  The ability to use quantum technology to achieve useful tasks, be they
scientific or industry related, boils down to precise quantum control. In
general it is difficult to assess a proposed solution due to the difficulties
in characterising the quantum system or device. These arise because of the
impossibility to characterise certain components in situ, and are exacerbated
by noise induced by the environment and active controls. Here we present a
general purpose characterisation and control solution making use of a novel
deep learning framework composed of quantum features. We provide the framework,
sample data sets, trained models, and their performance metrics. In addition,
we demonstrate how the trained model can be used to extract conventional
indicators, such as noise power spectra.
