Identifying nonclassicality from experimental data using artificial neural networks

TL;DR

This paper demonstrates that artificial neural networks can effectively identify nonclassical states of light from experimental quadrature data, offering a fast and sample-efficient alternative to traditional quantum state verification methods.

Contribution

The authors develop and validate a neural network-based method for classifying nonclassicality directly from experimental data, reducing the need for large sample sizes and complex tomography.

Findings

Neural network accurately classifies classical vs. nonclassical states.

Method works on experimental data, not just simulations.

Effective with small sample sizes, enabling rapid analysis.

Abstract

The fast and accessible verification of nonclassical resources is an indispensable step towards a broad utilization of continuous-variable quantum technologies. Here, we use machine learning methods for the identification of nonclassicality of quantum states of light by processing experimental data obtained via homodyne detection. For this purpose, we train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We demonstrate that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light. Furthermore, we show that nonclassicality of some states that were not used in the training phase is also recognized. Circumventing the requirement of the large sample sizes needed to perform homodyne tomography, our approach presents a promising alternative for the identification of nonclassicality for small sample sizes, indicating applicability for fast sorting or direct monitoring of experimental data.