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 …

Dissipation induced by interactions with an external environment typically hinders the
performance of quantum computation, but in some cases can be turned out as a useful …

Quantum reservoir computing is a neuro‐inspired machine learning approach harnessing
the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for …

The nascent computational paradigm of quantum reservoir computing presents an attractive
use of near-term, noisy-intermediate-scale quantum processors. To understand the potential …

Quantum reservoir processing offers an option to perform quantum tomography of input
objects by postprocessing quantities, obtained from local measurements, from a quantum …

We study the performance of an Ising spin network for quantum reservoir computing (QRC)
in linear and non-linear memory tasks. We investigate the extent to which quantumness …

Quantum reservoir computing is a machine learning approach designed to exploit the
dynamics of quantum systems with memory to process information. As an advantage, it …

The emerging technology of quantum neural networks (QNNs) offers a quantum advantage
over classical artificial neural networks (ANNs) in terms of speed or efficiency of information …

Interest in quantum machine learning is increasingly growing due to the possibility of
developing efficient solutions to problems that are difficult to tackle with classical methods. In …