Machine learning holographic mapping by neural network renormalization group
Hong-Ye Hu, Shuo-Hui Li, Lei Wang, Yi-Zhuang You
Abstract
The authors design a machine learning scheme to explore problems in quantum field theory. The method relies in providing quantum field theory as a visible layer of the machine, living on the boundary, and results in a dual theory that governs its hidden variables
Topics & Concepts
Artificial neural networkArtificial intelligenceField (mathematics)Computer scienceScheme (mathematics)QuantumMachine learningDual (grammatical number)Quantum field theoryGroup (periodic table)HolographyRenormalization groupMathematicsAlgorithmLayer (electronics)Hidden variable theoryField theory (psychology)Deep learningPattern recognition (psychology)Computational learning theoryQuantum computerQuantum informationTheoretical computer scienceRenormalizationBlack Holes and Theoretical PhysicsQuantum many-body systemsTopological Materials and Phenomena