Super-Resolution Quantum Imaging by Adaptively Maximizing Single Photon Fisher Information

Abstract

Resolving point sources below the Rayleigh limit in optical imaging presents a remarkable challenge. Although quantum-inspired imaging methods offer potential solutions, they often suffer from limitations such as dependency on prior information, noise sensitivity, and poor scalability. To overcome these limitations, we introduce super-resolution quantum imaging (SRQI), which leverages multiparameter quantum Fisher information maximization with analytical probability optimization. SRQI demonstrates approximately 100-fold higher estimation accuracy than prior approaches, achieves infinitesimal resolution for unequal brightness sources under extremely low separation conditions, and scales to localizing 10 sources within a sub-Rayleigh region. These results identify SRQI as a precise and scalable solution for super-resolution imaging without relying on prior information, thereby representing a critical advancement in quantum imaging.