Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

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 …

We introduce a class of phase transitions separating quantum states with different
entanglement features. An example of such an “entanglement phase transition” is provided …

Demonstrating quantum advantage requires experimental implementation of a
computational task that is hard to achieve using state-of-the-art classical systems. One …

We generalize the classical shadow tomography scheme to a broad class of finite-depth or
finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where …

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 present a variational renormalization group (RG) approach based on a reversible
generative model with hierarchical architecture. The model performs hierarchical change-of …

Classical shadow tomography is a powerful randomized measurement protocol for
predicting many properties of a quantum state with few measurements. Two classical …

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of
machine learning approaches in adversarial settings and developing techniques …