Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements

We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its...

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Detalles Bibliográficos
Autores: Benestad, Jacob, Tsintzis, Athanasios, Seoane Souto, Rubén, Leijnse, Martin, Van Nieuwenburg, Evert, Danon, Jeroen
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/393788
Acceso en línea:http://hdl.handle.net/10261/393788
https://api.elsevier.com/content/abstract/scopus_id/85200749300
Access Level:acceso abierto
Palabra clave:Andreev reflection
Majorana bound states
Majorana fermions
Optimization problems
Quantum dots
Machine learning
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spelling Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurementsBenestad, JacobTsintzis, AthanasiosSeoane Souto, RubénLeijnse, MartinVan Nieuwenburg, EvertDanon, JeroenAndreev reflectionMajorana bound statesMajorana fermionsOptimization problemsQuantum dotsMachine learningWe demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighboring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite Zeeman splitting. In both cases, the algorithm consistently converges towards high-quality sweet spots. Since tunneling spectroscopy provides one global metric for tuning all on-site potentials simultaneously, this presents a promising way towards tuning longer Kitaev chains, which are required for achieving topological protection of the Majorana modes.We acknowledge funding via the QuantERA II Programme under Grant Agreement No. 101017733, the work being part of INTFELLES-Project No. 333990, which is funded by the Research Council of Norway (RCN). This work was further funded by the Swedish Research Council under Grant Agreement No. 2020-03412, the Spanish CM Talento Program (Project No. 2022-T1/IND-24070), the Spanish Ministry of Science, Innovation, and Universities through Grant No. PID2022-140552NA-I00, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 856526, Nanolund, and the Horizon Europe Framework Program of the European Commission through the European Innovation Council Pathfinder Grant No. 101115315, QuKiT. This work was supported by the Dutch National Growth Fund (NGF), as part of the Quantum Delta NL programme. Simulations were performed on resources provided by the NTNU IDUN/EPIC computing cluster [62], and we thank the NTNU HPC group for their technical support.Peer reviewedAmerican Physical Society0009-0003-5993-76820000-0002-2744-07810000-0002-2978-35340000-0003-3639-85940000-0003-0323-00310000-0001-8088-8772Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/393788https://api.elsevier.com/content/abstract/scopus_id/85200749300reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésPhysical Review Bhttps://doi.org/10.1103/PhysRevB.110.075402Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3937882026-05-22T06:33:51Z
dc.title.none.fl_str_mv Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
title Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
spellingShingle Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
Benestad, Jacob
Andreev reflection
Majorana bound states
Majorana fermions
Optimization problems
Quantum dots
Machine learning
title_short Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
title_full Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
title_fullStr Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
title_full_unstemmed Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
title_sort Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
dc.creator.none.fl_str_mv Benestad, Jacob
Tsintzis, Athanasios
Seoane Souto, Rubén
Leijnse, Martin
Van Nieuwenburg, Evert
Danon, Jeroen
author Benestad, Jacob
author_facet Benestad, Jacob
Tsintzis, Athanasios
Seoane Souto, Rubén
Leijnse, Martin
Van Nieuwenburg, Evert
Danon, Jeroen
author_role author
author2 Tsintzis, Athanasios
Seoane Souto, Rubén
Leijnse, Martin
Van Nieuwenburg, Evert
Danon, Jeroen
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv 0009-0003-5993-7682
0000-0002-2744-0781
0000-0002-2978-3534
0000-0003-3639-8594
0000-0003-0323-0031
0000-0001-8088-8772
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Andreev reflection
Majorana bound states
Majorana fermions
Optimization problems
Quantum dots
Machine learning
topic Andreev reflection
Majorana bound states
Majorana fermions
Optimization problems
Quantum dots
Machine learning
description We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighboring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite Zeeman splitting. In both cases, the algorithm consistently converges towards high-quality sweet spots. Since tunneling spectroscopy provides one global metric for tuning all on-site potentials simultaneously, this presents a promising way towards tuning longer Kitaev chains, which are required for achieving topological protection of the Majorana modes.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/393788
https://api.elsevier.com/content/abstract/scopus_id/85200749300
url http://hdl.handle.net/10261/393788
https://api.elsevier.com/content/abstract/scopus_id/85200749300
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Physical Review B
https://doi.org/10.1103/PhysRevB.110.075402
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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