Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Issue 8 (29th June 2021)

• Record Type: Journal Article
• Authors: Mujal, Pere; Martínez‐Peña, Rodrigo; Nokkala, Johannes; García‐Beni, Jorge; Giorgi, Gian Luca; Soriano, Miguel C.; Zambrini, Roberta
• Is Part Of: Advanced quantum technologies. Volume 4:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-29
• Subjects: extreme learning machines -- information processing -- neural networks -- noisy intermediate‐scale quantum (NISQ) -- quantum machine learning -- reservoir computing -- unconventional computing; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202100027
• Languages: English
• ISSNs: 2511-9044

Abstract

Abstract: 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. Abstract : Quantum reservoir computing and extreme learning machines are two related emerging fields of research with a great potential, resulting from merging a classical machine‐learning approach with the quantum world. Both rely on exploiting quantum systems as physical substrates for information processing. Recent advances of these unconventional computing methods are broadly analyzed to identify present challenges and envision future developments.