This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.
@article{arxiv.2107.09961,
title = {Quantum Pattern Recognition in Photonic Circuits},
author = {Rui Wang and Carlos Hernani-Morales and José D. Martín-Guerrero and Enrique Solano and Francisco Albarrán-Arriagada},
journal= {arXiv preprint arXiv:2107.09961},
year = {2021}
}