Entanglement witnesses with tensor networks: characterizing entanglement in large systems

Detalhes bibliográficos
Autor(a) principal: Lucas Vieira Barbosa
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: http://hdl.handle.net/1843/31439
Resumo: Entanglement is certainly one of the most fascinating phenomena observed in Nature, and even after decades of research in entanglement theory there is still much to be discovered and understood. In particular, there are still few results detailing and characterizing the entanglement structure in strongly correlated systems on a large scale, involving a large number of subsystems. The most typical approach for studying in detail the entanglement structure in quantum states has been through the optimization of Entanglement Witnesses, optimized by means of algorithmic techniques such as Semidefinite Programming applied to matrices. However, this matrix description renders the technique computationally nonviable for large scale systems due to the exponential growth of the dimension of the optimization parameters. All these techniques are nonviable for a relatively small number of particles (on the order of n ~ 10^1). For these reasons, Tensor Networks have attracted the attention of researchers over the last decades for being more efficient ways to describe and simulate quantum systems composed of many parts, as they are more natural and efficient descriptions of the quantum correlations between subsystems. However, few progress has been made for the detailed characterization of entanglement in such systems, and the literature still hasn't fully bridged the gap between the two formalisms. In this work, we propose a small step towards the goal of adapting the formalism of entanglement witnesses in a way which is compatible with the description of quantum systems by means of tensor networks, making it possible the characterization of entanglement in large scale systems.
id UFMG_36a5ee3e7a49f48bfef8d3ee44a28cca
oai_identifier_str oai:repositorio.ufmg.br:1843/31439
network_acronym_str UFMG
network_name_str Repositório Institucional da UFMG
repository_id_str
spelling Reinaldo Oliveira Viannahttp://lattes.cnpq.br/8413008847105670Thiago Oliveira Macielhttp://lattes.cnpq.br/0618716919641724Carlos Henrique MonkenWalber Hugo de Britohttp://lattes.cnpq.br/2536186685642316Lucas Vieira Barbosa2019-12-05T19:48:21Z2019-12-05T19:48:21Z2019-08-02http://hdl.handle.net/1843/31439Entanglement is certainly one of the most fascinating phenomena observed in Nature, and even after decades of research in entanglement theory there is still much to be discovered and understood. In particular, there are still few results detailing and characterizing the entanglement structure in strongly correlated systems on a large scale, involving a large number of subsystems. The most typical approach for studying in detail the entanglement structure in quantum states has been through the optimization of Entanglement Witnesses, optimized by means of algorithmic techniques such as Semidefinite Programming applied to matrices. However, this matrix description renders the technique computationally nonviable for large scale systems due to the exponential growth of the dimension of the optimization parameters. All these techniques are nonviable for a relatively small number of particles (on the order of n ~ 10^1). For these reasons, Tensor Networks have attracted the attention of researchers over the last decades for being more efficient ways to describe and simulate quantum systems composed of many parts, as they are more natural and efficient descriptions of the quantum correlations between subsystems. However, few progress has been made for the detailed characterization of entanglement in such systems, and the literature still hasn't fully bridged the gap between the two formalisms. In this work, we propose a small step towards the goal of adapting the formalism of entanglement witnesses in a way which is compatible with the description of quantum systems by means of tensor networks, making it possible the characterization of entanglement in large scale systems.Emaranhamento é certamente um dos fenômenos mais fascinantes observados na Natureza, e mesmo após de décadas de pesquisas na teoria de emaranhamento ainda há muito a ser descoberto e entendido. Em particular, ainda há poucos resultados detalhando e caracterizando a estrutura do emaranhamento em sistemas fortemente correlacionados em larga escala, envolvendo um número grande de subsistemas. A forma mais comum de se estudar detalhadamente a estrutura de emaranhamento em estados quânticos tem sido através da otimização de Testemunhas de Emaranhamento (Entanglement Witnesses), otimizadas utilizando-se técnicas algorítmicas como Programação Semidefinida (Semidefinite Programming) aplicada à matrizes. No entanto, esta descrição matricial torna a técnica computacionalmente inviável para sistemas em larga escala devido ao crescimento exponencial da dimensão do espaço de parâmetros a serem otimizados. Todas as técnicas se tornam inviáveis para um número relativamente pequeno de partículas (na ordem de n ~ 10^1). Por estes motivos, Redes Tensoriais (Tensor Networks) têm atraído a atenção de pesquisadores nas últimas décadas por serem formas eficientes de descrever e simular sistemas quânticos compostos de muitas partes, pois são descrições mais naturais e eficientes das correlações quânticas entre os subsistemas. No entanto, pouco tem sido feito para caracterização detalhada do emaranhamento em tais sistemas, e a literatura ainda carece de uma ponte entre os dois formalismos. Neste trabalho, propomos um primeiro pequeno passo rumo ao objetivo de adaptar o formalismo de testemunhas de emaranhamento de forma compatível com a descrição de sistemas quânticos através de redes tensoriais, tornando possível a caracterização de emaranhamento em sistemas de larga escala.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorengUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em FísicaUFMGBrasilICX - DEPARTAMENTO DE FÍSICAInformação quânticaTeoria quânticaEntanglementEntanglement witnessesEntanglement in pure statesNegative partial transposeSemidefinite programmingTensor networksMatrix product stateMatrix product operatorDensity matrix renormalization groupPartial transposeEntanglement witnesses with tensor networks: characterizing entanglement in large systemsTestemunhas de emaranhamento com redes tensoriais: caracterizando emaranhamento em sistemas grandesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALEntanglement_Witness_with_Tensor_Networks.pdfEntanglement_Witness_with_Tensor_Networks.pdfapplication/pdf2975123https://repositorio.ufmg.br/bitstream/1843/31439/1/Entanglement_Witness_with_Tensor_Networks.pdf7b0d930572890ffaf3bc86004a7470fdMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82119https://repositorio.ufmg.br/bitstream/1843/31439/2/license.txt34badce4be7e31e3adb4575ae96af679MD52TEXTEntanglement_Witness_with_Tensor_Networks.pdf.txtEntanglement_Witness_with_Tensor_Networks.pdf.txtExtracted texttext/plain198621https://repositorio.ufmg.br/bitstream/1843/31439/3/Entanglement_Witness_with_Tensor_Networks.pdf.txt44dde9c3f346794dad82c9e9c3beac55MD531843/314392019-12-06 03:27:56.876oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2019-12-06T06:27:56Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Entanglement witnesses with tensor networks: characterizing entanglement in large systems
dc.title.alternative.pt_BR.fl_str_mv Testemunhas de emaranhamento com redes tensoriais: caracterizando emaranhamento em sistemas grandes
title Entanglement witnesses with tensor networks: characterizing entanglement in large systems
spellingShingle Entanglement witnesses with tensor networks: characterizing entanglement in large systems
Lucas Vieira Barbosa
Entanglement
Entanglement witnesses
Entanglement in pure states
Negative partial transpose
Semidefinite programming
Tensor networks
Matrix product state
Matrix product operator
Density matrix renormalization group
Partial transpose
Informação quântica
Teoria quântica
title_short Entanglement witnesses with tensor networks: characterizing entanglement in large systems
title_full Entanglement witnesses with tensor networks: characterizing entanglement in large systems
title_fullStr Entanglement witnesses with tensor networks: characterizing entanglement in large systems
title_full_unstemmed Entanglement witnesses with tensor networks: characterizing entanglement in large systems
title_sort Entanglement witnesses with tensor networks: characterizing entanglement in large systems
author Lucas Vieira Barbosa
author_facet Lucas Vieira Barbosa
author_role author
dc.contributor.advisor1.fl_str_mv Reinaldo Oliveira Vianna
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/8413008847105670
dc.contributor.advisor2.fl_str_mv Thiago Oliveira Maciel
dc.contributor.advisor2Lattes.fl_str_mv http://lattes.cnpq.br/0618716919641724
dc.contributor.referee1.fl_str_mv Carlos Henrique Monken
dc.contributor.referee2.fl_str_mv Walber Hugo de Brito
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/2536186685642316
dc.contributor.author.fl_str_mv Lucas Vieira Barbosa
contributor_str_mv Reinaldo Oliveira Vianna
Thiago Oliveira Maciel
Carlos Henrique Monken
Walber Hugo de Brito
dc.subject.por.fl_str_mv Entanglement
Entanglement witnesses
Entanglement in pure states
Negative partial transpose
Semidefinite programming
Tensor networks
Matrix product state
Matrix product operator
Density matrix renormalization group
Partial transpose
topic Entanglement
Entanglement witnesses
Entanglement in pure states
Negative partial transpose
Semidefinite programming
Tensor networks
Matrix product state
Matrix product operator
Density matrix renormalization group
Partial transpose
Informação quântica
Teoria quântica
dc.subject.other.pt_BR.fl_str_mv Informação quântica
Teoria quântica
description Entanglement is certainly one of the most fascinating phenomena observed in Nature, and even after decades of research in entanglement theory there is still much to be discovered and understood. In particular, there are still few results detailing and characterizing the entanglement structure in strongly correlated systems on a large scale, involving a large number of subsystems. The most typical approach for studying in detail the entanglement structure in quantum states has been through the optimization of Entanglement Witnesses, optimized by means of algorithmic techniques such as Semidefinite Programming applied to matrices. However, this matrix description renders the technique computationally nonviable for large scale systems due to the exponential growth of the dimension of the optimization parameters. All these techniques are nonviable for a relatively small number of particles (on the order of n ~ 10^1). For these reasons, Tensor Networks have attracted the attention of researchers over the last decades for being more efficient ways to describe and simulate quantum systems composed of many parts, as they are more natural and efficient descriptions of the quantum correlations between subsystems. However, few progress has been made for the detailed characterization of entanglement in such systems, and the literature still hasn't fully bridged the gap between the two formalisms. In this work, we propose a small step towards the goal of adapting the formalism of entanglement witnesses in a way which is compatible with the description of quantum systems by means of tensor networks, making it possible the characterization of entanglement in large scale systems.
publishDate 2019
dc.date.accessioned.fl_str_mv 2019-12-05T19:48:21Z
dc.date.available.fl_str_mv 2019-12-05T19:48:21Z
dc.date.issued.fl_str_mv 2019-08-02
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/31439
url http://hdl.handle.net/1843/31439
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Física
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICX - DEPARTAMENTO DE FÍSICA
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/31439/1/Entanglement_Witness_with_Tensor_Networks.pdf
https://repositorio.ufmg.br/bitstream/1843/31439/2/license.txt
https://repositorio.ufmg.br/bitstream/1843/31439/3/Entanglement_Witness_with_Tensor_Networks.pdf.txt
bitstream.checksum.fl_str_mv 7b0d930572890ffaf3bc86004a7470fd
34badce4be7e31e3adb4575ae96af679
44dde9c3f346794dad82c9e9c3beac55
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)
repository.mail.fl_str_mv
_version_ 1803589543860895744