Grassmann Variational Monte Carlo generalizes neural-network excited-state optimization to subspaces and accurately reproduces low-lying spectra of the 2D Heisenberg model.
Its corresponding un- normalized amplitudes are given by: ⟨⟨S|Φ⟩⟩ = det [ [S|Φ] ]
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Grassmann Variational Monte Carlo with neural wave functions
Grassmann Variational Monte Carlo generalizes neural-network excited-state optimization to subspaces and accurately reproduces low-lying spectra of the 2D Heisenberg model.