  In this paper machine learning and artificial neural network models are
proposed for the classification of external noise sources affecting a given
quantum dynamics. For this purpose, we train and then validate support vector
machine, multi-layer perceptron and recurrent neural network models with
different complexity and accuracy, to solve supervised binary classification
problems. As a result, we demonstrate the high efficacy of such tools in
classifying noisy quantum dynamics using simulated data sets from different
realizations of the quantum system dynamics. In addition, we show that for a
successful classification one just needs to measure, in a sequence of discrete
time instants, the probabilities that the analysed quantum system is in one of
the allowed positions or energy configurations. Albeit the training of machine
learning models is here performed on synthetic data, our approach is expected
to find application in experimental schemes, as e.g. for the noise benchmarking
of noisy intermediate-scale quantum devices.
