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

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.

Automated summary

Error mitigation through Clifford data regression (CDR) methods is crucial for achieving quantum advantage in the near term. This study demonstrates the effectiveness of CDR techniques in mitigating noise in quantum data, outperforming traditional zero-noise extrapolation methods. The research also shows that CDR-based approaches can accurately model complex observables like two-point correlation functions in large quantum systems.