HI-NQS uses a dual-channel autoregressive Transformer NQS inside an iterative sample-diagonalize-update loop to reach chemical accuracy on small molecules and nitrogen active spaces with better determinant scaling than CIPSI.
Large language model scaling laws for neural quantum states in quantum chemistry.Mach
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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.
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An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction
HI-NQS uses a dual-channel autoregressive Transformer NQS inside an iterative sample-diagonalize-update loop to reach chemical accuracy on small molecules and nitrogen active spaces with better determinant scaling than CIPSI.
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Scaling Laws for Neural-Network Quantum States
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.