Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

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

Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

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 …

The superiority of variational quantum algorithms (VQAs) such as quantum neural networks
(QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity …

Learning about physical systems from quantum-enhanced experiments can outperform
learning from experiments in which only classical memory and processing are available …

Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …