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.
Liang, Investigating the fermi-hubbard model by the tensor-backflow method, arXiv preprint arXiv:2507.01856 (2025)
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A post-processing sign-blocking technique mitigates the fermion sign problem by using data blocking to infer system energies from sign-energy correlations in Monte Carlo samples.
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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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A sign-blocking method for mitigating the fermion sign problem
A post-processing sign-blocking technique mitigates the fermion sign problem by using data blocking to infer system energies from sign-energy correlations in Monte Carlo samples.