Pauli Noise Learning for Mid-Circuit Measurements

Current benchmarks for midcircuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying MCM performance. Here, we introduce a theory for learning stochastic 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 with existing Pauli noise learning techniques to scalably characterize the errors in wide classes of circuits containing MCMs.