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Operator Entanglement in Interacting Integrable Quantum Systems: The Case of the Rule 54 Chain.

V Alba1, J Dubail2, M Medenjak3

  • 1Institute for Theoretical Physics, Universiteit van Amsterdam, Science Park 904, Postbus 94485, 1098 XH Amsterdam, Netherlands.

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|July 27, 2019
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Summary

Operator entanglement in quantum systems distinguishes chaotic from integrable dynamics. This study shows operator entanglement grows logarithmically in interacting integrable systems, unlike the linear growth in chaotic systems.

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Area of Science:

  • Quantum mechanics
  • Many-body physics
  • Quantum information theory

Background:

  • Local operators in quantum systems spread over time.
  • Operator entanglement (entanglement entropy in operator space) is a potential indicator of chaotic vs. integrable dynamics.
  • Chaotic systems exhibit linear growth in operator entanglement, while integrable systems are less understood.

Purpose of the Study:

  • To investigate the growth of operator entanglement in interacting integrable quantum systems.
  • To provide an analytical upper bound for operator entanglement in such systems.
  • To distinguish between chaotic and integrable dynamics using operator entanglement.

Main Methods:

  • Analytical upper bound calculation for operator entanglement.
  • Study of the "Rule 54" qubit chain, a representative interacting integrable system.
  • Mapping system dynamics to stable quasiparticle scattering.

Main Results:

  • Operator entanglement grows logarithmically in interacting integrable systems, specifically the Rule 54 qubit chain.
  • This contrasts with the linear growth observed in chaotic systems.
  • The logarithmic growth is attributed to the elastic scattering of stable quasiparticles.

Conclusions:

  • Operator entanglement provides a clear distinction between chaotic and integrable dynamics.
  • Logarithmic growth of operator entanglement is characteristic of interacting integrable systems.
  • The findings offer insights into the behavior of quantum information in integrable models.