Quantum machine learning with parametrised quantum circuits has attracted significant
attention over the past years as an early application for the era of noisy quantum processors …

Much hope for finding new physics phenomena at microscopic scale relies on the
observations obtained from High Energy Physics experiments, like the ones performed at …

In the noisy intermediate-scale quantum era, an important goal is the conception of
implementable algorithms that exploit the rich dynamics of quantum systems and the high …

Classical machine learning has succeeded in the prediction of both classical and quantum
phases of matter. Notably, kernel methods stand out for their ability to provide interpretable …

Quantum machine learning (QML) is the spearhead of quantum computer applications. In
particular, quantum neural networks (QNN) are actively studied as the method that works …

Generative modeling is an unsupervised machine learning framework, that exhibits strong
performance in various machine learning tasks. Recently, we find several quantum versions …