Recent advances in classical machine learning have shown that creating models with
inductive biases encoding the symmetries of a problem can greatly improve performance …

At the core of quantum photonic information processing and sensing, two major building
pillars are single-photon emitters and single-photon detectors. In this review, we …

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 …

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 …

The recent proliferation of NISQ devices has made it imperative to understand their power. In
this work, we define and study the complexity class NISQ, which encapsulates problems that …

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 …

With the increasing amount of digital data generated by Arabic speakers, the need for
effective and efficient document classification techniques is more important than ever. In …

Noisy data is ubiquitous in quantum computer, greatly affecting the performance of various
algorithms. However, existing quantum support vector machine models are not equipped …

Molecular biology and biochemistry interpret microscopic processes in the living world in
terms of molecular structures and their interactions, which are quantum mechanical by their …