Dynamical phase transitions in quantum reservoir computing

TL;DR

This paper explores how different dynamical phases in quantum systems, especially the thermal phase, influence the effectiveness of quantum reservoir computing, revealing enhanced performance at the thermalization transition.

Contribution

It demonstrates that the thermal phase in quantum systems is particularly suited for quantum reservoir computing, linking physical dynamical regimes to computational performance.

Findings

Enhanced computational performance at the thermalization transition

Thermal phase is naturally suited for quantum reservoir computing

Provides insights into physical mechanisms behind optimal information processing

Abstract

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 for the studied tasks. 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.