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...
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2021
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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) |
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Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 |
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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 |
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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 |