Approximately-symmetric neural networks for quantum spin liquids

Autor:	Kufel, Dominik S., Kemp, Jack, Linsel, Simon M., Laumann, Chris R., Yao, Norman Y.
Rok vydání:	2024
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Strongly Correlated Electrons Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.17541 Zobrazit plný text záznamu View this record from Arxiv