Identifying nonclassicality from experimental data using artificial neural networks

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.

Experimental Quantum Embedding for Machine Learning

“…This comes in the framework of an exchange of concepts and methods between machine learning and quantum information. [11] Indeed, there have been demonstrations of the benefits of machine learning approaches to analyze data generated by quantum experiments, [12][13][14][15][16][17][18][19] to improve the performance of quantum sensors, [20,21] to Bayesian parameter estimations, [22] to the classification of non-Markovian noise, [23] and to the design of optical experiments. [24,25] Small gate-model devices and quantum annealers have been used to perform quantum heuristic optimization [26][27][28][29][30] and to solve classification problems.…”

Experimental Quantum Embedding for Machine Learning

“…This comes in the framework of an exchange of concepts and methods between machine learning and quantum information [11]. Indeed, there have been demonstrations of the benefits of machine learning approaches to analyse data generated by quantum experiments [12][13][14][15][16][17][18][19], to improve the performance of quantum sensors [20,21], to Bayesian parameter estimations [22], to the classification of non-Markovian noise [23], and to the design of optical experiments [24,25]. Small gate-model devices and quantum annealers have been used to perform quantum heuristic optimization [26][27][28][29][30] and to solve classification problems [31][32][33][34].…”