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

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.

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• Classification and reconstruction of optical quantum states with deep neural networks
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• Quantum reservoir computing for photonic entanglement witnessing
• Experimental retrieval of photon statistics from click detection
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• Quantum process matrices as images: new tools to design novel denoising methods