Scalable Measurement Error Mitigation via Iterative Bayesian Unfolding

TL;DR

This paper introduces a scalable iterative Bayesian unfolding method for measurement error mitigation in quantum computing, effectively handling large qubit systems while maintaining valid probability distributions.

Contribution

It presents a scalable implementation of Bayesian unfolding for quantum error mitigation, overcoming previous scalability and validity limitations.

Findings

Successfully mitigated errors in 127-qubit GHZ state data

Applied to 26-qubit Bernstein-Vazirani algorithm

Achieved valid probability distributions in large-scale quantum data

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.