Detailed Characterization of Errors in Syndrome Extraction

Syndrome extraction is a critical component of quantum error correction, and its performance and primary error mechanisms must be understood for high-performance error correction. In this talk, I will introduce methods of estimating detailed Pauli noise models for syndrome extraction circuits. I will discuss top-down approaches using the results of syndrome extraction circuits themselves, and bottom-up approaches that build upon scalable Pauli noise learning methods for gates and mid-circuit measurements. In practice, physical-level operations in quantum processors experience non-stochastic errors, e.g., coherent and weakness errors. I will discuss how these errors affect estimated Pauli error models and how these model inaccuracies affect predictions of the performance of quantum error correction.