  The manifestation of the wave nature of light through diffraction imposes
limits on the resolution of optical imaging. For over a century, the
Abbe-Rayleigh criterion has been utilized to assess the spatial resolution
limits of optical instruments. Recently, there has been an enormous impetus in
overcoming the Abbe-Rayleigh resolution limit by projecting target light beams
onto spatial modes. These conventional schemes for superresolution rely on a
series of spatial projective measurements to pick up phase information that is
used to boost the spatial resolution of optical systems. Unfortunately, these
schemes require a priori information regarding the coherence properties of
"unknown" light beams. Furthermore, they require stringent alignment and
centering conditions that cannot be achieved in realistic scenarios. Here, we
introduce a smart quantum camera for superresolving imaging. This camera
exploits the self-learning features of artificial intelligence to identify the
statistical fluctuations of unknown mixtures of light sources at each pixel.
This is achieved through a universal quantum model that enables the design of
artificial neural networks for the identification of quantum photon
fluctuations. Our camera overcomes the inherent limitations of existing
superresolution schemes based on spatial mode projection. Thus, our work
provides a new perspective in the field of imaging with important implications
for microscopy, remote sensing, and astronomy.
