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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Three Transformer backflow fermionic wave functions for the finite-doping Hubbard model converge, after accuracy improvements, to the same state with coexisting superconducting and stripe orders, demonstrating that variational energy is insufficient to identify the ground state.
Broad regions of horizontal and vertical spin stripes appear at low temperature in the Hubbard model, with wavelengths that vary nonlinearly with hole doping and are highly sensitive to the ratio of nearest- and next-nearest-neighbor hoppings.
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
citing papers explorer
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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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Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
Three Transformer backflow fermionic wave functions for the finite-doping Hubbard model converge, after accuracy improvements, to the same state with coexisting superconducting and stripe orders, demonstrating that variational energy is insufficient to identify the ground state.
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Spin-stripes in the Hubbard model: a combined DMFT and Bethe-Salpeter analysis
Broad regions of horizontal and vertical spin stripes appear at low temperature in the Hubbard model, with wavelengths that vary nonlinearly with hole doping and are highly sensitive to the ratio of nearest- and next-nearest-neighbor hoppings.
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Solving the Gross-Pitaevskii equation on multiple different scales using the quantics tensor train representation
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.