Experimental visual quantum state discrimination

arXiv:2611.10282v1 Announce Type: new Abstract: Almost all solutions to vision tasks $\unicode{x2014}$ offered by nature and technology alike $\unicode{x2014}$ rely on capturing an image using a lens or a similar optical instrument, followed by computational analysis by neurons of a brain or a digital computer. This approach is inherently suboptimal in photon-limited and noisy conditions, where critical information is irreversibly lost at the measurement stage. We address this gap by introducing AI-driven optimization of quantum measurements on the incoming photons, learning how to extract the maximum possible information for a given computer vision task from each incoming photon. The trained measurement consists of a diffractive optical neural network (DONN) followed by a detector array.