  Large-scale quantum computation requires a fast assessment of the main
sources of error in the implemented quantum gates. To this aim, we provide a
learning based framework that allows to extract the contribution of each
physical noise source to the infidelity of a series of gates with a small
number of experimental measurements. To illustrate this method, we consider the
case of superconducting transmon architectures, where we focus on the diabatic
implementation of the CZ gate with tunable couplers. In this context, we
account for all relevant noise sources, including non-Markovian noise,
electronics imperfections and the effect of tunable couplers to the error of
the computation.
