Disorder-aware neural quantum states provide the first microscopic evidence for a pinned hole Wigner crystal near ν=2/3 that accounts for reentrant integer quantum Hall physics and reveals an electron-hole asymmetry in crystallization.
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Fermi Sets achieve universal approximation of fermionic wavefunctions using K antisymmetric bases times symmetric neural networks, where K equals 1 in 1D, 2 in 2D, and grows linearly with particle number in higher dimensions.
Self-attention variational wavefunctions for the 2D homogeneous electron gas up to N=169 yield energies below DMC and a converged collective-mode dispersion including a roton-like minimum.
QERNEL is a single conditioned neural wavefunction that variationally solves families of many-electron Hamiltonians in moiré heterobilayers and identifies the quantum liquid-crystal phase transition.
Irrep-resolved GCNN variational energies indicate a 4-fold degenerate columnar ground state for V <= 0.4 in the square-lattice quantum dimer model, shifting possible plaquette or mixed ordering to 0.4 < V < 1.
citing papers explorer
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Crystallization in the Fractional Quantum Hall Regime with Disorder-Aware Neural Quantum States
Disorder-aware neural quantum states provide the first microscopic evidence for a pinned hole Wigner crystal near ν=2/3 that accounts for reentrant integer quantum Hall physics and reveals an electron-hole asymmetry in crystallization.
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Fermi Sets: Universal and interpretable neural architectures for fermions
Fermi Sets achieve universal approximation of fermionic wavefunctions using K antisymmetric bases times symmetric neural networks, where K equals 1 in 1D, 2 in 2D, and grows linearly with particle number in higher dimensions.
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Accurate Self-Attention Wavefunctions at Large Scale
Self-attention variational wavefunctions for the 2D homogeneous electron gas up to N=169 yield energies below DMC and a converged collective-mode dispersion including a roton-like minimum.
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QERNEL: a Scalable Large Electron Model
QERNEL is a single conditioned neural wavefunction that variationally solves families of many-electron Hamiltonians in moiré heterobilayers and identifies the quantum liquid-crystal phase transition.
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Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model
Irrep-resolved GCNN variational energies indicate a 4-fold degenerate columnar ground state for V <= 0.4 in the square-lattice quantum dimer model, shifting possible plaquette or mixed ordering to 0.4 < V < 1.
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