Machine learning has recently gained attention as a possible way to understand phase transitions in many-body quantum systems. A new entanglement analysis reveals crucial properties of the data structures that encode quantum states in a neural network, opening new inroads in applying machine learning to quantum many-body physics.
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Deng et al. (2017) studied this question.
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