Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

We review the development of generative modeling techniques in machine learning for the
purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its …

Quantum master equations are an invaluable tool to model the dynamics of a plethora of
microscopic systems, ranging from quantum optics and quantum information processing to …

The state-of-the-art machine learning approaches are based on classical von Neumann
computing architectures and have been widely used in many industrial and academic …

Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence
and temporal dependency data modeling and its effectiveness has been extensively …

Neural-network quantum states have been successfully used to study a variety of lattice and
continuous-space problems. Despite a great deal of general methodological developments …

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …

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 …

We introduce a systematically improvable family of variational wave functions for the
simulation of strongly correlated fermionic systems. This family consists of Slater …

Understanding the real-time evolution of many-electron quantum systems is essential for
studying dynamical properties in condensed matter, quantum chemistry, and complex …