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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Approximately-invariant neural networks for quantum many-body physics

Dominik Kufel · Jack Kemp · Norman Yao

Abstract: We propose \textit{approximately} group-invariant neural networks for quantum many-body physics problems. Those tailored-made architectures are parameter-efficient, scalable, significantly outperform existing symmetry-unaware neural network architectures and are competitive with the state-of-the-art iPEPS methods as we demonstrate on a perturbed toric code toy model on a $10 \times 10$ lattice. This paves way towards studying traditionally challenging quantum spin liquid problems within interpretable neural network architectures.

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