부산대학교 도서관

Perturbations in complex media, due to their own dynamical evolution or to external effects, are often seen as detrimental. Therefore, a common strategy, especially for telecommunication and imaging applications, is to limit the sensitivity to those perturbations in order to avoid them. Here, we instead consider crashing straight into them in order to maximize the interaction between light and the perturbations and thus produce the largest change in output intensity. Our work hinges on the innovative use of tensor-based techniques, presently at the forefront of machine learning explorations, to study intensity-based measurements where its quadratic relationship to the field prevents the use of standard matrix methods. With this tensor-based framework, we are able to identify the optimal crashing channel which maximizes the change in its output intensity distribution and the Fisher information encoded in it about a given perturbation. We further demonstrate experimentally its superiority for robust and precise sensing applications. Additionally, we derive the appropriate strategy to reach the precision limit for intensity-based measurements leading to an increase in Fisher information by more than four orders of magnitude with respect to the mean for random wavefronts when measured with the pixels of a camera.