A2 Refereed review article in a scientific journal
Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
Authors: Mujal Pere, Martinez-Peña Rodrigo, Nokkala Johannes, Garcia-Beni Jorge, Giorgi Gian Luca, Soriano Miguel C., Zambrini Roberta
Publisher: WILEY
Publication year: 2021
Journal: Advanced Quantum Technologies
Journal name in source: ADVANCED QUANTUM TECHNOLOGIES
Journal acronym: ADV QUANTUM TECHNOL
Article number: ARTN 2100027
Volume: 4
Issue: 8
Number of pages: 14
eISSN: 2511-9044
DOI: https://doi.org/10.1002/qute.202100027
Self-archived copy’s web address: 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.
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