Smart Quantum Statistical Imaging beyond the Abbe-Rayleigh Criterion

TL;DR

This paper introduces a smart quantum imaging system that uses artificial intelligence and quantum models to surpass traditional resolution limits, enabling superresolution imaging without prior knowledge of light coherence.

Contribution

The work presents a novel quantum camera that leverages AI to identify statistical fluctuations of unknown light sources, overcoming limitations of existing superresolution methods.

Findings

Achieves superresolution beyond the Abbe-Rayleigh limit.

Does not require prior coherence information of light sources.

Applicable to microscopy, remote sensing, and astronomy.

Abstract

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.