  Machine learning software applications are nowadays ubiquitous in many fields
of science and society for their outstanding capability of solving
computationally vast problems like the recognition of patterns and regularities
in big datasets. One of the main goals of research is the realization of a
physical neural network able to perform data processing in a much faster and
energy-efficient way than the state-of-the-art technology. Here we show that
lattices of exciton-polariton condensates accomplish neuromorphic computing
using fast optical nonlinearities and with lower error rate than any previous
hardware implementation. We demonstrate that our neural network significantly
increases the recognition efficiency compared to the linear classification
algorithms on one of the most widely used benchmarks, the MNIST problem,
showing a concrete advantage from the integration of optical systems in
reservoir computing architectures.
