  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 based methods which are used to mitigate the effect of
noise in real quantum data. We explore the dynamics of the 1-D Ising model with
transverse and longitudinal magnetic fields, highlighting signatures of
confinement. We find in general Clifford data regression based techniques are
advantageous in comparison with zero-noise extrapolation and obtain
quantitative agreement with exact results for systems of 9 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.
