Error mitigation with Clifford quantum-circuit data

TL;DR

This paper introduces a scalable error mitigation technique for gate-based quantum computers using Clifford circuits to generate training data, significantly reducing errors in observable estimation.

Contribution

The authors propose a novel linear ansatz-based error mitigation method utilizing Clifford circuit data, enabling efficient noise correction for arbitrary quantum circuits.

Findings

Order-of-magnitude error reduction on 16-qubit IBMQ hardware

Effective noise mitigation on 64-qubit noisy simulator

Performance analyzed across qubit number, circuit depth, and non-Clifford gates

Abstract

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.