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.
@article{arxiv.2102.11831,
title = {Opportunities in Quantum Reservoir Computing and Extreme Learning Machines},
author = {Pere Mujal and Rodrigo Martínez-Peña and Johannes Nokkala and Jorge García-Beni and Gian Luca Giorgi and Miguel C. Soriano and Roberta Zambrini},
journal= {arXiv preprint arXiv:2102.11831},
year = {2021}
}