Physicists dating back to Feynman have lamented the difficulties of applying the variational
principle to quantum field theories. In nonrelativistic quantum field theories, the challenge is …

Quantum many-body physics simulation has important impacts on understanding
fundamental science and has applications to quantum materials design and quantum …

Among the variational wave functions for fermionic Hamiltonians, neural network backflow
(NNBF) and hidden fermion determinant states (HFDS) are two prominent classes that …

Studying the dynamics of open quantum systems can enable breakthroughs both in
fundamental physics and applications to quantum engineering and quantum computation …

The ground state of second-quantized quantum chemistry Hamiltonians provides access to
an important set of chemical properties. Wave functions based on machine-learning …

We introduce a pairing-based graph neural network, $\textit {GemiNet} $, for simulating
quantum many-body systems. Our architecture augments a BCS mean-field wavefunction …

Lattice gauge theories coupled to fermionic matter account for many interesting phenomena
in both high-energy physics and condensed-matter physics. Certain regimes, eg, at finite …

Partial differential equations (PDEs) are instrumental for modeling dynamical systems in
science and engineering. The advent of neural networks has initiated a significant shift in …

Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial
quantum materials with designer properties. We solve the many-body problem of interacting …

In this paper, a Hamiltonian lattice formulation for 2+ 1D compact Maxwell-Chern-Simons
theory is derived. We analytically solve this theory and demonstrate that the mass gap in the …