In the dilute limit of the 1D infinite-U Hubbard model the charge Drude weight admits a closed-form expression whose low-temperature expansion, after regularization of the singular contribution, yields linear-in-T resistivity.
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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.
Fractal lattices like the Sierpiński carpet suppress d-wave superconductivity at half filling while strongly enhancing extended s-wave pairing at other fillings through geometric frustration of sign-changing order parameters.
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.
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.
GCNN variational states optimized with directed-loop sampling yield a 4-fold degenerate ground state for V ≤ 0.4 in the quantum dimer model, with benchmarks matching ED and QMC up to L=32.
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Time-dependent variational Monte Carlo without bias
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.