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Cited by 487

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Entanglement and thermodynamics after a quantum quench in integrable systems

Open access
Alba, Vincenzo, Calabrese, Pasquale·2017·Proceedings of the National Academy of Sciences

Abstract

Significance: Understanding how statistical ensembles arise from the out-of-equilibrium dynamics of isolated pure systems has been a fascinating question since the early days of quantum mechanics. Recently, it has been proposed that the thermodynamic entropy of the long-time statistical ensemble is the stationary entanglement of a large subsystem in an infinite system. Here, we combine this concept with the quasiparticle picture of the entanglement evolution and integrability-based knowledge of the steady state to obtain exact analytical predictions for the time evolution of the entanglement in arbitrary 1D integrable models. These results explicitly show the transformation between the entanglement and thermodynamic entropy during the time evolution. Thus, entanglement is the natural witness for the generalized microcanonical principle underlying relaxation in integrable models.

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Crossref · 10.1073/pnas.1703516114

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Metadata

Authors
Alba, V., & Calabrese, P.
Venue
Proceedings of the National Academy of Sciences
Published
2017-07-11
Volume
114
Issue
30
Pages
7947-7951
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2486166576
Cited by
487
References
9
FWCI
22.39
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Entanglement and thermodynamics after a quantum quench in integrable systems. https://app.cochat.ai/papers/10.1073/pnas.1703516114
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Alba, V., & Calabrese, P. (2017). Entanglement and thermodynamics after a quantum quench in integrable systems. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1703516114

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.