• Open Access

Unified approach to data-driven quantum error mitigation

Angus Lowe, Max Hunter Gordon, Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, and Lukasz Cincio
Phys. Rev. Research 3, 033098 – Published 28 July 2021

Abstract

Achieving near-term quantum advantage will require effective methods for mitigating hardware noise. Data-driven approaches to error mitigation are promising, with popular examples including zero-noise extrapolation (ZNE) and Clifford data regression (CDR). Here, we propose a scalable error mitigation method that conceptually unifies ZNE and CDR. Our approach, called variable-noise Clifford data regression (vnCDR), significantly outperforms these individual methods in numerical benchmarks. vnCDR generates training data first via near-Clifford circuits (which are classically simulable) and second by varying the noise levels in these circuits. We employ a noise model obtained from IBM's Ourense quantum computer to benchmark our method. For the problem of estimating the energy of an 8-qubit Ising model system, vnCDR improves the absolute energy error by a factor of 33 over the unmitigated results and by factors of 20 and 1.8 over ZNE and CDR, respectively. For the problem of correcting observables from random quantum circuits with 64 qubits, vnCDR improves the error by factors of 2.7 and 1.5 over ZNE and CDR, respectively.

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  • Received 28 December 2020
  • Accepted 30 June 2021

DOI:https://doi.org/10.1103/PhysRevResearch.3.033098

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Angus Lowe1,*, Max Hunter Gordon2,*, Piotr Czarnik3, Andrew Arrasmith3, Patrick J. Coles3,4, and Lukasz Cincio3,4

  • 1Department of Combinatorics and Optimization and Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
  • 2Instituto de Física Teórica, UAM/CSIC, Universidad Autónoma de Madrid, 28049 Madrid, Spain
  • 3Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
  • 4Quantum Science Center, Oak Ridge, Tennessee 37931, USA

  • *These authors contributed equally to this work.

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Vol. 3, Iss. 3 — July - September 2021

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