3 citations · 6 across the 8 of their papers we have counts for
10 papers
TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Kacper Cybiński, Björn van Zwol, James Enouen +5
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is gui…
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…
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…
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…
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Kacper Cybiński, James Enouen, Antoine Georges +1
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating…
Fast Scrambling at the Boundary
Ancel Larzul, Anirvan M. Sengupta, Antoine Georges +1
Many-body systems which saturate the quantum bound on chaos are attracting interest across a wide range of fields. Notable examples include the Sachdev-Ye-Kitaev model and its vari…
Mott Transition and Volume Law Entanglement with Neural Quantum States
Chloé Gauvin-Ndiaye, Joseph Tindall, Javier Robledo Moreno +1
The interplay between delocalisation and repulsive interactions can cause electronic systems to undergo a Mott transition between a metal and an insulator. Here we use neural netwo…