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 …

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 …

Clinical trials are necessary for assessing the safety and efficacy of treatments. However,
trial timelines are severely delayed with minimal success due to a multitude of factors …

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

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 …

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 …

Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …

Using a quantum processor to embed and process classical data enables the generation of
correlations between variables that are inefficient to represent through classical …