5 papers
Variational Autoregressive Networks with probability priors
Piotr BiaÅas, Piotr Korcyl, Tomasz Stebel +1
Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increase in autocorrelation times n…
Sampling two-dimensional spin systems with transformers
Piotr BiaÅas, Piotr Korcyl, Tomasz Stebel +2
Autoregressive Neural Networks based on dense or convolutional layers have recently been shown to be a viable strategy for generating classical spin systems. Unlike these methods,…
Hierarchical autoregressive neural networks in three-dimensional statistical system
Piotr BiaÅas, Vaibhav Chahar, Piotr Korcyl +3
Autoregressive Neural Networks (ANN) have been recently proposed as a mechanism to improve the efficiency of Monte Carlo algorithms for several spin systems. The idea relies on the…
Estimation of the reduced density matrix and entanglement entropies using autoregressive networks
Piotr BiaÅas, Piotr Korcyl, Tomasz Stebel +1
We present an application of autoregressive neural networks to Monte Carlo simulations of quantum spin chains using the correspondence with classical two-dimensional spin systems.…
NeuMC -- a package for neural sampling for lattice field theories
Piotr Bialas, Piotr Korcyl, Tomasz Stebel +1
We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normali…