mlsanityData to 14 Oct 2026

Approximately-symmetric neural networks for quantum spin liquids

arXiv 2405.17541 ↗First posted 27 May 2024quant-ph cond-mat.dis-nn cond-mat.str-elcited by 5

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Abstract (arXiv)

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 outperform existing symmetry-unaware neural network architectures. Utilizing the mixed-field toric code and PXP Rydberg Hamiltonian models, 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 for toric code, N=1584 for Rydberg PXP), 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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 113 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

27 of 113 references.

References · 27

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15 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
  2. 2024Relaxed Equivariance via Multitask Learningcited by 13
  3. 2025Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetrycited by 7
  4. 2024Learning topological states from randomized measurements using variational tensor network tomographycited by 3
  5. 2026Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoderscited by 1