Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

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

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

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 …

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 …

Pursuing fractionalized particles that do not bear properties of conventional measurable
objects, exemplified by bare particles in the vacuum such as electrons and elementary …

Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …

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