Optimized higher-order photon state classification by machine learning
arXiv:2404.16203 · doi:10.1063/5.0215915
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 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.
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