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Recurrent neural network optimization to approximate ground-states of quantum systems

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Recurrent_NN_VMC

Variational Monte Carlo optimization for the ground-state of a periodic XXZ Heisenberg model in one spatial dimension using a deep recurrent neural network (coded in tensorflow).

The variational wave-function is optimized using a perfect sampling technique.

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Recurrent neural network optimization to approximate ground-states of quantum systems

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  • Jupyter Notebook 89.8%
  • Python 10.2%