Pauli Noise Learning for Mid-Circuit Measurements

TL;DR

This paper introduces a scalable method called MCM cycle benchmarking for quantifying and characterizing Pauli noise in mid-circuit measurements, improving the ability to benchmark quantum hardware performance.

Contribution

It develops a theoretical framework for learning Pauli noise in MCMs and creates a scalable benchmarking technique that can quantify error rates and correlated errors in quantum circuits.

Findings

MCM cycle benchmarking effectively quantifies error rates in MCMs.

The method can detect correlated errors during mid-circuit measurements.

It integrates with existing noise learning techniques for broader applicability.

Abstract

Current benchmarks for mid-circuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying their performance. Here, we introduce a theory for learning Pauli noise in MCMs and use it to create MCM cycle benchmarking, a scalable method for benchmarking MCMs. MCM cycle benchmarking extracts detailed information about the rates of errors in randomly compiled layers of MCMs and Clifford gates, and we demonstrate how its results can be used to quantify correlated errors during MCMs on current quantum hardware. Our method can be integrated into existing Pauli noise learning techniques to scalably characterize and benchmark wide classes of circuits containing MCMs.