A hierarchical generative model for critical lattice scalar field theories achieves orders-of-magnitude lower autocorrelation times than HMC while enabling exact multilevel Monte Carlo.
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The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
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Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
A hierarchical generative model for critical lattice scalar field theories achieves orders-of-magnitude lower autocorrelation times than HMC while enabling exact multilevel Monte Carlo.
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Introduction to the artificial neural network-based variational Monte Carlo method
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.