Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

TL;DR

This review explores the emerging field of quantum reservoir computing and quantum extreme learning machines, highlighting their potential, recent advances, and advantages over classical methods in quantum machine learning tasks.

Contribution

It provides a comprehensive overview of recent proposals, experiments, and opportunities in quantum reservoir computing and quantum extreme learning machines.

Findings

Quantum approaches show promising performance in machine learning tasks.

Quantum platforms enable diverse implementations of QRC and QELM.

Quantum methods offer advantages over classical counterparts.

Abstract

Quantum reservoir computing (QRC) and quantum extreme learning machines (QELM) are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities.