  Closed quantum systems exhibit different dynamical regimes, like Many-Body
Localization or thermalization, which determine the mechanisms of spread and
processing of information. Here we address the impact of these dynamical phases
in quantum reservoir computing, an unconventional computing paradigm recently
extended into the quantum regime that exploits dynamical systems to solve
nonlinear and temporal tasks. We establish that the thermal phase is naturally
adapted to the requirements of quantum reservoir computing and report an
increased performance at the thermalization transition. Uncovering the
underlying physical mechanisms behind optimal information processing
capabilities of spin networks is essential for future experimental
implementations and provides a new perspective on dynamical phases.
