Scalable measurement error mitigation via iterative bayesian unfolding
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. Published by the American Physical Society 2024
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-00OR22725; SC0020316
- OSTI ID:
- 2310405
- Journal Information:
- Physical Review Research, Journal Name: Physical Review Research Journal Issue: 1 Vol. 6; ISSN PPRHAI; ISSN 2643-1564
- Publisher:
- American Physical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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