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Information scrambling at finite temperature in local quantum systems

Subhayan Sahu and Brian Swingle
Phys. Rev. B 102, 184303 – Published 5 November 2020

Abstract

This paper investigates the temperature dependence of quantum information scrambling in local systems with an energy gap, m, above the ground state. We study the speed and shape of growing Heisenberg operators as quantified by out-of-time-order correlators, with particular attention paid to so-called contour dependence, i.e., dependence on the way operators are distributed around the thermal circle. We report large-scale tensor network numerics on a gapped chaotic spin chain down to temperatures comparable to the gap which show that the speed of operator growth is strongly contour-dependent. The numerics also show a characteristic broadening of the operator wavefront at finite temperature T. To study the behavior at temperatures much below the gap, we perform a perturbative calculation in the paramagnetic phase of a 2+1D O(N) nonlinear sigma model, which is analytically tractable at large N. Using the ladder diagram technique, we find that operators spread at a speed T/m at low temperatures, Tm. In contrast to the numerical findings of spin chain, the large N computation is insensitive to the contour dependence and does not show broadening of operator front. We discuss these results in the context of a recently proposed state-dependent bound on scrambling.

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  • Received 18 June 2020
  • Revised 20 October 2020
  • Accepted 21 October 2020

DOI:https://doi.org/10.1103/PhysRevB.102.184303

©2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsAtomic, Molecular & OpticalQuantum Information, Science & TechnologyStatistical Physics & Thermodynamics

Authors & Affiliations

Subhayan Sahu1 and Brian Swingle1,2

  • 1Condensed Matter Theory Center and Department of Physics, University of Maryland, College Park, Maryland 20742, USA
  • 2Maryland Center for Fundamental Physics and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742, USA

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Issue

Vol. 102, Iss. 18 — 1 November 2020

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