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
The paper investigates quantum reservoir computing using highly scrambling quantum systems modeled by high‑order unitary designs. It shows that in noiseless settings, measurement readouts become exponentially concentrated as the reservoir size grows, but this concentration does not worsen with repeated iterations, while memory of early inputs decays exponentially with both reservoir size and iterations. In noisy environments, memory also decays exponentially over time for local noisy channels, and the study introduces new proof techniques for bounding concentration in temporal quantum models. Numerical results indicate that even physical reservoirs, such as an Ising model in a quantum‑chaotic phase, can exhibit exponential concentration, whereas reservoirs in a many‑body localized phase or at the edge of chaos do not.
By Weijie Xiong, Zo\"e Holmes, Armando Angrisani, Yudai Suzuki, Thiparat Chotibut, Supanut Thanasilp
arXiv:2609.36901v1 Announce Type: cross
Abstract: Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and ex...
By Abhijit Sen, Bikram Keshari Parida, Shital Chauhan, Mahima Arya, Denys I. Bondar