Quantum Reservoir Computing with Spin Networks

The complex dynamical behavior of quantum systems can be harnessed for information processing. With this aim, Quantum Reservoir Computing (QRC) has been recently introduced as a neuro-inspired computing paradigm. Reservoir computing is a known machine learning technique that exploits dynamical systems to solve nonlinear and temporal tasks. In this talk, I will introduce a possible extension of reservoir computing into the quantum regime and show some preliminary results about the characterization of the performance of spin networks as quantum reservoir computers. This characterization will be provided by the Information Processing Capacity (IPC) of the dynamical system, which shows the contributions of the linear and nonlinear memory of the system and the trade-off between them. Finally, I will comment some aspects about the role of quantum coherences.