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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Exact condensate-pair eigenstates are built for Fermi ladders under SU(2) symmetry via spectrum generating algebra and mapped to Bose ladders by operator replacement, revealing pair equivalence and a possible Hilbert-space fragmentation mechanism.
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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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Condensate states in Fermi and Bose-Hubbard ladders
Exact condensate-pair eigenstates are built for Fermi ladders under SU(2) symmetry via spectrum generating algebra and mapped to Bose ladders by operator replacement, revealing pair equivalence and a possible Hilbert-space fragmentation mechanism.