  The dynamical behaviour of complex quantum systems can be harnessed for
information processing. With this aim, quantum reservoir computing (QRC) with
Ising spin networks was recently introduced as a quantum version of classical
reservoir computing. In turn, reservoir computing is a neuro-inspired machine
learning technique that consists in exploiting dynamical systems to solve
nonlinear and temporal tasks. We characterize the performance of the spin-based
QRC model with the Information Processing Capacity (IPC), which allows to
quantify the computational capabilities of a dynamical system beyond specific
tasks. The influence on the IPC of the input injection frequency, time
multiplexing, and different measured observables encompassing local spin
measurements as well as correlations, is addressed. We find conditions for an
optimum input driving and provide different alternatives for the choice of the
output variables used for the readout. This work establishes a clear picture of
the computational capabilities of a quantum network of spins for reservoir
computing. Our results pave the way to future research on QRC both from the
theoretical and experimental points of view.
