Optimized higher-order photon state classification by machine learning

Abstract

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 an 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.