Okay, this one got published in Science today, https://arxiv.org/abs/1606.02318, they solve an n-body quantum wave function with artificial neural nets, they earned two separate commentary articles:
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial 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 variable number of hidden neurons. A reinforcement-learning scheme is then demonstrated, capable of either finding the ground-state or describing the unitary time evolution of complex interacting quantum systems. We show that this approach achieves very high accuracy in the description of equilibrium and dynamical properties of prototypical interacting spins models in both one and two dimensions, thus offering a new powerful tool to solve the quantum many-body problem. This is getting sort of close to home, now, we're replacing cleverly contrived numerical methods for exotic quantum physics with generic machine learning algorithms. -- rec --
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