Quantum pattern recognition in photonic circuits

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.

Automated summary

This paper proposes a machine learning method that characterizes photonic states using a simple optical circuit and data processing of photon number distributions. The trained supervised learning algorithms can predict the degree of entanglement in the photonic states and perform full tomography of photon modes.