Neural networks in quantum many-body physics: a hands-on tutorial
Published:
Summary:
Overview of some applications of machine learning in condensed matter physics and quantum information. For example:
- supervised machine learning with convolutional neural networks to learn a phase transition
- unsupervised learning with restricted Boltzmann machines to perform quantum tomography
- variational Monte Carlo with recurrent neural-networks for approximating the ground state of a many-body Hamiltonian.
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