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

TL;DR

This paper demonstrates the use of Clifford data regression error mitigation techniques to simulate quench dynamics of a 1-D Ising model on a 9-qubit quantum computer, achieving accurate results despite noise.

Contribution

It introduces and applies Clifford data regression methods for noise mitigation in quantum simulations, enabling larger system studies than previously possible.

Findings

Clifford data regression outperforms zero-noise extrapolation.

Achieved accurate simulation of 9-qubit systems with deep circuits.

Mitigated noise effects on complex observables like two-point correlations.

Abstract

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.