Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913380524097536 |
|---|---|
| author | Luo, Di Chen, Zhuo Hu, Kaiwen Zhao, Zhizhen Hur, Vera Mikyoung Clark, Bryan K. |
| author_facet | Luo, Di Chen, Zhuo Hu, Kaiwen Zhao, Zhizhen Hur, Vera Mikyoung Clark, Bryan K. |
| contents | Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics. We develop a general approach to constructing gauge invariant or anyonic symmetric autoregressive neural network quantum states, including a wide range of architectures such as Transformer and recurrent neural network (RNN), for quantum lattice models. These networks can be efficiently sampled and explicitly obey gauge symmetries or anyonic constraint. We prove that our methods can provide exact representation for the ground and excited states of the 2D and 3D toric codes, and the X-cube fracton model. We variationally optimize our symmetry incorporated autoregressive neural networks for ground states as well as real-time dynamics for a variety of models. We simulate the dynamics and the ground states of the quantum link model of $\text{U(1)}$ lattice gauge theory, obtain the phase diagram for the 2D $\mathbb{Z}_2$ gauge theory, determine the phase transition and the central charge of the $\text{SU(2)}_3$ anyonic chain, and also compute the ground state energy of the SU(2) invariant Heisenberg spin chain. Our approach provides powerful tools for exploring condensed matter physics, high energy physics and quantum information science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2101_07243 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models Luo, Di Chen, Zhuo Hu, Kaiwen Zhao, Zhizhen Hur, Vera Mikyoung Clark, Bryan K. Strongly Correlated Electrons Disordered Systems and Neural Networks Machine Learning High Energy Physics - Lattice Quantum Physics Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics. We develop a general approach to constructing gauge invariant or anyonic symmetric autoregressive neural network quantum states, including a wide range of architectures such as Transformer and recurrent neural network (RNN), for quantum lattice models. These networks can be efficiently sampled and explicitly obey gauge symmetries or anyonic constraint. We prove that our methods can provide exact representation for the ground and excited states of the 2D and 3D toric codes, and the X-cube fracton model. We variationally optimize our symmetry incorporated autoregressive neural networks for ground states as well as real-time dynamics for a variety of models. We simulate the dynamics and the ground states of the quantum link model of $\text{U(1)}$ lattice gauge theory, obtain the phase diagram for the 2D $\mathbb{Z}_2$ gauge theory, determine the phase transition and the central charge of the $\text{SU(2)}_3$ anyonic chain, and also compute the ground state energy of the SU(2) invariant Heisenberg spin chain. Our approach provides powerful tools for exploring condensed matter physics, high energy physics and quantum information science. |
| title | Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models |
| topic | Strongly Correlated Electrons Disordered Systems and Neural Networks Machine Learning High Energy Physics - Lattice Quantum Physics |
| url | https://arxiv.org/abs/2101.07243 |