Bayesian mitigation of measurement errors in multi-qubit experiments

Abstract: We introduce an implementation of Bayesian measurement error mitigation tailored for multiqubit experiments on near-term quantum devices. Our approach leverages complete information from the readout signal, which is available before any binary state assignment of the qubits. We provide a detailed algorithm workflow, from the calibration of detector response functions to the post-processing of measurement outcomes, offering a computationally efficient solution suitable for the output size typical of current quantum computing devices and quantum algorithms. We benchmark our protocol on actual quantum computers with superconducting qubits, where the readout signal encodes the measurement information in the IQ clouds before qubit state assignment. Finally, we compare the performance of our algorithm against other measurement error mitigation methods, such as iterative Bayesian unfolding and noise matrix inversion.