Neural Network Quantum States (NQS or NNQS) is a general class of variational quantum states parameterized in terms of an artificial neural network. It was first introduced in 2017 by the physicists Giuseppe Carleo and Matthias Troyer[1] to approximate wave functions of many-body quantum systems.
Given a many-body quantum state comprising degrees of freedom and a choice of associated quantum numbers , then an NQS parameterizes the wave-function amplitudes
where is an artificial neural network of parameters (weights) , input variables () and one complex-valued output corresponding to the wave-function amplitude.
This variational form is used in conjunction with specific stochastic learning approaches to approximate quantum states of interest.
^Cite error: The named reference CarleoTroyer:2016 was invoked but never defined (see the help page).
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