  The identification of nonclassical features of multiphoton quantum states
represents a task of the utmost importance in the development of many quantum
photonic technologies. Under realistic experimental conditions, a photonic
quantum state gets affected by its interaction with several nonideal
opto-electronic devices, including those used to guide, detect or characterize
it. The result of such noisy interaction is that the nonclassical features of
the original quantum state get considerably reduced or are completely absent in
the detected, final state. In this work, the self-learning features of
artificial neural networks are exploited to experimentally show that the
nonclassicality of multiphoton quantum states can be assessed and fully
characterized, even in the cases in which the nonclassical features are
concealed by the measuring devices. Our work paves the way toward
artificial-intelligence-assisted experimental-setup characterization, as well
as smart quantum-state nonclassicality identification.
