Identifying nonclassicality from experimental data using artificial neural networks

Year: 2021

Authors: Gebhart V.; Bohmann M.; Weiher K.; Biagi N.; Zavatta A.; Bellini M.; Agudelo E.

Autors Affiliation: INO CNR, QSTAR, Largo Enrico Fermi 2, I-50125 Florence, Italy; LENS, Largo Enrico Fermi 2, I-50125 Florence, Italy; Univ Napoli Federico II, Via Cinthia 21, I-80126 Naples, Italy; Austrian Acad Sci, Inst Quantum Opt & Quantum Informat IQOQI Vienna, Boltzmanngasse 3, A-1090 Vienna, Austria; Univ Rostock, Inst Phys, D-18051 Rostock, Germany; Ist Nazl Ott CNR INO, Lgo E Fermi 6, I-50125 Florence, Italy; Univ Firenze, LENS, I-50019 Florence, Italy; Univ Firenze, Dept Phys & Astron, I-50019 Florence, Italy.

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; statistics
DOI: 10.1103/PhysRevResearch.3.023229

Citations: 5
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