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2017

Identifying Quantum Phase Transitions with Adversarial Neural Networks

Huembeli, Patrick, Dauphin, Alexandre, Wittek, Peter

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The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system.

We address this problem with state-of-the-art deep learning techniques: adversarial domain adaptation.
We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning.
The input data set contains both labeled and unlabeled data instances.

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