A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
URL https://arxiv.org/abs/2506.19960
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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.
QERNEL is a single conditioned neural wavefunction that variationally solves families of many-electron Hamiltonians in moiré heterobilayers and identifies the quantum liquid-crystal phase transition.
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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Neural network quantum states in the grand canonical ensemble
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.