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
15 Pith papers cite this work. Polarity classification is still indexing.
15
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
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.
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.
Real-time dynamics in the 2D Hubbard model show thermalization of double occupancy below a critical U_c but clear breakdown of thermalization above it.
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 general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
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.
citing papers explorer
-
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.