Revealing Nonclassicality of Multiphoton Optical Beams via Artificial Neural Networks

TL;DR

This paper demonstrates that artificial neural networks can identify and characterize the nonclassical features of multiphoton quantum states even when these features are obscured by experimental noise and device imperfections.

Contribution

It introduces a neural network-based method for detecting nonclassicality in multiphoton states under realistic noisy conditions, advancing quantum state analysis.

Findings

Neural networks successfully identify nonclassicality despite noise.

The method works with realistic experimental data.

It enables AI-assisted quantum state characterization.

Abstract

The identification of nonclassical features of multiphoton quantum states represents a task of the utmost importance in the development of many quantum photonic technologies. Under realistic experimental conditions, a photonic quantum state gets affected by its interaction with several nonideal opto-electronic devices, including those used to guide, detect or characterize it. The result of such noisy interaction is that the nonclassical features of the original quantum state get considerably reduced or are completely absent in the detected, final state. In this work, the self-learning features of artificial neural networks are exploited to experimentally show that the nonclassicality of multiphoton quantum states can be assessed and fully characterized, even in the cases in which the nonclassical features are concealed by the measuring devices. Our work paves the way toward artificial-intelligence-assisted experimental-setup characterization, as well as smart quantum-state nonclassicality identification.