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Perturbations in complex media, due to their own dynamical evolution or toexternal effects, are often seen as detrimental. Therefore, a common strategy,especially for telecommunication and imaging applications, is to limit thesensitivity to those perturbations in order to avoid them. Here, we insteadconsider crashing straight into them in order to maximize the interactionbetween light and the perturbations and thus produce the largest change inoutput intensity. Our work hinges on the innovative use of tensor-basedtechniques, presently at the forefront of machine learning explorations, tostudy intensity-based measurements where its quadratic relationship to thefield prevents the use of standard matrix methods. With this tensor-basedframework, we are able to identify the optimal crashing channel which maximizesthe change in its output intensity distribution and the Fisher informationencoded in it about a given perturbation. We further demonstrate experimentallyits superiority for robust and precise sensing applications. Additionally, wederive the appropriate strategy to reach the precision limit forintensity-based measurements leading to an increase in Fisher information bymore than four orders of magnitude with respect to the mean for randomwavefronts when measured with the pixels of a camera.

Crashing with disorder: Reaching the precision limit with tensor-based wavefront shaping