The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Over the past decade, topology has emerged as a major branch in broad areas of physics,
from atomic lattices to condensed matter. In particular, topology has received significant …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

The experimental realization of increasingly complex synthetic quantum systems calls for the
development of general theoretical methods to validate and fully exploit quantum resources …

Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

We analyze the zero-temperature phases of an array of neutral atoms on the kagome lattice,
interacting via laser excitation to atomic Rydberg states. Density-matrix renormalization …

Artificial neural networks (ANNs) have been widely used for industrial applications and have
played a more important role in fundamental research. Although most ANN hardware …