Efficient flexible characterization of quantum processors with nested error models

Understand

We present a simple and powerful technique for finding a good error model for a quantum processor.

• The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a quantification of the unmodeled error) at each step.
• Each best-fit model, along with a quantification of its unmodeled error, constitute a characterization of the processor.
• We explain how quantum processor models can be compared with experimental data and to each other.