The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that 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 photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

光的波动特性限制了光学成像系统的分辨率。一个多世纪以来，Abbe-Rayleigh 标准一直被用于评估成像仪器的空间分辨率极限。最近，人们对使用空间投影测量来提高成像系统的分辨率产生了兴趣。不幸的是，这些方案需要关于“未知”光束的相干特性的先验信息，并施加严格的对准条件。在这里，我们介绍了一种用于超分辨率成像的智能量子相机，它利用人工智能的自学习特性来识别每个像素处未知光源混合的统计波动。这是通过通用量子模型实现的，该模型能够设计用于识别光子涨落的人工神经网络。我们的协议克服了基于空间模式投影的现有超分辨率方案的限制，从而为显微镜、遥感和天文学提供了替代方法。