Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models

Abstract

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 networks, including a wide range of architectures such as transformer and recurrent neural network, 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 two- and three-dimensional 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 U(1) lattice gauge theory, obtain the phase diagram for the two-dimensional Z2 gauge theory, determine the phase transition and the central charge of the 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.26 MoreReceived 3 December 2021Revised 26 May 2022Accepted 15 December 2022DOI:https://doi.org/10.1103/PhysRevResearch.5.013216Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasGauge theoriesMachine learningTopological phases of matterTechniquesGauge symmetriesLattice models in condensed matterVariational approachQuantum InformationCondensed Matter, Materials & Applied PhysicsParticles & Fields