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

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …

Quantum machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …

Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

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

Generalization bounds are a critical tool to assess the training data requirements of
Quantum Machine Learning (QML). Recent work has established guarantees for in …

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 …