Scalable quantum processor noise characterization

TL;DR

This paper introduces a scalable method using cumulant expansion to construct approximate measurement fidelity matrices for many-qubit quantum devices, enabling efficient error characterization and mitigation.

Contribution

It presents a novel scalable approach to approximate measurement fidelity matrices for large quantum systems, addressing the exponential cost challenge.

Findings

Enables error mitigation in multi-qubit quantum hardware

Reduces experimental complexity for error characterization

Applicable to various correlation error types

Abstract

Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansion. Our method can also be used to characterize various types of correlation error.