  Quantum characterization, validation, and verification (QCVV) techniques are
used to probe, characterize, diagnose, and detect errors in quantum information
processors (QIPs). An important component of any QCVV protocol is a mapping
from experimental data to an estimate of a property of a QIP. Machine learning
(ML) algorithms can help automate the development of QCVV protocols, creating
such maps by learning them from training data. We identify the critical
components of "machine-learned" QCVV techniques, and present a rubric for
developing them. To demonstrate this approach, we focus on the problem of
determining whether noise affecting a single qubit is coherent or stochastic
(incoherent) using the data sets originally proposed for gate set tomography.
We leverage known ML algorithms to train a classifier distinguishing these two
kinds of noise. The accuracy of the classifier depends on how well it can
approximate the "natural" geometry of the training data. We find GST data sets
generated by a noisy qubit can reliably be separated by linear surfaces,
although feature engineering can be necessary. We also show the classifier
learned by a support vector machine (SVM) is robust under finite-sample noise.
