Machine-learned QCVV for distinguishing single-qubit noise

We investigate the use of machine learning (ML) algorithms for developing new QCVV protocols. ML algorithms learn approximations to functions that relate experimental data to some property of interest. As an example, we show ML algorithms can successfully learn separating surfaces for distinguishing coherent and stochastic noise affecting a single qubit. The performance of various ML algorithms depends strongly on the geometry of experimental data (in this case, data from gate set tomography experiments). We show performance can be boosted by hyperparameter tuning and feature engineering.