WF-Bench is a new benchmark for neural network wavefunctions that matches them to diverse quantum many-body targets and derives empirical scaling laws for representability based on system size and model parameters like determinant count and depth.
When can classical neural networks represent quantum states?
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Dilated RNN wave functions induce power-law correlations for the critical 1D transverse-field Ising model and the Cluster state, unlike the exponential decay of conventional RNN ansatze.
Variational optimization of quantum ground states represented as SIC-POVM outcome probabilities using GRU autoregressive networks, tested on 1D Ising and Heisenberg models up to L=128.
A review of how quantum information science is expected to provide new tools and insights for nuclear and high-energy physics phenomenology and quantum simulations.
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Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States
Dilated RNN wave functions induce power-law correlations for the critical 1D transverse-field Ising model and the Cluster state, unlike the exponential decay of conventional RNN ansatze.