  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.
