Sensing technology is an important aspect of information processing. Current development
in artificial intelligence systems (especially those aimed at medical and environmental …

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

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-
driving cars may consume up to 50% of the total power available onboard, thereby limiting …

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

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

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 …

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 a promising approach for quantum neural networks,
capable of solving hard learning tasks on both classical and quantum input data. However …

Quantum reservoir computing is strongly emerging for sequential and time series data
prediction in quantum machine learning. We make advancements to the quantum noise …

Quantum extreme learning machines (QELMs) aim to efficiently post-process the outcome of
fixed—generally uncalibrated—quantum devices to solve tasks such as the estimation of the …