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.

中文翻译：

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用于表征和减轻门和测量误差的贝叶斯方法

在量子计算研究中已经开发了各种噪声模型来描述来自硬件不完善实现的噪声的传播和影响。在这些模型中，关键参数，例如门的错误率，通常被建模为常数。相反，我们将这些参数建模为随机变量，并将新的贝叶斯推理算法应用于经典门和测量误差模型，以识别这些参数的分布。通过以这种方式表征设备错误，我们进一步改进了错误过滤器。在 IBM 的量子计算设备上进行的实验表明，与现有的错误缓解技术相比，我们的方法提供了更好的错误缓解性能，在现有的错误缓解技术中，错误率被估计为确定性值。