Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

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

The use of artificial neural networks to represent quantum wave functions has recently
attracted interest as a way to solve complex many-body problems. The potential of these …

In the last decade, quantum simulators, and in particular cold atoms in optical lattices, have
emerged as a valuable tool to study strongly correlated quantum matter. These experiments …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

Altermagnetism represents a type of collinear magnetism, that is in some aspects distinct
from ferromagnetism and from conventional antiferromagnetism. In contrast to the latter …

Understanding strongly correlated quantum many-body states is one of the most difficult
challenges in modern physics. For example, there remain fundamental open questions on …

High harmonic generation (HHG) has developed in recent years as a promising tool for
ultrafast materials spectroscopy. At the forefront of these advancements, several works …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

We demonstrate quantum many-body state reconstruction from experimental data generated
by a programmable quantum simulator by means of a neural-network model incorporating …