  Electronic computers have evolved drastically over the past years with an
ever-growing demand for improved performance. However, the transfer of
information from memory and high energy consumption have emerged as issues that
require solutions. Optical techniques are considered promising solutions to
these problems with higher speed than their electronic counterparts and with
reduced energy consumption. Here, we use the optical reservoir computing
framework we have previously described (Scalable Optical Learning Operator or
SOLO) to program the spatial-spectral output of the light after nonlinear
propagation in a multimode fiber. The novelty in the current paper is that the
system is programmed through an output sampling scheme, similar to that used in
hyperspectral imaging in astronomy. Linear and nonlinear computations are
performed by light in the multimode fiber and the high dimensional
spatial-spectral information at the fiber output is optically programmed before
it reaches the camera. We then used a digital computer to classify the
programmed output of the multi-mode fiber using a simple, single layer network.
When combining front-end programming and the proposed spatial-spectral
programming, we were able to achieve 89.9% classification accuracy on the
dataset consisting of chest X-ray images from COVID-19 patients. At the same
time, we obtained a decrease of 99% in the number of tunable parameters
compared to an equivalently performing digital neural network. These results
show that the performance of programmed SOLO is comparable with cutting-edge
electronic computing platforms, albeit with a much-reduced number of electronic
operations.
