Optimized higher-order photon state classification by machine learning

TL;DR

This paper presents a machine learning approach using a 2D CNN to rapidly classify higher-order photon Fock states with high accuracy, enabling near real-time quantum state analysis in quantum technologies.

Contribution

The study introduces a CNN-based classifier that efficiently identifies multiphoton Fock states up to |3> with high accuracy, reducing measurement time compared to traditional methods.

Findings

Achieved 94% overall classification accuracy.

Effective with sparse data, reaching 90% accuracy with 800 co-detection events.

Enables quasi real-time classification for quantum applications.

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.