Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems

The dynamical behavior 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.This study was financially supported by the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711), CSIC extension to QuaResC (PID2019-109094GB), and CSIC Quantum Technologies PTI-001. The work of RMP and MCS has been supported by MICINN, AEI, FEDER and the University of the Balearic Islands through a predoctoral fellowship (MDM-2017-0711-18-1), and a “Ramon y Cajal” Fellowship (RYC-2015-18140), respectively. GLG acknowledges funding from the CAIB postdoctoral program.Peer reviewe