Estimating detector error models from syndrome data

Abstract

Protecting quantum information using quantum error correction (QEC) requires repeatedly measuring stabilizers to extract error syndromes that are used to identify and correct errors. Syndrome extraction data provides information about the processes that cause errors. The collective effects of these processes can be described by a detector error model (DEM). We show how to estimate probabilities of individual DEM events, and of aggregated classes of DEM events, using data from multiple cycles of syndrome extraction.