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Error mitigation is likely to be key in obtaining near term quantumadvantage. In this work we present one of the first implementations of severalClifford data regression based methods which are used to mitigate the effect ofnoise in real quantum data. We explore the dynamics of the 1-D Ising model withtransverse and longitudinal magnetic fields, highlighting signatures ofconfinement. We find in general Clifford data regression based techniques areadvantageous in comparison with zero-noise extrapolation and obtainquantitative agreement with exact results for systems of 9 qubits with circuitdepths of up to 176, involving hundreds of CNOT gates. This is the largestsystems investigated so far in a study of this type. We also investigate thetwo-point correlation function and find the effect of noise on this morecomplicated observable can be mitigated using Clifford quantum circuit datahighlighting the utility of these methods.

Simulating quench dynamics on a digital quantum computer with data-driven error mitigation