Approximately-symmetric neural networks for quantum spin liquids

• URL: http://arxiv.org/abs/2405.17541v1
• Date: Mon, 27 May 2024 18:00:00 GMT
• Title: Approximately-symmetric neural networks for quantum spin liquids
• Authors: Dominik S. Kufel, Jack Kemp, Simon M. Linsel, Chris R. Laumann, Norman Y. Yao,
• Abstract summary: We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. Our work paves the way toward investigating quantum spin liquid problems within interpretable neural network architectures.
• Score: 0.4369058206183195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. These tailored architectures are parameter-efficient, scalable, and significantly out-perform existing symmetry-unaware neural network architectures. Utilizing the mixed-field toric code model, we demonstrate that our approach is competitive with the state-of-the-art tensor network and quantum Monte Carlo methods. Moreover, at the largest system sizes (N=480), our method allows us to explore Hamiltonians with sign problems beyond the reach of both quantum Monte Carlo and finite-size matrix-product states. The network comprises an exactly symmetric block following a non-symmetric block, which we argue learns a transformation of the ground state analogous to quasiadiabatic continuation. Our work paves the way toward investigating quantum spin liquid problems within interpretable neural network architectures

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