  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 perspective article, we overview recent
proposals and first experiments displaying a broad range of possibilities 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.
