Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …

Classical optimization of parameterized quantum circuits is a widely studied methodology for
the preparation of complex quantum states, as well as the solution of machine learning and …

Classical neural networks with random initialization famously behave as Gaussian
processes in the limit of many neurons, which allows one to completely characterize their …

A common trait of many machine learning models is that it is often difficult to understand and
explain what caused the model to produce the given output. While the explainability of …

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

Quantum machine learning (QML) has surged as a prominent area of research with the
objective to go beyond the capabilities of classical machine learning models. A critical …

Quantum computing holds the unparalleled potentials to enhance, speed up or innovate
machine learning. However, an unambiguous demonstration of quantum learning …

Quantum machine learning for classical data is currently perceived to have a scalability
problem due to (i) a bottleneck at the point of loading data into quantum states,(ii) the lack of …