Quantum Reservoir Computing for Speckle-Disorder Potentials

TL;DR

This paper introduces quantum reservoir computing using a spin-based quantum reservoir to predict ground-state energies in disordered potentials, demonstrating improved performance with two-qubit correlations.

Contribution

It presents a novel application of quantum reservoir computing with spin systems to quantum energy prediction tasks, highlighting the role of correlations.

Findings

Performance improves with two-qubit correlations.

Quantum reservoir computing can effectively predict quantum energies.

The method leverages quantum resources for machine learning tasks.

Abstract

Quantum reservoir computing is a machine-learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground-state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle-disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it shows to be enhanced when two-qubit correlations are employed.