  Reservoir computing is a relatively recent computational paradigm that
originates from a recurrent neural network and is known for its wide range of
implementations using different physical technologies. Large reservoirs are
very hard to obtain in conventional computers, as both the computation
complexity and memory usage grow quadratically. We propose an optical scheme
performing reservoir computing over very large networks potentially being able
to host several millions of fully connected photonic nodes thanks to its
intrinsic properties of parallelism and scalability. Our experimental studies
confirm that, in contrast to conventional computers, the computation time of
our optical scheme is only linearly dependent on the number of photonic nodes
of the network, which is due to electronic overheads, while the optical part of
computation remains fully parallel and independent of the reservoir size. To
demonstrate the scalability of our optical scheme, we perform for the first
time predictions on large spatiotemporal chaotic datasets obtained from the
Kuramoto-Sivashinsky equation using optical reservoirs with up to 50 000
optical nodes. Our results are extremely challenging for conventional von
Neumann machines, and they significantly advance the state of the art of
unconventional reservoir computing approaches, in general.

Optics;Emerging Technologies