Error mitigation is likely to be key in obtaining near term quantum advantage. In this work we present one of the first implementations of several Clifford data regression (CDR) based methods which are used to mitigate the effect of noise in real quantum data. We explore the dynamics of the 1D Ising model with transverse and longitudinal magnetic fields, highlighting signatures of confinement. We find in general CDR based techniques are advantageous in comparison with zero-noise extrapolation and obtain quantitative agreement with exact results for systems of nine qubits with circuit depths of up to 176, involving hundreds of CNOT gates. This is the largest systems investigated so far in a study of this type. We also investigate the two-point correlation function and find the effect of noise on this more complicated observable can be mitigated using Clifford quantum circuit data highlighting the utility of these methods.

错误缓解可能是获得近期量子优势的关键。在这项工作中，我们展示了几种基于 Clifford 数据回归 (CDR) 的方法的首批实现之一，这些方法用于减轻真实量子数据中的噪声影响。我们探索具有横向和纵向磁场的 1D Ising 模型的动力学，突出限制的特征。我们发现，一般而言，基于 CDR 的技术与零噪声外推相比具有优势，并且与电路深度高达 176 的 9 个量子位系统的精确结果获得定量一致，涉及数百个 CNOT 门。这是迄今为止在此类研究中调查的最大系统。