Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize
parameterized quantum circuits which naturally express Lie groups. It has been postulated …

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 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 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 …

Jonas Landman: jonas. landman@ qcware. com we trained on these small-scale datasets
require fewer parameters compared to standard classical benchmarks. While this …

Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

Exploiting the power of quantum computation to realize superior machine learning
algorithms has been a major research focus of recent years, but the prospects of quantum …

Geometric quantum machine learning based on equivariant quantum neural networks
(EQNNs) recently appeared as a promising direction in quantum machine learning. Despite …

Quantum computers hold the promise of more efficient combinatorial optimization solvers,
which could be game-changing for a broad range of applications. However, a bottleneck for …