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  • Open Access

Scalable measurement error mitigation via iterative bayesian unfolding

Bibek Pokharel1,2,3,*, Siddarth Srinivasan4,*, Gregory Quiroz5,6, and Byron Boots4

  • 1Department of Physics and Astronomy, University of Southern California, Los Angeles, California, 90089, USA
  • 2Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, California 90089, USA
  • 3IBM Quantum, IBM Research – Almaden, San Jose, California 95120, USA
  • 4Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA
  • 5Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland 20723, USA
  • 6William H. Miller III Department of Physics & Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA

  • *These authors contributed equally to this work.
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Phys. Rev. Research 6, 013187 – Published 21 February, 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.013187

Abstract

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead.

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Phys. Rev. Research 6, 013187– Published 21 February, 2024
  • Received 17 March 2023
  • Accepted 1 September 2023
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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.

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