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 …

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 (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

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 …

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

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

Variational quantum computing schemes have received considerable attention due to their
high versatility and potential to make practical use of near-term quantum devices. At their …

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 …