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 …

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 …

Neural quantum states (NQSs) have emerged as a novel promising numerical method to
solve the quantum many-body problem. However, it has remained a central challenge to …

Abstract Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and
Florence, are trained on large private datasets of image-caption pairs and achieve superior …

The quantum many-body problem is ultimately a curse of dimensionality: the state of a
system with many particles is determined by a function with many dimensions, which rapidly …

With the introduction of x-ray free electron laser sources around the world, new scientific
approaches for visualizing matter at fundamental length and time-scales have become …

Minimally entangled typical thermal states are a construction that allows one to solve for the
imaginary time evolution of quantum many-body systems. By using wave functions that are …

Deep learning methods have been shown to be effective in representing ground-state wave
functions of quantum many-body systems. Existing methods use convolutional neural …

Predicting the phase diagram of interacting quantum many-body systems is a central
problem in condensed matter physics and related fields. A variety of quantum many-body …