Very exciting... I'll have to read deeper into this... I think we are
on the verge of another punctuation in our equilibrium (of Sci/Tech
advances)...
On 2/9/17 3:20 PM, Roger Critchlow wrote:
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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