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

Recent advances in classical machine learning have shown that creating models with
inductive biases encoding the symmetries of a problem can greatly improve performance …

Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

The Haar measure plays a vital role in quantum information, but its study often requires a
deep understanding of representation theory, posing a challenge for beginners. This tutorial …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

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

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 …

A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …