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 …

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 …

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 …

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

A major bottleneck in the development of scalable many-body quantum technologies is the
difficulty in benchmarking state preparations, which suffer from an exponential 'curse of …

Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …

Artificial neural networks have been recently introduced as a general ansatz to represent
many-body wave functions. In conjunction with variational Monte Carlo calculations, this …

Restricted Boltzmann machines in quantum physics | Nature Physics Skip to main content Thank
you for visiting nature.com. You are using a browser version with limited support for CSS. To …

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 …