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 …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …

Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …

We investigate quantum circuits for graph representation learning, and propose equivariant
quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong …

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum
(NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the …

Abstract Machine learning has shown significant breakthroughs in quantum science, where
in particular deep neural networks exhibited remarkable power in modeling quantum many …

Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks
(NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of …

Physical unclonable functions (PUFs) have been proposed as a way to identify and
authenticate electronic devices. Recently, several ideas have been presented to that aim to …

Exploiting the utility of near-term quantum devices is a long-standing challenge whereas
hybrid quantum machine learning emerges as a promising candidate. Here we propose a …