Identifying nonclassicality from experimental data using artificial neural networks
Authors: Gebhart V.; Bohmann M.; Weiher K.; Biagi N.; Zavatta A.; Bellini M.; Agudelo E.
Autors Affiliation: QSTAR, INO-CNR, LENS, Largo Enrico Fermi 2, Firenze, I-50125, Italy; Universita Degli Studi di Napoli Federico II, Via Cinthia 21, Napoli, I-80126, Italy; Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna, 1090, Austria; Institut f
Abstract: The fast and accessible verification of nonclassical resources is an indispensable step toward 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.
Journal/Review: PHYSICAL REVIEW RESEARCH
Volume: 3 (2) Pages from: 023229-1 to: 023229-9
More Information: The authors thank B. Hage for kindly providing the experimental quadrature data for the squeezed states. E.A. acknowledges funding from the European Union´s (EU´s) Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie IF (InDiQE-EU Project No. 845486). N.B., M.B., and A.Z. acknowledge funding from thTe EU under the ERA-NET QuantERA project “ShoQC” and the FET Flagship on Quantum Technologies project “Qombs” (Grant No. 820419).KeyWords: quntum-state; homodyne tomography; coherent states; squeezed states; statisticsDOI: 10.1103/PhysRevResearch.3.023229