  Modern machine learning models use an ever-increasing number of parameters to
train (175 billion parameters for GPT-3) with large datasets to obtain better
performance. Bigger is better has been the norm. Optical computing has been
reawakened as a potential solution to large-scale computing through optical
accelerators that carry out linear operations while reducing electrical power.
However, to achieve efficient computing with light, creating and controlling
nonlinearity optically rather than electronically remains a challenge. This
study explores a reservoir computing (RC) approach whereby a 14 mm long
few-mode waveguide in LiNbO3 on insulator is used as a complex nonlinear
optical processor. A dataset is encoded digitally on the spectrum of a
femtosecond pulse which is then launched in the waveguide. The output spectrum
depends nonlinearly on the input. We experimentally show that a simple digital
linear classifier with 784 parameters using the output spectrum from the
waveguide as input increased the classification accuracy of several databases
compared to non-transformed data, approximately 10$\%$. In comparison, a deep
digital neural network (NN) with 40000 parameters was necessary to achieve the
same accuracy. Reducing the number of parameters by a factor of $\sim$50
illustrates that a compact optical RC approach can perform on par with a deep
digital NN.
