A self-attention fermionic neural network variationally solves the disk-geometry fractional quantum Hall problem including Landau level mixing, outperforming LLL-projected exact diagonalization and revealing short-distance wavefunction structure.
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Solving and visualizing fractional quantum Hall wavefunctions with neural network
A self-attention fermionic neural network variationally solves the disk-geometry fractional quantum Hall problem including Landau level mixing, outperforming LLL-projected exact diagonalization and revealing short-distance wavefunction structure.