  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. We
demonstrate the technique by using it to characterize a simulated noisy 2-qubit
processor.
