Digitized-counterdiabatic quantum approximate optimization algorithm

[EN] The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since the QAOA is an Ansatz-dependent algorithm,...

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Autores: Chandarana, Pranav, Hegade, Narendra N., Paul, Koushik, Albarrán Arriagada, Francisco, Solano Villanueva, Enrique Leónidas, Del Campo, Adolfo, Chen, Xi
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/56521
Acceso en línea:http://hdl.handle.net/10810/56521
Access Level:acceso abierto
Palabra clave:model
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spelling Digitized-counterdiabatic quantum approximate optimization algorithmChandarana, PranavHegade, Narendra N.Paul, KoushikAlbarrán Arriagada, FranciscoSolano Villanueva, Enrique LeónidasDel Campo, AdolfoChen, Ximodel[EN] The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since the QAOA is an Ansatz-dependent algorithm, there is always a need to design Ansatze for better optimization. To this end, we propose a digitized version of the QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better Ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitized-CD QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms the standard QAOA in all cases we study.This paper is supported by EU Future and Emerging Technologies (FET) Open Grants EPIQUS (899368) and Quromorphic (828826), the Basque Government IT986-16, the Spanish Government PGC2018-095113-B-I00 (MCIU/AEI/FEDER, UE), projects QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies, NSFC (12075145), and STCSM (2019SHZDZX01-ZX04). X.C. acknowledges the Ramon y Cajal program (RYC-2017-22482).American Physical Society202220222022info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/56521reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MINECO/RYC-2017-22482/info:eu-repo/grantAgreement/MICIU/PGC2018-095113-B-I00/https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.013141info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Atribución 3.0 Españaoai:addi.ehu.eus:10810/565212026-06-18T09:23:17Z
dc.title.none.fl_str_mv Digitized-counterdiabatic quantum approximate optimization algorithm
title Digitized-counterdiabatic quantum approximate optimization algorithm
spellingShingle Digitized-counterdiabatic quantum approximate optimization algorithm
Chandarana, Pranav
model
title_short Digitized-counterdiabatic quantum approximate optimization algorithm
title_full Digitized-counterdiabatic quantum approximate optimization algorithm
title_fullStr Digitized-counterdiabatic quantum approximate optimization algorithm
title_full_unstemmed Digitized-counterdiabatic quantum approximate optimization algorithm
title_sort Digitized-counterdiabatic quantum approximate optimization algorithm
dc.creator.none.fl_str_mv Chandarana, Pranav
Hegade, Narendra N.
Paul, Koushik
Albarrán Arriagada, Francisco
Solano Villanueva, Enrique Leónidas
Del Campo, Adolfo
Chen, Xi
author Chandarana, Pranav
author_facet Chandarana, Pranav
Hegade, Narendra N.
Paul, Koushik
Albarrán Arriagada, Francisco
Solano Villanueva, Enrique Leónidas
Del Campo, Adolfo
Chen, Xi
author_role author
author2 Hegade, Narendra N.
Paul, Koushik
Albarrán Arriagada, Francisco
Solano Villanueva, Enrique Leónidas
Del Campo, Adolfo
Chen, Xi
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv model
topic model
description [EN] The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since the QAOA is an Ansatz-dependent algorithm, there is always a need to design Ansatze for better optimization. To this end, we propose a digitized version of the QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better Ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitized-CD QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms the standard QAOA in all cases we study.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/56521
url http://hdl.handle.net/10810/56521
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO/RYC-2017-22482/
info:eu-repo/grantAgreement/MICIU/PGC2018-095113-B-I00/
https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.013141
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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