论文标题
贝叶斯的表征和缓解门和测量误差的方法
A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors
论文作者
论文摘要
量子计算研究中已经开发了各种噪声模型，以描述由硬件实现不完善的噪声的传播和效果。识别诸如门和读数错误率之类的参数对这些模型至关重要。我们使用贝叶斯推论方法来对这些参数的后验分布，以便可以更精心表征它们。通过以这种方式表征设备错误，我们可以进一步提高缓解量子误差的准确性。在IBM的量子计算设备上进行的实验表明，与供应商使用的现有技术相比，我们的方法提供了更好的错误缓解性能。同样，在此类实验中，我们的方法表现优于标准的贝叶斯推理方法。
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.
