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 …

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

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 …

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 …

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 …

Describing the ground states of continuous, real-space quantum many-body systems, like
atoms and molecules, is a significant computational challenge with applications throughout …

We consider achieving equivariance in machine learning systems via frame averaging.
Current frame averaging methods involve a costly sum over large frames or rely on sampling …

Capturing the correlation emerging between constituents of many-body systems accurately
is one of the key challenges for the appropriate description of various systems whose …