Solving the quantum many-body problem with artificial neural networks
Top Cited Papers
- 10 February 2017
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 355 (6325), 602-606
- https://doi.org/10.1126/science.aag2302
Abstract
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems. Our approach achieves high accuracy in describing prototypical interacting spins models in one and two dimensions.Keywords
Funding Information
- European Commission Directorate-General for Research and Innovation (287220)
- Microsoft Research (287221)
- Lincoln Laboratory (287222)
- Swiss National Science Foundation (287223)
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