Recent advancements in quantum computing have positioned it as a prospective solution for
tackling intricate computational challenges, with supervised learning emerging as a …

Classical learning of the expectation values of observables for quantum states is a natural
variant of learning quantum states or channels. While learning-theoretic frameworks …

Quantum kernel methods are promising candidates for achieving a practical quantum
advantage for certain machine learning tasks. Similar to classical machine learning, an …

Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the
unique properties of quantum mechanics to enhance classical machine learning. However …

Quantum machine learning models use encoding circuits to map data into a quantum Hilbert
space. While it is well known that the architecture of these circuits significantly influences …

The double descent phenomenon challenges traditional statistical learning theory by
revealing scenarios where larger models do not necessarily lead to reduced performance …

The quest for successful variational quantum machine learning (QML) relies on the design of
suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical …

Whether in fundamental physics, cybersecurity or finance, the detection of anomalies with
machine learning techniques is a highly relevant and active field of research, as it potentially …

Quantum Machine Learning (QML) is considered one of the most promising applications of
Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is …

We present spectral and information-theoretic characterizations of learning tasks involving
quantum systems, and develop new perturbative error mitigation techniques for near-term …