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dc.contributor.authorZambrano, L.
dc.contributor.authorMuñoz-Moller, A. D.
dc.contributor.authorMuñoz, M.
dc.contributor.authorPereira, L.
dc.contributor.authorDelgado, A
dc.date.accessioned2024-05-23T11:27:21Z
dc.date.available2024-05-23T11:27:21Z
dc.date.issued2024
dc.identifier.citationZambrano, L., Muñoz-Moller, A. D., Muñoz, M., Pereira, L., & Delgado, A. (2024). Avoiding barren plateaus in the variational determination of geometric entanglement. <i>Quantum Science and Technology</i>, <i>9</i>(2), Article 025016. <a href="https://doi.org/10.1088/2058-9565/ad2a16" target="_blank">https://doi.org/10.1088/2058-9565/ad2a16</a>
dc.identifier.otherCONVID_215901295
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/95118
dc.description.abstractThe barren plateau (BP) phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the BP phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition from which a global function can be further optimized without encountering a BP. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18 qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from BPs induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise agree with experiments carried out on IBM's quantum processors for seven qubits.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIOP Publishing
dc.relation.ispartofseriesQuantum Science and Technology
dc.rightsCC BY 4.0
dc.titleAvoiding barren plateaus in the variational determination of geometric entanglement
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202405233882
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2058-9565
dc.relation.numberinseries2
dc.relation.volume9
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysokvanttilaskenta
dc.subject.ysoalgoritmiikka
dc.subject.ysosimulointi
dc.subject.ysooptimointi
dc.subject.ysokvanttitietokoneet
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p39209
jyx.subject.urihttp://www.yso.fi/onto/yso/p3365
jyx.subject.urihttp://www.yso.fi/onto/yso/p4787
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p38991
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1088/2058-9565/ad2a16
jyx.fundinginformationThis work was supported by ANID—Millennium Science Initiative Program—ICN17-012. L Z was supported by the Government of Spain (Severo Ochoa CEX2019-000910-S, TRANQI, and European Union NextGenerationEU PRTR-C17.I1), Fundació Cellex, Fundació Mir-Puig and Generalitat de Catalunya (CERCA program). A D was supported by FONDECYT Grants 1231940 and 1230586. M M was supported by ANID-PFCHA/DOCTORADO-NACIONAL/2019-21190958. L P was supported by ANID-PFCHA/DOCTORADO-BECAS-CHILE/2019-772200275, the CSIC Interdisciplinary Thematic Platform (PTI+) on Quantum Technologies (PTI-QTEP+), the CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM), and the Proyecto Sinérgico CAM 2020 Y2020/TCS-6545 (NanoQuCo-CM).
dc.type.okmA1


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