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 …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

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

Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series
of concurrent, partially overlap** processes, as successfully metastasizing cells assume …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

We develop a machine learning method to construct accurate ground-state wave functions
of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A …

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 …

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 …

We present a general variational approach to determine the steady state of open quantum
lattice systems via a neural-network approach. The steady-state density matrix of the lattice …