Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

The expressive capacity of physical systems employed for learning is limited by the
unavoidable presence of noise in their extracted outputs. Though present in physical …

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

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) is a highly promising computational paradigm that
leverages quantum systems as a computational resource for nonlinear information …

The practical implementation of many quantum algorithms known today is limited by the
coherence time of the executing quantum hardware and quantum sampling noise. Here we …

Quantum reservoir computing is an emerging field in machine learning with quantum
systems. While classical reservoir computing has proven to be a capable concept for …

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to
efficiently process information encoded in input quantum states, avoiding the high …

We study the performance of an Ising spin network for quantum reservoir computing in linear
and nonlinear memory tasks. We investigate the extent to which quantumness enhances …

Squeezing is known to be a quantum resource in many applications in metrology,
cryptography, and computing, being related to entanglement in multimode settings. In this …