  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.
