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

Quantum reservoir networks combine the intelligence of neural networks with the potential of
quantum computing in a single platform. This platform operates on the architecture of …

Gaussian states have played an important role in the physics of continuous-variable
quantum systems. They are appealing for the experimental ease with which they can be …

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 …

Abstract Quantum Reservoir Computing (QRC) offers potential advantages over classical
reservoir computing, including inherent processing of quantum inputs and a vast Hilbert …

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 …

Quantum reservoir computing (QRC) is a highly promising computational paradigm that
leverages quantum systems as a computational resource for nonlinear information …