Approximately-symmetric neural networks for quantum spin liquids
<|reference_start|>Approximately-symmetric neural networks for quantum spin liquids: 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<|reference_end|>
@article{kufel2024approximately-symmetric,
    title={Approximately-symmetric neural networks for quantum spin liquids},
    author={Dominik S. Kufel, Jack Kemp, Simon M. Linsel, Chris R. Laumann, Norman
  Y. Yao},
    journal={arXiv preprint arXiv:2405.17541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17541},
    primaryClass={quant-ph cond-mat.dis-nn cond-mat.str-el cs.LG}
}