Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems

TL;DR

This paper evaluates the information processing capacity of spin-based quantum reservoir computing systems, analyzing how input and measurement choices affect their computational capabilities, and establishing a foundation for future quantum reservoir computing research.

Contribution

It introduces a quantitative characterization of quantum reservoir computing with spin networks using the Information Processing Capacity metric, highlighting optimal conditions and measurement strategies.

Findings

Optimal input driving conditions identified

Measurement choices significantly impact computational capacity

Quantum spin networks show promising reservoir computing potential

Abstract

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.