • large deviations in non-equilibrium systems
  • thermalization

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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