Multi-mode fiber reservoir computing overcomes shallow neural networks classifiers. (arXiv:2210.04745v1 [physics.optics])

In disordered photonics, one typically tries to characterize the optically
opaque material in order to be able to deliver light or perform imaging through
it. Among others, multi-mode optical fibers are extensively studied because
they are cheap and easy-to-handle complex devices. Here, instead, we use the
reservoir computing paradigm to turn these optical tools into random projectors
capable of introducing a sufficient amount of interaction to perform non-linear
classification. We show that training a single logistic regression layer on the …

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