SCALE and ACE are new convolutional backflow architectures for Neural Quantum States that deliver O(N^3) scaling with high accuracy and over 40x speedup on Hubbard and t-J models up to 32x32 lattices.
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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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Pareto Frontier of Neural Quantum States: Scalable, Affordable, and Accurate Convolutional Backflow for Strongly Correlated Lattice Fermions
SCALE and ACE are new convolutional backflow architectures for Neural Quantum States that deliver O(N^3) scaling with high accuracy and over 40x speedup on Hubbard and t-J models up to 32x32 lattices.
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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.