  The fast and accessible verification of nonclassical resources is an
indispensable step towards a broad utilization of continuous-variable quantum
technologies. Here, we use machine learning methods for the identification of
nonclassicality of quantum states of light by processing experimental data
obtained via homodyne detection. For this purpose, we train an artificial
neural network to classify classical and nonclassical states from their
quadrature-measurement distributions. We demonstrate that the network is able
to correctly identify classical and nonclassical features from real
experimental quadrature data for different states of light. Furthermore, we
show that nonclassicality of some states that were not used in the training
phase is also recognized. Circumventing the requirement of the large sample
sizes needed to perform homodyne tomography, our approach presents a promising
alternative for the identification of nonclassicality for small sample sizes,
indicating applicability for fast sorting or direct monitoring of experimental
data.
