arXiv:2608. 10382v1 Announce Type: cross Abstract: Universal approximation in reservoir computing is typically associated with a class of reservoirs.
By Peter J. Ehlers, Phi Hung Nguyen, Kanu Sinha, Noelle Daigle, Travis W. Sawyer, Hendra I. Nurdin, Daniel Soh
arXiv:2607. 18552v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout.
By Shehbaz Tariq, Muhammad Talha, Arshid Ali, Muhammad Diyan, Symeon Chatzinotas
arXiv:2605. 12713v3 Announce Type: replace-cross Abstract: In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed.
By Erik L. Connerty, Ethan N. Evans
arXiv:2501. 08640v2 Announce Type: replace Abstract: We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity.
By Naomi Mona Chmielewski (L2S), Nina Amini (L2S, CNRS), Joseph Mikael
arXiv:2607. 09905v1 Announce Type: cross Abstract: Can a small quantum computer forecast a changing signal better than an ordinary classical method?
By Tushar Pandey
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
By Chi-Sheng Chen, En-Jui Kuo