Reservoir Computing on Current Quantum Processors

The Reservoir Computing (RC) paradigm enables the encoding of computation into the natural time evolution of any physical system that is sufficiently complex. Quantum circuits operated as Reservoir Computers (QRC) have recently been shown to have the potential to realize universal approximators for arbitrary functions and causal filters on input signals. In this work we develop a practical gate-based realization of a reservoir computer accounting for the role of dissipation and finite sampling via projective measurements. We focus on processing large classical datasets with current small-scale quantum hardware, enabled through an algorithm to vary input encodings and gate sets associated with reservoir evolution, as well as via the measurement of higher order moments. We apply this reservoir computing approach to nontrivial machine learning tasks such as handwritten digit recognition and dynamical signal processing, both through simulation and on IBMQ processors. By benchmarking against linear baselines, we demonstrate that the expressive power of the nonlinearity induced by the quantum hardware enables compelling performance using limited quantum resources, and without the need for error mitigation techniques.