Emergent statistical mechanics in holographic random tensor networks
Shozab Qasim, Jens Eisert, Alexander Jahn

TL;DR
This paper demonstrates that random tensor network states, especially those modeling holographic dualities, tend to equilibrate over time under generic Hamiltonians, linking tensor networks, statistical mechanics, and holography.
Contribution
It establishes the dynamical equilibration of RTN states under broad conditions, extending static ensemble averages to time-dependent behavior in holographic models.
Findings
RTN states equilibrate at large bond dimension.
Equilibration occurs in hyperbolic, MPS, and black hole geometries.
Hierarchy of equilibration linked to entanglement and geometry.
Abstract
Recent years have enjoyed substantial progress in capturing properties of complex quantum systems by means of random tensor networks (RTNs), which form ensembles of quantum states that depend only on the tensor network geometry and bond dimensions. Of particular interest are RTNs on hyperbolic geometries, with local tensors typically chosen from the unitary Haar measure, that model critical boundary states of holographic bulk-boundary dualities. In this work, we elevate static pictures of ensemble averages to a dynamical one, to show that RTN states exhibit equilibration of time-averaged operator expectation values under a highly generic class of Hamiltonians with non-degenerate spectra. We prove that RTN states generally equilibrate at large bond dimension and also in the scaling limit for three classes of geometries: Those of matrix product states, regular hyperbolic tilings, and…
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