Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Computing the ground state of interacting quantum matter is a long-standing challenge,
especially for complex two-dimensional systems. Recent developments have highlighted the …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

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 …

Neural-network architectures have been increasingly used to represent quantum many-body
wave functions. These networks require a large number of variational parameters and are …

The continued development of computational approaches to many-body ground-state
problems in physics and chemistry calls for a consistent way to assess its overall progress …

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

We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …

We show that neural quantum states based on very deep (4–16-layered) neural networks
can outperform state-of-the-art variational approaches on highly frustrated quantum …

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to
simulate the dynamics of many-body quantum systems classically. By systematically …