A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
Tekijät: Mujal Pere, Martinez-Peña Rodrigo, Nokkala Johannes, Garcia-Beni Jorge, Giorgi Gian Luca, Soriano Miguel C., Zambrini Roberta
Kustantaja: WILEY
Julkaisuvuosi: 2021
Journal: Advanced Quantum Technologies
Tietokannassa oleva lehden nimi: ADVANCED QUANTUM TECHNOLOGIES
Lehden akronyymi: ADV QUANTUM TECHNOL
Artikkelin numero: ARTN 2100027
Vuosikerta: 4
Numero: 8
Sivujen määrä: 14
eISSN: 2511-9044
DOI: https://doi.org/10.1002/qute.202100027
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
