At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained
significant attention as a potential candidate for achieving a quantum advantage in data …

Kernel methods in Quantum Machine Learning (QML) have recently gained significant
attention as a potential candidate for achieving a quantum advantage in data analysis …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

It has been hypothesized that quantum computers may lend themselves well to applications
in machine learning. In the present work, we analyze function classes defined via quantum …

Quantum kernel methods show promise for accelerating data analysis by efficiently learning
relationships between input data points that have been encoded into an exponentially large …

A new paradigm for data science has emerged, with quantum data, quantum models, and
quantum computational devices. This field, called quantum machine learning (QML), aims to …

Quantum computers are known to provide speedups over classical state-of-the-art machine
learning methods in some specialized settings. For example, quantum kernel methods have …

The functional characterization of different neuronal types has been a longstanding and
crucial challenge. With the advent of physical quantum computers, it has become possible to …

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 …