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 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 …

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

Quantum reservoir computing (QRC) exploits the dynamical properties of quantum systems
to perform machine learning tasks. We demonstrate that optimal performance in QRC can be …

We demonstrate a novel approach to reservoir computer measurements using random
matrices. We do so to motivate how atomic-scale devices might be used for real-world …

Reservoir computing (RC) is an effective method for predicting chaotic systems by using a
high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning …

Quantum machine learning promises to deliver near-term practical quantum computation
applications using machine learning tools to optimize quantum hardware for scientific …

This research investigates transport regimes in metal-insulator Anderson transition through
Hamiltonian learning. Quantum reservoir computing is employed to estimate the …