Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …

Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

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 …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

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 …

Abstract Quantum Boltzmann machines (QBMs) are machine-learning models for both
classical and quantum data. We give an operational definition of QBM learning in terms of …

Generalization is the ability of machine learning models to make accurate predictions on
new data by learning from training data. However, understanding generalization of quantum …

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially
with system size, poses a challenge to scaling variational quantum algorithms. Here we …

Machine learning techniques are essential tools to compute efficient, yet accurate, force
fields for atomistic simulations. This approach has recently been extended to incorporate …

Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for
quantum machine learning. Their appeal lies in the rich feature map induced by the …