Related papers: Optimized higher-order photon state classification by machine learning

Optimized higher-order photon state classification by machine learning

URL: http://arxiv.org/abs/2404.16203v1
Date: Wed, 24 Apr 2024 20:57:07 GMT
Title: Optimized higher-order photon state classification by machine learning
Authors: Guangpeng Xu, Jeffrey Carvalho, Chiran Wijesundara, Tim Thomay,
Abstract summary: We show 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%. The model exhibits efficient performance particularly with sparse correlation data, with 800 co-detection events to achieve an accuracy of 90%.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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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