177 citations · 1.4k across the 57 of their papers we have counts for
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physics.comp-ph2020★ 24 cited
Scalable neural networks for the efficient learning of disordered quantum systems
N. Saraceni, S. Cantori, S. Pilati
Supervised machine learning is emerging as a powerful computational tool to predict the properties of complex quantum systems at a limited computational cost. In this article, we q…
physics.comp-ph2020★ 22 cited
Simulating disordered quantum systems via dense and sparse restricted Boltzmann machines
S. Pilati, P. Pieri
In recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs) have been successfully employed as accurate and flexible variational wave funct…
physics.comp-ph2020★ 58 cited
Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks
B. McNaughton, M. V. Milošević, A. Perali +1
The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionaliti…