Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
arXiv preprint arXiv:2311.02143 (2023) arXiv:2311.02143 [cond-mat.str-el]
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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.
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
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Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states
Direct differentiation of the local energy at fixed samples yields an unbiased low-variance estimator for the variational Monte Carlo phase force in complex neural quantum states, with an adaptive mixture extending it to coupled networks and improving results on flux ladders, chiral chains, and frac
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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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Attention is all you need to solve chiral superconductivity
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.