Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models
This provides powerful tools for exploring condensed matter physics, high energy physics, and quantum information science, though it is incremental as it builds on existing neural network methods by incorporating symmetries.
The authors tackled the problem of incorporating gauge invariance and anyonic symmetry into autoregressive neural network quantum states for quantum lattice models, achieving exact representations for ground and excited states in models like the 2D/3D toric codes and X-cube fracton model, and successfully simulating dynamics and phase diagrams for various systems.
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.