Opportunities in Quantum Reservoir Computing and Extreme Learning Machines



: Mujal Pere, Martinez-Peña Rodrigo, Nokkala Johannes, Garcia-Beni Jorge, Giorgi Gian Luca, Soriano Miguel C., Zambrini Roberta

Publisher: WILEY

: 2021

: Advanced Quantum Technologies

: ADVANCED QUANTUM TECHNOLOGIES

: ADV QUANTUM TECHNOL

: ARTN 2100027

: 4

: 8

: 14

: 2511-9044

DOI: https://doi.org/10.1002/qute.202100027

: http://research.utu.fi/converis/portal/detail/Publication/66528233


Quantum reservoir computing and quantum extreme learning machines 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.

Last updated on 2024-26-11 at 22:05