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 …

Barren plateaus have emerged as a pivotal challenge for variational quantum computing.
Our understanding of this phenomenon underwent a transformative shift with the recent …

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

A basic primitive in quantum information is the computation of the moments EU [Tr [U ρ U†
O] t]. These describe the distribution of expectation values obtained by sending a state ρ …

The quest for successful variational quantum machine learning (QML) relies on the design of
suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical …

Parametrized and random unitary (or orthogonal) $ n $-qubit circuits play a central role in
quantum information. As such, one could naturally assume that circuits implementing …

Quantum machine learning requires powerful, flexible and efficiently trainable models to be
successful in solving challenging problems. In this work, we present density quantum neural …

In this work, quantum transformers are designed and analysed in detail by extending the
state-of-the-art classical transformer neural network architectures known to be very …