arXiv:1709.06475cond-mat.str-el
Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
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Abstract
We develop a machine learning method to construct accurate ground-state wave functions of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A restricted Boltzmann machine algorithm in the form of an artificial neural network is combined with a conventional variational Monte Carlo method with pair product (geminal) wave functions and quantum number projections. The combination allows an application of the machine learning scheme to interacting fermionic systems. The combined method substantially improves the accuracy beyond that ever achieved by each method separately, in the Heisenberg as well as Hubbard models on square lattices, thus proving its power as a highly accurate quantum many-body solver.
Quantum mechanics and machine learning seem to be closely related, such that they can emulate each other efficiently.¹ Both can be represented with very high dimensional vectors, non-commuting observables and linear algebra. I personally suspect there are a lot more similarities…
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