Hard-coding translational, reflection, and bit-flip symmetries into Boltzmann-style neural quantum states cuts parameters from thousands to tens and speeds up training while preserving ground-state accuracy, with new Fubini-Study diagnostics tying the gains to a more target-focused optimization…
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Symmetry Constraints Regularize Neural Quantum State Learning
Hard-coding translational, reflection, and bit-flip symmetries into Boltzmann-style neural quantum states cuts parameters from thousands to tens and speeds up training while preserving ground-state accuracy, with new Fubini-Study diagnostics tying the gains to a more target-focused optimization…