Efficient modeling of superconducting quantum circuits with tensor networks

Abstract We use a tensor network method to compute the low-energy excitations of a large-scale fluxonium qubit up to a desired accuracy. We employ this numerical technique to estimate the pure-dephasing coherence time of the fluxonium qubit due to charge noise and coherent quantum phase slips from f...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Agustin Di Paolo, Thomas E. Baker, Alexandre Foley, David Sénéchal, Alexandre Blais
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Acceso en línea:https://doaj.org/article/14ba4cdcfdf34b19b72748b00ad90274
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:14ba4cdcfdf34b19b72748b00ad90274
record_format dspace
spelling oai:doaj.org-article:14ba4cdcfdf34b19b72748b00ad902742021-12-02T13:58:01ZEfficient modeling of superconducting quantum circuits with tensor networks10.1038/s41534-020-00352-42056-6387https://doaj.org/article/14ba4cdcfdf34b19b72748b00ad902742021-01-01T00:00:00Zhttps://doi.org/10.1038/s41534-020-00352-4https://doaj.org/toc/2056-6387Abstract We use a tensor network method to compute the low-energy excitations of a large-scale fluxonium qubit up to a desired accuracy. We employ this numerical technique to estimate the pure-dephasing coherence time of the fluxonium qubit due to charge noise and coherent quantum phase slips from first principles, finding an agreement with previously obtained experimental results. By developing an accurate single-mode theory that captures the details of the fluxonium device, we benchmark the results obtained with the tensor network for circuits spanning a Hilbert space as large as 15180. Our algorithm is directly applicable to the wide variety of circuit-QED systems and may be a useful tool for scaling up superconducting quantum technologies.Agustin Di PaoloThomas E. BakerAlexandre FoleyDavid SénéchalAlexandre BlaisNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-11 (2021)
institution DOAJ
collection DOAJ
language EN
topic Physics
QC1-999
Electronic computers. Computer science
QA75.5-76.95
spellingShingle Physics
QC1-999
Electronic computers. Computer science
QA75.5-76.95
Agustin Di Paolo
Thomas E. Baker
Alexandre Foley
David Sénéchal
Alexandre Blais
Efficient modeling of superconducting quantum circuits with tensor networks
description Abstract We use a tensor network method to compute the low-energy excitations of a large-scale fluxonium qubit up to a desired accuracy. We employ this numerical technique to estimate the pure-dephasing coherence time of the fluxonium qubit due to charge noise and coherent quantum phase slips from first principles, finding an agreement with previously obtained experimental results. By developing an accurate single-mode theory that captures the details of the fluxonium device, we benchmark the results obtained with the tensor network for circuits spanning a Hilbert space as large as 15180. Our algorithm is directly applicable to the wide variety of circuit-QED systems and may be a useful tool for scaling up superconducting quantum technologies.
format article
author Agustin Di Paolo
Thomas E. Baker
Alexandre Foley
David Sénéchal
Alexandre Blais
author_facet Agustin Di Paolo
Thomas E. Baker
Alexandre Foley
David Sénéchal
Alexandre Blais
author_sort Agustin Di Paolo
title Efficient modeling of superconducting quantum circuits with tensor networks
title_short Efficient modeling of superconducting quantum circuits with tensor networks
title_full Efficient modeling of superconducting quantum circuits with tensor networks
title_fullStr Efficient modeling of superconducting quantum circuits with tensor networks
title_full_unstemmed Efficient modeling of superconducting quantum circuits with tensor networks
title_sort efficient modeling of superconducting quantum circuits with tensor networks
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/14ba4cdcfdf34b19b72748b00ad90274
work_keys_str_mv AT agustindipaolo efficientmodelingofsuperconductingquantumcircuitswithtensornetworks
AT thomasebaker efficientmodelingofsuperconductingquantumcircuitswithtensornetworks
AT alexandrefoley efficientmodelingofsuperconductingquantumcircuitswithtensornetworks
AT davidsenechal efficientmodelingofsuperconductingquantumcircuitswithtensornetworks
AT alexandreblais efficientmodelingofsuperconductingquantumcircuitswithtensornetworks
_version_ 1718392244025688064