Quantum reservoir computing on random regular graphs

TL;DR

This paper investigates how the structure and quantum correlations in a disordered spin system on random regular graphs influence quantum reservoir computing performance, providing design guidelines for quantum learning devices.

Contribution

It introduces a new quantum reservoir computing model based on strongly interacting spins on random regular graphs and analyzes how disorder, interactions, and connectivity affect learning capabilities.

Findings

Quantum correlations enhance learning performance.

Network connectivity critically impacts quantum memory capacity.

Optimal regimes depend on disorder and interaction parameters.

Abstract

Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines the inherent dynamics of input-driven many-body quantum systems with classical learning techniques for nonlinear temporal data processing. Optimizing the QRC process and computing device is a complex task due to the dependence of many-body quantum systems to various factors. To explore this, we introduce a strongly interacting spin model on random regular graphs as the quantum component and investigate the interplay between static disorder, interactions, and graph connectivity, revealing their critical impact on quantum memory capacity and learnability accuracy. We tackle linear quantum and nonlinear classical tasks, and identify optimal learning and memory regimes through studying information localization, dynamical quantum correlations, and the many-body structure of the disordered Hamiltonian. In particular, we uncover the role of previously overlooked network connectivity and demonstrate how the presence of quantum correlations can significantly enhance the learning performance. Our findings thus provide guidelines for the optimal design of disordered analog quantum learning platforms.