  The paper suggest employing machine learning for resource-efficient
classification of quantum correlations in entanglement distribution networks.
Specifically, artificial neural networks (ANN) are utilized to classify quantum
correlations based on collective measurements conducted in the geometry of
entanglement swapping. ANNs are trained to categorize two-qubit quantum states
into five mutually exclusive classes depending on the strength of quantum
correlations exhibited by the states. The precision and recall of the ANN
models are analyzed as functions of the quantum resources consumed, i.e. the
number of collective measurements performed.
