Quantum circuit synthesis using projective simulation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | https://journal.iberamia.org/index.php/intartif/article/view/586 http://repositorio.ufla.br/jspui/handle/1/49327 |
Resumo: | Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use. These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored. We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased. |
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Repositório Institucional da UFLA |
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Quantum circuit synthesis using projective simulationMachine learningReinforcement learningProjective simulationQuantum circuit synthesisQuantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use. These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored. We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.2022-02-15T18:03:32Z2022-02-15T18:03:32Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePIRES, O. M. et al. Quantum circuit synthesis using projective simulation. Inteligência Artificial, [S.l.], v. 24, n. 67, p. 90-101, 2021.https://journal.iberamia.org/index.php/intartif/article/view/586http://repositorio.ufla.br/jspui/handle/1/49327Inteligência Artificialreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAPires, Otto MenegassoDuzzioni, Eduardo InacioMarchi, JerusaSantiago, Rafael deinfo:eu-repo/semantics/openAccesseng2023-05-03T13:18:38Zoai:localhost:1/49327Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T13:18:38Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Quantum circuit synthesis using projective simulation |
title |
Quantum circuit synthesis using projective simulation |
spellingShingle |
Quantum circuit synthesis using projective simulation Pires, Otto Menegasso Machine learning Reinforcement learning Projective simulation Quantum circuit synthesis |
title_short |
Quantum circuit synthesis using projective simulation |
title_full |
Quantum circuit synthesis using projective simulation |
title_fullStr |
Quantum circuit synthesis using projective simulation |
title_full_unstemmed |
Quantum circuit synthesis using projective simulation |
title_sort |
Quantum circuit synthesis using projective simulation |
author |
Pires, Otto Menegasso |
author_facet |
Pires, Otto Menegasso Duzzioni, Eduardo Inacio Marchi, Jerusa Santiago, Rafael de |
author_role |
author |
author2 |
Duzzioni, Eduardo Inacio Marchi, Jerusa Santiago, Rafael de |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Pires, Otto Menegasso Duzzioni, Eduardo Inacio Marchi, Jerusa Santiago, Rafael de |
dc.subject.por.fl_str_mv |
Machine learning Reinforcement learning Projective simulation Quantum circuit synthesis |
topic |
Machine learning Reinforcement learning Projective simulation Quantum circuit synthesis |
description |
Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use. These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored. We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-02-15T18:03:32Z 2022-02-15T18:03:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
PIRES, O. M. et al. Quantum circuit synthesis using projective simulation. Inteligência Artificial, [S.l.], v. 24, n. 67, p. 90-101, 2021. https://journal.iberamia.org/index.php/intartif/article/view/586 http://repositorio.ufla.br/jspui/handle/1/49327 |
identifier_str_mv |
PIRES, O. M. et al. Quantum circuit synthesis using projective simulation. Inteligência Artificial, [S.l.], v. 24, n. 67, p. 90-101, 2021. |
url |
https://journal.iberamia.org/index.php/intartif/article/view/586 http://repositorio.ufla.br/jspui/handle/1/49327 |
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.source.none.fl_str_mv |
Inteligência Artificial reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835051399839744 |