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Entanglement classification with matrix product states

We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families...

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Detalles Bibliográficos
Autores principales: Sanz, M., Egusquiza, I. L., Di Candia, R., Saberi, H., Lamata, L., Solano, E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960485/
https://www.ncbi.nlm.nih.gov/pubmed/27457273
http://dx.doi.org/10.1038/srep30188
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author Sanz, M.
Egusquiza, I. L.
Di Candia, R.
Saberi, H.
Lamata, L.
Solano, E.
author_facet Sanz, M.
Egusquiza, I. L.
Di Candia, R.
Saberi, H.
Lamata, L.
Solano, E.
author_sort Sanz, M.
collection PubMed
description We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by [Image: see text].
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spelling pubmed-49604852016-08-04 Entanglement classification with matrix product states Sanz, M. Egusquiza, I. L. Di Candia, R. Saberi, H. Lamata, L. Solano, E. Sci Rep Article We propose an entanglement classification for symmetric quantum states based on their diagonal matrix-product-state (MPS) representation. The proposed classification, which preserves the stochastic local operation assisted with classical communication (SLOCC) criterion, relates entanglement families to the interaction length of Hamiltonians. In this manner, we establish a connection between entanglement classification and condensed matter models from a quantum information perspective. Moreover, we introduce a scalable nesting property for the proposed entanglement classification, in which the families for N parties carry over to the N + 1 case. Finally, using techniques from algebraic geometry, we prove that the minimal nontrivial interaction length n for any symmetric state is bounded by [Image: see text]. Nature Publishing Group 2016-07-26 /pmc/articles/PMC4960485/ /pubmed/27457273 http://dx.doi.org/10.1038/srep30188 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Sanz, M.
Egusquiza, I. L.
Di Candia, R.
Saberi, H.
Lamata, L.
Solano, E.
Entanglement classification with matrix product states
title Entanglement classification with matrix product states
title_full Entanglement classification with matrix product states
title_fullStr Entanglement classification with matrix product states
title_full_unstemmed Entanglement classification with matrix product states
title_short Entanglement classification with matrix product states
title_sort entanglement classification with matrix product states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4960485/
https://www.ncbi.nlm.nih.gov/pubmed/27457273
http://dx.doi.org/10.1038/srep30188
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