Mitigating measurement errors in multi-qubit experiments

TL;DR

This paper presents classical post-processing techniques to mitigate measurement errors in multi-qubit quantum experiments, improving the accuracy of observable estimations on noisy quantum devices.

Contribution

It introduces two error mitigation schemes based on noise models and demonstrates their effectiveness on IBM Quantum devices with up to 20 qubits.

Findings

Error rates can be extracted from calibration data using a simple formula.

Applying the inverse noise matrix reduces measurement errors.

Experimental results show improved accuracy in stabilizer measurements.

Abstract

Reducing measurement errors in multi-qubit quantum devices is critical for performing any quantum algorithm. Here we show how to mitigate measurement errors by a classical post-processing of the measured outcomes. Our techniques apply to any experiment where measurement outcomes are used for computing expected values of observables. Two error mitigation schemes are presented based on tensor product and correlated Markovian noise models. Error rates parameterizing these noise models can be extracted from the measurement calibration data using a simple formula. Error mitigation is achieved by applying the inverse noise matrix to a probability vector that represents the outcomes of a noisy measurement. The error mitigation overhead, including the the number of measurements and the cost of the classical post-processing, is exponential in $\epsilon n$, where $\epsilon$ is the maximum error rate and $n$ is the number of qubits. We report experimental demonstration of our error mitigation methods on IBM Quantum devices using stabilizer measurements for graph states with $n\le 12$ qubits and entangled 20-qubit states generated by low-depth random Clifford circuits.