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.

翻译：在量子计算研究中开发了各种噪音模型,以描述因硬件执行不完善而造成的噪音的传播和影响。确定门和读出错误率等参数对于这些模型至关重要。我们对这些参数的身份后方分布采用了贝叶斯推论法,这样可以更详细地描述这些参数的特征分布。通过这样描述装置错误的特征,我们可以进一步提高量子误差的准确性。在IBM量子计算设备上进行的实验表明,我们的方法比供应商使用的现有技术提供更好的减少误差的性能。此外,我们的方法也超过了这种实验中标准的贝叶斯推论方法。