  Photonic reservoir computing is a promising candidate for low-energy
computing at high bandwidths. Despite recent successes, there are bounds to
what one can achieve simply by making photonic reservoirs larger. Therefore, a
switch from single-reservoir computing to multi-reservoir and even deep
physical reservoir computing is desirable. Given that backpropagation can not
be used directly to train multi-reservoir systems in our targeted setting, we
propose an alternative approach that still uses its power to derive
intermediate targets. In this work we report our findings on a conducted
experiment to evaluate the general feasibility of our approach by training a
network of 3 Echo State Networks to perform the well-known NARMA-10 task using
targets derived through backpropagation. Our results indicate that our proposed
method is well-suited to train multi-reservoir systems in a efficient way.
