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.
URL https://arxiv.org/abs/2507.02644
16 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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.
Spin-adapted neural-network backflow (SA-NNBF) enforces total spin, removes spin contamination, and matches large-DMRG FeMoco energies with far fewer parameters.
Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.
In the 2D Hubbard model, the long-time double occupancy after a ramp quench matches the canonical thermal value for U≤3 but deviates above U≈3–4, suggesting thermalization breakdown.
Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
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.
A learnable Gaussian-basis locality parameter α lowers NNVMC variational energies on the 3D electron gas and sharpens the Fermi-liquid–Wigner-crystal transition for message-passing ansatze.
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
Neural network quantum states applied to the doped Kondo-Heisenberg model on the square lattice find Fermi-volume reconstruction and coexisting d-wave superconductivity between weak- and strong-Kondo regimes.
Numerical simulations show repulsive interactions enhance ferromagnetic correlations at high electron densities in the Kagome Hubbard model and extend the strong-correlation region toward half filling, linking smoothly to Nagaoka ferromagnetism.
DMRG on honeycomb cylinders and slave-boson mean-field theory find a robust t'-induced d-wave SC phase coexisting with armchair stripes for moderate t', transitioning to uniform nematic SC at large t' for doping 1/8.
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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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Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Neural network modeling of many-body super- and sub-radiant dynamics
Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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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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Spin-adapted neural network backflow for symmetry-preserving simulations of strongly correlated electrons
Spin-adapted neural-network backflow (SA-NNBF) enforces total spin, removes spin contamination, and matches large-DMRG FeMoco energies with far fewer parameters.
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Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Projected Inverse Iteration reframes ground-state search for neural quantum states as an eigenvalue problem to deliver rapid, spectral-gap-insensitive convergence while retaining polynomial scaling.
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Thermalization Dynamics in the Two-Dimensional Hubbard Model with Neural-Network Quantum States
In the 2D Hubbard model, the long-time double occupancy after a ramp quench matches the canonical thermal value for U≤3 but deviates above U≈3–4, suggesting thermalization breakdown.
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Scaling Laws for Neural-Network Quantum States
Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
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Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
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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.
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Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
A learnable Gaussian-basis locality parameter α lowers NNVMC variational energies on the 3D electron gas and sharpens the Fermi-liquid–Wigner-crystal transition for message-passing ansatze.
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Attention is all you need to solve chiral superconductivity
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
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Fermi surface change and $d$-wave superconductivity in the square lattice Kondo-Heisenberg model
Neural network quantum states applied to the doped Kondo-Heisenberg model on the square lattice find Fermi-volume reconstruction and coexisting d-wave superconductivity between weak- and strong-Kondo regimes.
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Finite-Temperature Ferromagnetic Correlations of the Kagome Lattice Hubbard Model
Numerical simulations show repulsive interactions enhance ferromagnetic correlations at high electron densities in the Kagome Hubbard model and extend the strong-correlation region toward half filling, linking smoothly to Nagaoka ferromagnetism.
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Superconductivity and competing orders in honeycomb $t$-$J$ model: interplay of lattice geometry and next-nearest-neighbor hopping
DMRG on honeycomb cylinders and slave-boson mean-field theory find a robust t'-induced d-wave SC phase coexisting with armchair stripes for moderate t', transitioning to uniform nematic SC at large t' for doping 1/8.