تقرير
Approximately-symmetric neural networks for quantum spin liquids
| العنوان: | Approximately-symmetric neural networks for quantum spin liquids |
|---|---|
| المؤلفون: | Kufel, Dominik S., Kemp, Jack, Linsel, Simon M., Laumann, Chris R., Yao, Norman Y. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Condensed Matter Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Strongly Correlated Electrons, Computer Science - Machine Learning |
| الوصف: | 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 Comment: 5+10 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.17541 |
| رقم الأكسشن: | edsarx.2405.17541 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |