A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors
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Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise from imperfect implementation of hardware. In these models, critical parameters, e.g., error rate of a gate, are typically modeled as constants. Instead, we model such parameters as random variables, and apply a new Bayesian inference algorithm to classical gate and measurement error models to identify the distribution of these parameters. By charactering the device errors in this way, we further improve error filters accordingly. Experiments conducted on IBM's quantum computing devices suggest that our approach provides better error-mitigation performance than existing error-mitigation techniques, in which error rates are estimated as deterministic values. Our approach also outperforms the standard Bayesian inference method in such experiments.
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