Quantum limits of imaging: Making the invisible visible

For more than a century, the diffraction limit has defined the resolution achievable by passive optical imaging systems. Although some resolution improvement can be gained through classical data processing of the image, it is limited by the noise arising from the quantum nature of light. Counteracting the effect of this noise requires a quantum treatment of optical imaging. By reformulating imaging as a problem of quantum measurement and estimation, it becomes possible to identify optimal detection strategies that recover spatial information previously thought inaccessible.

In this talk, I will present recent progress towards a comprehensive quantum statistical framework for three-dimensional passive optical imaging and surface metrology. Modelling sources and surfaces as incoherent ensembles of point emitters, we establish the ultimate quantum limits for estimating and discriminating sub-diffraction features. We highlight key applications of this framework to extract characteristic features such as surface roughness, and to identify and characterise structural features such as surface defects. In all these case studies, we derive the ultimate quantum precision limits and provide measurement procedures able to approach them in the sub-diffraction regime. Ultimately, we show that by passively probing a limited number of spatial modes, we can achieve optimal surface inspection without requiring any complex illumination control.