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Non-Kochen–Specker Contextuality

Quantum contextuality supports quantum computation and communication. One of its main vehicles is hypergraphs. The most elaborated are the Kochen–Specker ones, but there is also another class of contextual sets that are not of this kind. Their representation has been mostly operator-based and limite...

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Autor principal: Pavičić, Mladen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453090/
https://www.ncbi.nlm.nih.gov/pubmed/37628147
http://dx.doi.org/10.3390/e25081117
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author Pavičić, Mladen
author_facet Pavičić, Mladen
author_sort Pavičić, Mladen
collection PubMed
description Quantum contextuality supports quantum computation and communication. One of its main vehicles is hypergraphs. The most elaborated are the Kochen–Specker ones, but there is also another class of contextual sets that are not of this kind. Their representation has been mostly operator-based and limited to special constructs in three- to six-dim spaces, a notable example of which is the Yu-Oh set. Previously, we showed that hypergraphs underlie all of them, and in this paper, we give general methods—whose complexity does not scale up with the dimension—for generating such non-Kochen–Specker hypergraphs in any dimension and give examples in up to 16-dim spaces. Our automated generation is probabilistic and random, but the statistics of accumulated data enable one to filter out sets with the required size and structure.
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spelling pubmed-104530902023-08-26 Non-Kochen–Specker Contextuality Pavičić, Mladen Entropy (Basel) Article Quantum contextuality supports quantum computation and communication. One of its main vehicles is hypergraphs. The most elaborated are the Kochen–Specker ones, but there is also another class of contextual sets that are not of this kind. Their representation has been mostly operator-based and limited to special constructs in three- to six-dim spaces, a notable example of which is the Yu-Oh set. Previously, we showed that hypergraphs underlie all of them, and in this paper, we give general methods—whose complexity does not scale up with the dimension—for generating such non-Kochen–Specker hypergraphs in any dimension and give examples in up to 16-dim spaces. Our automated generation is probabilistic and random, but the statistics of accumulated data enable one to filter out sets with the required size and structure. MDPI 2023-07-26 /pmc/articles/PMC10453090/ /pubmed/37628147 http://dx.doi.org/10.3390/e25081117 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pavičić, Mladen
Non-Kochen–Specker Contextuality
title Non-Kochen–Specker Contextuality
title_full Non-Kochen–Specker Contextuality
title_fullStr Non-Kochen–Specker Contextuality
title_full_unstemmed Non-Kochen–Specker Contextuality
title_short Non-Kochen–Specker Contextuality
title_sort non-kochen–specker contextuality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453090/
https://www.ncbi.nlm.nih.gov/pubmed/37628147
http://dx.doi.org/10.3390/e25081117
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