  Retrieving images transmitted through multi-mode fibers is of growing
interest, thanks to their ability to confine and transport light efficiently in
a compact system. Here, we demonstrate machine-learning-based decoding of
large-scale digital images (pages), maximizing page capacity for optical
storage applications. Using a millimeter-sized square cross-section waveguide,
we image an 8-bit spatial light modulator, presenting data as a matrix of
symbols. Normally, decoders will incur a prohibitive O(n^2) computational
scaling to decode n symbols in spatially scrambled data. However, by combining
a digital twin of the setup with a U-Net, we can retrieve up to 66 kB using
efficient convolutional operations only. We compare trainable ray-tracing-based
with eigenmode-based twins and show the former to be superior thanks to its
ability to overcome the simulation-to-experiment gap by adjusting to optical
imperfections. We train the pipeline end-to-end using a differentiable
mutual-information estimator based on the von-Mises distribution, generally
applicable to phase-coding channels.
