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Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks by McNaughton, B.; Milošević, M. V.; Perali, A.; Pilati, S. is a scholarly article available to read on EtoBox.

What is Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks about?

The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionalities is that, after they have learned a probability distribution from a dataset, they allow exact and efficient sampling of typical system configurations. Here we employ a neural autoregressive distribution estimator (NADE) to boost Markov chain Monte Carlo (MCMC) simulations of a paradigmatic classical model of spin-glass theory, namely the two-dimensional Edwards-Anderson Hamiltonian. We show that a NADE can be trained to accurately mimic the Boltzmann distribution using unsupervised learning from system configurations generated using standard MCMC algorithms. The trained NADE is then employed as smart proposal distribution for the Metropolis-Hastings algorithm. This allows us to perform efficient MCMC simulations, which provide unbiased results even if the expectation value corresponding to the probability distribution learned by the NADE is not exact. Notably, we implement a sequential tempering procedure, whereby a NADE trained at a higher temperature is iteratively employed as proposal distribution in

Author

McNaughton, B.; Milošević, M. V.; Perali, A.; Pilati, S.

Published

2020

Language

EN