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 …

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 Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …

Quantum error mitigation has been proposed as a means to combat unwanted and
unavoidable errors in near-term quantum computing without the heavy resource overheads …

The detection of out-of-distribution data points is a common task in particle physics. It is used
for monitoring complex particle detectors or for identifying rare and unexpected events that …

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

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 generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …

A new paradigm for data science has emerged, with quantum data, quantum models, and
quantum computational devices. This field, called quantum machine learning (QML), aims to …

Quantum computers are known to provide speedups over classical state-of-the-art machine
learning methods in some specialized settings. For example, quantum kernel methods have …