  Machine-learning and neural-network approaches have gained huge attention in
the context of quantum science and technology in recent years. One of the most
essential tasks for the future development of quantum technologies is the
verification of nonclassical resources. Here, we present an artificial neural
network approach for the identification of nonclassical states of light based
on recorded measurement statistics. In particular, we implement and train a
network which is capable of recognizing nonclassical states based on the click
statistics recorded with multiplexed detectors. We use simulated data for
training and testing the network, and we show that it is capable of identifying
some nonclassical states even if they were not used in the training phase.
Especially, in the case of small sample sizes, our approach can be more
sensitive in identifying nonclassicality than established criteria which
suggests possible applications in presorting of experimental data and online
applications.
