  This paper proposes a machine learning method to characterize photonic states
via a simple optical circuit and data processing of photon number
distributions, such as photonic patterns. The input states consist of two
coherent states used as references and a two-mode unknown state to be studied.
We successfully trained supervised learning algorithms that can predict the
degree of entanglement in the two-mode state as well as perform the full
tomography of one photonic mode, obtaining satisfactory values in the
considered regression metrics.

Quantum Physics;Mesoscale and Nanoscale Physics;Machine Learning