222 citations · 961 across the 42 of their papers we have counts for
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quant-ph2019★ 53 cited
The learnability scaling of quantum states: restricted Boltzmann machines
Dan Sehayek, Anna Golubeva, Michael S. Albergo +3
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…
hep-lat2019★ 222 cited
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo, G. Kanwar, P. E. Shanahan
A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimiz…