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.
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Transport and Temperature 1: Exact spectrum and resistivity for the one-dimensional infinite-$U$ Hubbard model
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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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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Extended Hubbard model on fractals: d-Wave superconductivity and competing pairing channels
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.
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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.
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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.
- Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
- Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model