Shadow estimation of gate-set properties from random sequences

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Standard

Shadow estimation of gate-set properties from random sequences. / Helsen, J.; Ioannou, M.; Kitzinger, J.; Onorati, E.; Werner, A. H.; Eisert, J.; Roth, I.

I: Nature Communications, Bind 14, Nr. 1, 5039, 19.08.2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Helsen, J, Ioannou, M, Kitzinger, J, Onorati, E, Werner, AH, Eisert, J & Roth, I 2023, 'Shadow estimation of gate-set properties from random sequences', Nature Communications, bind 14, nr. 1, 5039. https://doi.org/10.1038/s41467-023-39382-9

APA

Helsen, J., Ioannou, M., Kitzinger, J., Onorati, E., Werner, A. H., Eisert, J., & Roth, I. (2023). Shadow estimation of gate-set properties from random sequences. Nature Communications, 14(1), [5039]. https://doi.org/10.1038/s41467-023-39382-9

Vancouver

Helsen J, Ioannou M, Kitzinger J, Onorati E, Werner AH, Eisert J o.a. Shadow estimation of gate-set properties from random sequences. Nature Communications. 2023 aug. 19;14(1). 5039. https://doi.org/10.1038/s41467-023-39382-9

Author

Helsen, J. ; Ioannou, M. ; Kitzinger, J. ; Onorati, E. ; Werner, A. H. ; Eisert, J. ; Roth, I. / Shadow estimation of gate-set properties from random sequences. I: Nature Communications. 2023 ; Bind 14, Nr. 1.

Bibtex

@article{ef0c637780db450ea63015fc53e98e97,
title = "Shadow estimation of gate-set properties from random sequences",
abstract = "With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment—random sequence estimation—solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk. {\textcopyright} 2023, Springer Nature Limited.",
author = "J. Helsen and M. Ioannou and J. Kitzinger and E. Onorati and Werner, {A. H.} and J. Eisert and I. Roth",
year = "2023",
month = aug,
day = "19",
doi = "10.1038/s41467-023-39382-9",
language = "English",
volume = "14",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - Shadow estimation of gate-set properties from random sequences

AU - Helsen, J.

AU - Ioannou, M.

AU - Kitzinger, J.

AU - Onorati, E.

AU - Werner, A. H.

AU - Eisert, J.

AU - Roth, I.

PY - 2023/8/19

Y1 - 2023/8/19

N2 - With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment—random sequence estimation—solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk. © 2023, Springer Nature Limited.

AB - With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment—random sequence estimation—solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk. © 2023, Springer Nature Limited.

U2 - 10.1038/s41467-023-39382-9

DO - 10.1038/s41467-023-39382-9

M3 - Journal article

C2 - 37598209

VL - 14

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 5039

ER -

ID: 365707096