Symmetry-Aware Reservoir Computing

We match the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increasing its processing power. We apply our method to the parity task, a challenging benchmark problem, and to a chaotic system inference task. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced artificial neurons and training data, greatly speeding up the time-to-result and outperforming hand crafted artificial neural networks (ANN). For the inference task, the performance is orders-of-magnitude better than regular RCs. We anticipate that generalizations of our procedure will have widespread applicability in information processing with ANNs.