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Restricted Boltzmann machines in quantum physics

A type of stochastic neural network called a restricted Boltzmann machine has been widely used in artificial intelligence applications for decades. They are now finding new life in the simulation of complex wavefunctions in quantum many-body physics.

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Fig. 1: Using variational methods to learn the parameters of an RBM.
Fig. 2: Training an RBM with experimental data.


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We thank G. Torlai, A. Rocchetto and M. Albergo for fruitful discussions. This research was supported by NSERC of Canada, the Perimeter Institute for Theoretical Physics, the Shared Hierarchical Academic Research Computing Network (SHARCNET) and the National Science Foundation under grant no. PHY-1748958. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Economic Development, Job Creation and Trade. R.G.M. acknowledges support from a Canada Research Chair. J.C. acknowledges financial and computational support from the AI grant. J.I.C. was funded from the European Research Council (grant agreement no. 742102).

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Correspondence to Roger G. Melko.

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Melko, R.G., Carleo, G., Carrasquilla, J. et al. Restricted Boltzmann machines in quantum physics. Nat. Phys. 15, 887–892 (2019).

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