Circuit-based quantum random access memory for sparse quantum state preparation
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPE |
dARK ID: | ark:/64986/00130000033hm |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/41848 |
Resumo: | In order to use a quantum device to assess a classical dataset D, we need to representthe set D in a quantum state. Applying a quantum algorithm, that is a quantum state preparation algorithm, to convert classical data into quantum data would be the common method. Loading classical data into a quantum device is required in many current applications. Efficiently preparing a quantum state to be used as the initial state of a quantum algorithm is an essential step in developing efficient quantum algorithms, since many algorithms need to reload the initial state several times during their execution. The cost to initialize a quantum state can compromise the algorithm efficiency if the process of quantum states preparation is not efficient. The topic of quantum states preparation in quantum computing has been the focus of much attention. In this scope, preparing sparse quantum states is a more specific problem that remains open since many quantum algorithms also require sparse initialization. This dissertation presents the results of an investigation on sparse quantum states preparation with the development of three algorithms, with highlight to the preparation of sparse quantum states, the main contributionof this dissertation. From a classical input dataset with M patterns formed by pairs composed of a complex number and a binary pattern with n bits, this algorithm can prepare a quantum state with n qubits and continuous amplitudes. The cost of its steps is O(nM), classical cost of o(MlogM+nM)and requires a lower CNOT number than the main quantum state preparation algorithms currently known. The preparation of a quantumstate with 2 non-zero amplitudes reveals the need of fewer CNOT gates in n>>1 relation to the main known state preparation algorithms, with even more favorable results with s higher and less 1S in the binary string. |
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VERAS, Tiago Mendonça Lucena dehttp://lattes.cnpq.br/0549911789240539http://lattes.cnpq.br/1825502153580661http://lattes.cnpq.br/0314035098884256QUEIROZ, Ruy José Guerra Barretto deSILVA, Adenilton José da2021-11-29T19:27:57Z2021-11-29T19:27:57Z2021-09-13VERAS, Tiago Mendonça Lucena de. Circuit-based quantum random access memory for sparse quantum state preparation. 2021. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Pernambuco, Recife, 2021.https://repositorio.ufpe.br/handle/123456789/41848ark:/64986/00130000033hmIn order to use a quantum device to assess a classical dataset D, we need to representthe set D in a quantum state. Applying a quantum algorithm, that is a quantum state preparation algorithm, to convert classical data into quantum data would be the common method. Loading classical data into a quantum device is required in many current applications. Efficiently preparing a quantum state to be used as the initial state of a quantum algorithm is an essential step in developing efficient quantum algorithms, since many algorithms need to reload the initial state several times during their execution. The cost to initialize a quantum state can compromise the algorithm efficiency if the process of quantum states preparation is not efficient. The topic of quantum states preparation in quantum computing has been the focus of much attention. In this scope, preparing sparse quantum states is a more specific problem that remains open since many quantum algorithms also require sparse initialization. This dissertation presents the results of an investigation on sparse quantum states preparation with the development of three algorithms, with highlight to the preparation of sparse quantum states, the main contributionof this dissertation. From a classical input dataset with M patterns formed by pairs composed of a complex number and a binary pattern with n bits, this algorithm can prepare a quantum state with n qubits and continuous amplitudes. The cost of its steps is O(nM), classical cost of o(MlogM+nM)and requires a lower CNOT number than the main quantum state preparation algorithms currently known. The preparation of a quantumstate with 2 non-zero amplitudes reveals the need of fewer CNOT gates in n>>1 relation to the main known state preparation algorithms, with even more favorable results with s higher and less 1S in the binary string.Com objetivo de usar um dispositivo quântico para avaliar um conjunto de dados clássicos D, precisamos representar o conjunto D em um estado quântico. Aplicar um algoritmo quântico, que é um algoritmo de preparação de estados quânticos, para converter dados clássicos em dados quânticos seria o método comum. Carregar dados clássicos em um dispositivo quântico é necessário em muitas aplicações atuais. A preparação eficientede um estado quântico para ser utilizado como o estado inicial de um algoritmo quântico é uma etapa essencial no desenvolvimento de algoritmos quânticos eficientes, uma vez quemuitos algoritmos precisam recarregar o estado inicial várias vezes durante sua execução. O custo para inicializar um estado quântico pode comprometer a eficiência do algoritmo se o processo de preparação dos estados quânticos não for eficiente. O tópico da preparação de estados quânticos na computação quântica tem sido o foco de muita atenção. Nesse escopo, a preparação de estados quânticos esparsos é um problema mais específico que permanece em aberto, uma vez que muitos algoritmos quânticos também requerem inicialização esparsa. Esta tese apresenta os resultados de uma investigação sobre a preparação de estados quânticos esparsos com o desenvolvimento de três algoritmos, com destaque para a preparação de estados quânticos esparsos, principal contribuição desta tese. Apartir de um conjunto de dados de entrada clássico com M padrões, formados por pares compostos por um número complexo e um padrão binário com n bits, este algoritmo pode preparar um estado quântico com n qubits e amplitudes contínuas. O custo de passos é O(nM), o custo clássico é de O(MlogM+nM)e requer um número de CNOT menor do que os principais algoritmos de preparação de estado quântico conhecidos atualmente. Na preparação de um estado quântico com 2 amplitudes diferentes de zero, revela a necessidade de menos portas CNOT quando n>>1 em relação aos principais algoritmos de preparação de estado conhecidos, com resultados ainda mais favoráveis com s maior e menor 1s na string binária.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessTeoria da computaçãoComputação quânticaCircuit-based quantum random access memory for sparse quantum state preparationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisdoutoradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPEORIGINALTESE Tiago Mendonça Lucena de Veras.pdfTESE Tiago Mendonça Lucena de Veras.pdfapplication/pdf2448463https://repositorio.ufpe.br/bitstream/123456789/41848/1/TESE%20Tiago%20Mendon%c3%a7a%20Lucena%20de%20Veras.pdf848e6e43745de4f17f296a3e727a1140MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81908https://repositorio.ufpe.br/bitstream/123456789/41848/3/license.txtc59d330e2c454f71974f5866a0e8a96aMD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufpe.br/bitstream/123456789/41848/2/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD52TEXTTESE Tiago Mendonça Lucena de Veras.pdf.txtTESE Tiago Mendonça Lucena de Veras.pdf.txtExtracted texttext/plain211266https://repositorio.ufpe.br/bitstream/123456789/41848/4/TESE%20Tiago%20Mendon%c3%a7a%20Lucena%20de%20Veras.pdf.txt7588625abb7c61da8a226b6a1816124fMD54THUMBNAILTESE Tiago Mendonça Lucena de Veras.pdf.jpgTESE Tiago Mendonça Lucena de Veras.pdf.jpgGenerated Thumbnailimage/jpeg1237https://repositorio.ufpe.br/bitstream/123456789/41848/5/TESE%20Tiago%20Mendon%c3%a7a%20Lucena%20de%20Veras.pdf.jpgcbb28d8267f418738682da8ea989c0fbMD55123456789/418482021-11-30 02:09:08.818oai:repositorio.ufpe.br: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ório InstitucionalPUBhttps://repositorio.ufpe.br/oai/requestattena@ufpe.bropendoar:22212021-11-30T05:09:08Repositório Institucional da UFPE - Universidade Federal de Pernambuco (UFPE)false |
dc.title.pt_BR.fl_str_mv |
Circuit-based quantum random access memory for sparse quantum state preparation |
title |
Circuit-based quantum random access memory for sparse quantum state preparation |
spellingShingle |
Circuit-based quantum random access memory for sparse quantum state preparation VERAS, Tiago Mendonça Lucena de Teoria da computação Computação quântica |
title_short |
Circuit-based quantum random access memory for sparse quantum state preparation |
title_full |
Circuit-based quantum random access memory for sparse quantum state preparation |
title_fullStr |
Circuit-based quantum random access memory for sparse quantum state preparation |
title_full_unstemmed |
Circuit-based quantum random access memory for sparse quantum state preparation |
title_sort |
Circuit-based quantum random access memory for sparse quantum state preparation |
author |
VERAS, Tiago Mendonça Lucena de |
author_facet |
VERAS, Tiago Mendonça Lucena de |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/0549911789240539 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/1825502153580661 |
dc.contributor.advisor-coLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/0314035098884256 |
dc.contributor.author.fl_str_mv |
VERAS, Tiago Mendonça Lucena de |
dc.contributor.advisor1.fl_str_mv |
QUEIROZ, Ruy José Guerra Barretto de |
dc.contributor.advisor-co1.fl_str_mv |
SILVA, Adenilton José da |
contributor_str_mv |
QUEIROZ, Ruy José Guerra Barretto de SILVA, Adenilton José da |
dc.subject.por.fl_str_mv |
Teoria da computação Computação quântica |
topic |
Teoria da computação Computação quântica |
description |
In order to use a quantum device to assess a classical dataset D, we need to representthe set D in a quantum state. Applying a quantum algorithm, that is a quantum state preparation algorithm, to convert classical data into quantum data would be the common method. Loading classical data into a quantum device is required in many current applications. Efficiently preparing a quantum state to be used as the initial state of a quantum algorithm is an essential step in developing efficient quantum algorithms, since many algorithms need to reload the initial state several times during their execution. The cost to initialize a quantum state can compromise the algorithm efficiency if the process of quantum states preparation is not efficient. The topic of quantum states preparation in quantum computing has been the focus of much attention. In this scope, preparing sparse quantum states is a more specific problem that remains open since many quantum algorithms also require sparse initialization. This dissertation presents the results of an investigation on sparse quantum states preparation with the development of three algorithms, with highlight to the preparation of sparse quantum states, the main contributionof this dissertation. From a classical input dataset with M patterns formed by pairs composed of a complex number and a binary pattern with n bits, this algorithm can prepare a quantum state with n qubits and continuous amplitudes. The cost of its steps is O(nM), classical cost of o(MlogM+nM)and requires a lower CNOT number than the main quantum state preparation algorithms currently known. The preparation of a quantumstate with 2 non-zero amplitudes reveals the need of fewer CNOT gates in n>>1 relation to the main known state preparation algorithms, with even more favorable results with s higher and less 1S in the binary string. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-11-29T19:27:57Z |
dc.date.available.fl_str_mv |
2021-11-29T19:27:57Z |
dc.date.issued.fl_str_mv |
2021-09-13 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
VERAS, Tiago Mendonça Lucena de. Circuit-based quantum random access memory for sparse quantum state preparation. 2021. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Pernambuco, Recife, 2021. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/41848 |
dc.identifier.dark.fl_str_mv |
ark:/64986/00130000033hm |
identifier_str_mv |
VERAS, Tiago Mendonça Lucena de. Circuit-based quantum random access memory for sparse quantum state preparation. 2021. Tese (Doutorado em Ciência da Computação) - Universidade Federal de Pernambuco, Recife, 2021. ark:/64986/00130000033hm |
url |
https://repositorio.ufpe.br/handle/123456789/41848 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
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http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Ciencia da Computacao |
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UFPE |
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Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
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