Neural-network approach for identifying nonclassicality from click-counting data

Year: 2020

Authors: Gebhart V., Bohmann M.

Autors Affiliation: QSTAR, INO CNR, 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 Vienna, Boltzmanngasse 3, A-1090 Vienna, Austria.

Abstract: Machine-learning and neural-network approaches have gained huge attention in the context of quantum science and technology in recent years. One of the most essential tasks for the future development of quantum technologies is the verification of nonclassical resources. Here, we present an artificial neural-network approach for the identification of nonclassical states of light based on recorded measurement statistics. In particular, we implement and train a network which is capable of recognizing nonclassical states based on the click statistics recorded with multiplexed detectors. We use simulated data for training and testing the network, and we show that it is capable of identifying some nonclassical states even if they were not used in the training phase. Especially, in the case of small sample sizes, our approach can be more sensitive in identifying nonclassicality than established criteria, which suggests possible applications in presorting of experimental data and online applications.

Journal/Review: PHYSICAL REVIEW RESEARCH

Volume: 2 (2)      Pages from: 23150-1  to: 23150-9

More Information: M.B. acknowledges financial support from the Leopoldina Fellowship Programme of the German National Academy of Science (LPDS 2019-01).
KeyWords: Quantum State
DOI: 10.1103/PhysRevResearch.2.023150

Citations: 16
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