Quantum Pattern Recognition in Photonic Circuits

TL;DR

This paper introduces a machine learning approach using photonic circuits to analyze quantum states, enabling entanglement measurement and state tomography with high accuracy.

Contribution

It presents a novel method combining optical circuits and supervised learning for quantum state characterization, including entanglement detection and tomography.

Findings

Successfully predicted entanglement degree

Performed full photonic mode tomography

Achieved satisfactory regression metrics

Abstract

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.