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 …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

We extend the concept of transfer learning, widely applied in modern machine learning
algorithms, to the emerging context of hybrid neural networks composed of classical and …

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

The Landau description of phase transitions relies on the identification of a local order
parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological …

Abstract Machine-learning techniques such as artificial neural networks are currently
revolutionizing many technological areas and have also proven successful in quantum …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

The study of topological band structures is an active area of research in condensed matter
physics and beyond. Here, we combine recent progress in this field with developments in …

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 …

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …