Pauli Noise Learning for Mid-Circuit Measurements

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.

arXiv comment6+7 pages, 3+4 figures; v2: changed notation throughout, fixed typos in theory section, added SM section on learnability

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Cited by in corpus (6)

• Scalable noise characterization of syndrome-extraction circuits with averaged circuit eigenvalue sampling
• Efficient self-consistent learning of gate set Pauli noise
• Dynamic quantum circuit compilation
• Characterizing physical and logical errors in a transversal CNOT via cycle error reconstruction
• Mitigating errors in state preparation and measurement with noncomputational states
• Disambiguating Pauli noise in quantum computers