Scalable quantum processor noise characterization

Kathleen E. Hamilton; Tyler Kharazi; Titus Morris; Alexander J. McCaskey; Ryan S. Bennink; Raphael C. Pooser

doi:10.1109/QCE49297.2020.00060

Scalable quantum processor noise characterization

Kathleen E. Hamilton, Tyler Kharazi, Titus Morris, Alexander J. McCaskey, Ryan S. Bennink, Raphael C. Pooser

Quantum Computational Science

Oak Ridge National Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020
Editors	Hausi A. Muller, Greg Byrd, Candace Culhane, Erik DeBenedictis, Travis Humble
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	430-440
Number of pages	11
ISBN (Electronic)	9781728189697
DOIs	https://doi.org/10.1109/QCE49297.2020.00060
State	Published - Oct 2020
Event	2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020 - Denver, United States Duration: Oct 12 2020 → Oct 16 2020

Publication series

Name	Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020

Conference

Conference	2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020
Country/Territory	United States
City	Denver
Period	10/12/20 → 10/16/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

NISQ computing
error mitigation
noise characterization
quantum computing

Access to Document

10.1109/QCE49297.2020.00060

Cite this

Hamilton, K. E., Kharazi, T., Morris, T., McCaskey, A. J., Bennink, R. S., & Pooser, R. C. (2020). Scalable quantum processor noise characterization. In H. A. Muller, G. Byrd, C. Culhane, E. DeBenedictis, & T. Humble (Eds.), Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020 (pp. 430-440). Article 9259938 (Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QCE49297.2020.00060

Hamilton, Kathleen E. ; Kharazi, Tyler ; Morris, Titus et al. / Scalable quantum processor noise characterization. Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020. editor / Hausi A. Muller ; Greg Byrd ; Candace Culhane ; Erik DeBenedictis ; Travis Humble. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 430-440 (Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020).

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abstract = "Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.",

keywords = "NISQ computing, error mitigation, noise characterization, quantum computing",

author = "Hamilton, {Kathleen E.} and Tyler Kharazi and Titus Morris and McCaskey, {Alexander J.} and Bennink, {Ryan S.} and Pooser, {Raphael C.}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020 ; Conference date: 12-10-2020 Through 16-10-2020",

year = "2020",

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language = "English",

series = "Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Hamilton, KE, Kharazi, T, Morris, T, McCaskey, AJ, Bennink, RS & Pooser, RC 2020, Scalable quantum processor noise characterization. in HA Muller, G Byrd, C Culhane, E DeBenedictis & T Humble (eds), Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020., 9259938, Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 430-440, 2020 IEEE International Conference on Quantum Computing and Engineering, QCE 2020, Denver, United States, 10/12/20. https://doi.org/10.1109/QCE49297.2020.00060

Scalable quantum processor noise characterization. / Hamilton, Kathleen E.; Kharazi, Tyler; Morris, Titus et al.
Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020. ed. / Hausi A. Muller; Greg Byrd; Candace Culhane; Erik DeBenedictis; Travis Humble. Institute of Electrical and Electronics Engineers Inc., 2020. p. 430-440 9259938 (Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kharazi, Tyler

AU - Morris, Titus

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N2 - Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.

AB - Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.

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KW - error mitigation

KW - noise characterization

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BT - Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020

A2 - Muller, Hausi A.

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A2 - DeBenedictis, Erik

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Hamilton KE, Kharazi T, Morris T, McCaskey AJ, Bennink RS, Pooser RC. Scalable quantum processor noise characterization. In Muller HA, Byrd G, Culhane C, DeBenedictis E, Humble T, editors, Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 430-440. 9259938. (Proceedings - IEEE International Conference on Quantum Computing and Engineering, QCE 2020). doi: 10.1109/QCE49297.2020.00060

Scalable quantum processor noise characterization

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Keywords

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