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 …

We consider the preparation of matrix product states (MPS) on quantum devices via
quantum circuits of local gates. We first prove that faithfully preparing translation-invariant …

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 …

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

Tensor networks (TNs) are a family of computational methods built on graph-structured
factorizations of large tensors, which have long represented state-of-the-art methods for the …

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 …

We report cutting edge performance results on a single node hybrid CPU-multi-GPU
implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) …

Classical simulation of quantum dynamics plays an important role in our understanding of
quantum complexity, and in the development of quantum technologies. Compared to other …