Wildcard error: Quantifying unmodeled errors in quantum processors

Error models for quantum computing processors describe their deviation from ideal behavior and predict the consequences in applications. But experimental behavior is rarely consistent with error models, even in characterization experiments like randomized benchmarking (RB) or gate set tomography (GST). We show how to resolve these inconsistencies, and quantify the rate of unmodeled errors, by augmenting error models with a parameterized wildcard error model. Wildcard error relaxes predictions, and the amount of wildcard error needed quantifies the rate of unmodeled errors. We demonstrate the use of wildcard error to augment RB and GST, and to quantify leakage.