Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental
bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential …

Quantum reservoir computing and quantum extreme learning machines are two emerging
approaches that have demonstrated their potential both in classical and quantum machine …

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

Reservoir computing is a temporal information processing system that exploits artificial or
physical dissipative dynamics to learn a dynamical system and generate the target time …

Closed quantum systems exhibit different dynamical regimes, like many-body localization or
thermalization, which determine the mechanisms of spread and processing of information …

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-
dimensional thermal convection flow with three and eight degrees of freedom by a hybrid …

Quantum reservoir computing aims at harnessing the rich dynamics of quantum systems for
machine-learning purposes. It can be used for online time series processing while having a …

Quantum computing promises to speed up machine-learning algorithms. However, noisy
intermediate-scale quantum (NISQ) devices pose engineering challenges to realizing …

Time-series processing is a major challenge in machine learning with enormous progress in
the last years in tasks such as speech recognition and chaotic series prediction. A promising …

Quantum reservoir computing (QRC) exploits the information-processing capabilities of
quantum systems to solve nontrivial temporal tasks, improving over their classical …