  Various noise models have been developed in quantum computing study to
describe the propagation and effect of the noise which is caused by imperfect
implementation of hardware. Identifying parameters such as gate and readout
error rates are critical to these models. We use a Bayesian inference approach
to identity posterior distributions of these parameters, such that they can be
characterized more elaborately. By characterizing the device errors in this
way, we can further improve the accuracy of quantum error mitigation.
Experiments conducted on IBM's quantum computing devices suggest that our
approach provides better error mitigation performance than existing techniques
used by the vendor. Also, our approach outperforms the standard Bayesian
inference method in such experiments.
