Measurement Error Mitigation via Truncated Neumann Series

TL;DR

This paper introduces a measurement error mitigation technique for near-term quantum devices using truncated Neumann series, improving expectation value accuracy without prior noise calibration and with system-size-independent overhead.

Contribution

The proposed method is a practical, noise-structure-agnostic approach that significantly enhances measurement accuracy in quantum computing without requiring prior noise calibration.

Findings

Substantially improved expectation value accuracy.

Error mitigation overhead independent of system size.

No assumption of noise structure or prior noise calibration.

Abstract

Measurements on near-term quantum processors are inevitably subject to hardware imperfections that lead to readout errors. Mitigation of such unavoidable errors is crucial to better explore and extend the power of near-term quantum hardware. In this work, we propose a method to mitigate measurement errors in computing quantum expectation values using the truncated Neumann series. The essential idea is to cancel the errors by combining various noisy expectation values generated by sequential measurements determined by terms in the truncated series. We numerically test this method and find that the computation accuracy is substantially improved. Our method possesses several advantages: it does not assume any noise structure, it does not require the calibration procedure to learn the noise matrix a prior, and most importantly, the incurred error mitigation overhead is independent of system size, as long as the noise resistance of the measurement device is moderate. All these advantages empower our method as a practical measurement error mitigation method for near-term quantum devices.