Scalable Measurement Error Mitigation via Iterative Bayesian Unfolding

• URL: http://arxiv.org/abs/2210.12284v1
• Date: Fri, 21 Oct 2022 22:42:12 GMT
• Title: Scalable Measurement Error Mitigation via Iterative Bayesian Unfolding
• Authors: Siddarth Srinivasan, Bibek Pokharel, Gregory Quiroz, Byron Boots
• Abstract summary: We present a scalable implementation of iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments. We demonstrate our method by mitigating QC data from experimental preparation of Greenberger-Horne-Zeilinger (GHZ) states up to 127 qubits and implementation of the Bernstein-Vazirani algorithm on up to 26 qubits.
• Score: 30.113578832669695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Measurement error mitigation (MEM) techniques are postprocessing strategies to counteract systematic read-out errors on quantum computers (QC). Currently used MEM strategies face a tradeoff: methods that scale well with the number of qubits return negative probabilities, while those that guarantee a valid probability distribution are not scalable. Here, we present a scheme that addresses both of these issues. In particular, we present a scalable implementation of iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments. We demonstrate our method by mitigating QC data from experimental preparation of Greenberger-Horne-Zeilinger (GHZ) states up to 127 qubits and implementation of the Bernstein-Vazirani algorithm on up to 26 qubits.

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