  The classification of higher-order photon emission becomes important with
more methods being developed for deterministic multiphoton generation. The
widely-used second-order correlation g(2) is not sufficient to determine the
quantum purity of higher photon Fock states. Traditional characterization
methods require a large amount of photon detection events which leads to
increased measurement and computation time. Here, we demonstrate a Machine
Learning model based on a 2D Convolutional Neural Network (CNN) for rapid
classification of multiphoton Fock states up to |3> with an overall accuracy of
94%. By fitting the g(3) correlation with simulated photon detection events,
the model exhibits efficient performance particularly with sparse correlation
data, with 800 co-detection events to achieve an accuracy of 90%. Using the
proposed experimental setup, this CNN classifier opens up the possibility for
quasi real-time classification of higher photon states, which holds broad
applications in quantum technologies.
