Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
Solving fractional electron states in twisted mote 2 with deep neural network.arXiv preprint arXiv:2503.13585,
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WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.
Charge-pumping simulation extracts Chern numbers and identifies anomalous composite Fermi liquids from neural network wavefunctions in fractional Chern insulators.
Variational Monte Carlo shows magnetic fields melt Wigner crystals into integer quantum Hall liquids via downward cusps in liquid ground-state energy at integer fillings, establishing a density range for the transition.
Neural quantum states yield Born-Oppenheimer and non-Born-Oppenheimer energies for high-pressure atomic hydrogen that match or beat prior projector Monte Carlo results up to 128 atoms while avoiding symmetry assumptions and mass-scale issues.
Remote band mixing in moiré models preferentially stabilizes electron Wigner crystals over hole crystals, explaining the greater instability of fractional Chern insulators at ν=1/3 than at ν=2/3.
Quantum confinement in 2D hexagonal crystals like graphene and TMDs produces discrete electronic and excitonic spectra with strongly amplified interactions that enable correlated quantum states.
citing papers explorer
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Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders
Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
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WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws
WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.
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Topological invariant of periodic many body wavefunction from charge pumping simulation
Charge-pumping simulation extracts Chern numbers and identifies anomalous composite Fermi liquids from neural network wavefunctions in fractional Chern insulators.
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Quantum melting a Wigner crystal into Hall liquids
Variational Monte Carlo shows magnetic fields melt Wigner crystals into integer quantum Hall liquids via downward cusps in liquid ground-state energy at integer fillings, establishing a density range for the transition.
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Neural Wave Functions for High-Pressure Atomic Hydrogen
Neural quantum states yield Born-Oppenheimer and non-Born-Oppenheimer energies for high-pressure atomic hydrogen that match or beat prior projector Monte Carlo results up to 128 atoms while avoiding symmetry assumptions and mass-scale issues.
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Band mixing and particle-hole asymmetry in moir\'e fractional Chern insulators
Remote band mixing in moiré models preferentially stabilizes electron Wigner crystals over hole crystals, explaining the greater instability of fractional Chern insulators at ν=1/3 than at ν=2/3.
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Correlated Quantum Phenomena in Confined Two-Dimensional Hexagonal Crystals
Quantum confinement in 2D hexagonal crystals like graphene and TMDs produces discrete electronic and excitonic spectra with strongly amplified interactions that enable correlated quantum states.
- Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model