Does quantum mechanics have anything useful to contribute to technology development in high-precision classical sensing? For quantitative sensing tasks in many application spaces, modeling the light collected by an optical sensor as a quantum state is a powerful theoretical avenue for evaluating how precisely the task could be carried out if the sensor is allowed to make any measurements that obey the laws of physics. Focusing on far-field super-resolution imaging, I will discuss how such a quantum information-theoretic framework has led us at the University of Arizona and others to develop "quantum-inspired" techniques that provably approach or achieve the upper limits on imaging precision that nature allows (such as the quantum Cramer-Rao bound on parameter estimation or the quantum Chernoff bound on object discrimination) while yielding large fundamental improvements over conventional resolution limits. I will highlight our theoretical and experimental progress in pushing these insights beyond toy problems and into the realm of useful real-world imaging needs, with applications in super-resolution microscopy, astronomy, and remote sensing.
Michael R Grace is a graduating PhD student working with Professor Saikat Guha at the University of Arizona College of Optical Sciences. He has a background in classical and quantum physics and diffractive optics as well as experience in experimental super-resolution microscopy. His current research interests include optical sensing, quantum information theory, and optical machine learning.