  The rapidity and low power consumption of superconducting electronics makes
them an ideal substrate for physical reservoir computing, which commandeers the
computational power inherent to the evolution of a dynamical system for the
purposes of performing machine learning tasks. We focus on a subset of
superconducting circuits that exhibit soliton-like dynamics in simple
transmission line geometries. With numerical simulations we demonstrate the
effectiveness of these circuits in performing higher-order parity calculations
and channel equalization at rates approaching 100 Gb/s. The availability of a
proven superconducting logic scheme considerably simplifies the path to a fully
integrated reservoir computing platform and makes superconducting reservoirs an
enticing substrate for high rate signal processing applications.
