8 papers
Superconductivity in the - Hubbard Model from Symmetry-Preserving Neural-Network Quantum States
Riccardo Rende, Luciano Loris Viteritti, Antoine Georges
Despite its fundamental importance in the theory of strongly correlated electrons, the nature of the ground state of the two-dimensional doped Hubbard model remains intensely debat…
Scaling Laws for Neural-Network Quantum States
Riccardo Rende, Alessandro Sinibaldi, Luciano Loris Viteritti +3
Scaling laws, the power-law relations between loss, architecture size, and compute observed in modern neural networks, offer a quantitative way to characterize the complexity of a…
Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
Luciano Loris Viteritti, Riccardo Rende, Christopher Roth +3
The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its…
Double descent: When do neural quantum states generalize?
M. Schuyler Moss, Alev Orfi, Christopher Roth +5
Neural quantum states (NQS) provide flexible and compact wavefunction parameterizations for numerical studies of quantum many-body physics. In particular, NQS aim to circumvent the…
Learning interactions between Rydberg atoms
Olivier Simard, Anna Dawid, Joseph Tindall +3
Quantum simulators have the potential to solve quantum many-body problems that are beyond the reach of classical computers, especially when they feature long-range entanglement. To…
Neural Network-Augmented Pfaffian Wave-functions for Scalable Simulations of Interacting Fermions
Ao Chen, Zhou-Quan Wan, Anirvan Sengupta +2
Developing accurate numerical methods for strongly interacting fermions is crucial for improving our understanding of various quantum many-body phenomena, especially unconventional…