Exploiting Symmetry in Variational Quantum Machine Learning
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, Jens Eisert
Abstract
A blueprint for exploiting symmetries in the construction of variational quantum learning models that can result in improved generalization performance is developed and demonstrated on practical problems.
Topics & Concepts
BlueprintGeneralizationHomogeneous spaceSymmetry (geometry)QuantumComputer scienceArtificial intelligenceTheoretical physicsTheoretical computer scienceApplied mathematicsAlgebra over a fieldMathematicsPure mathematicsPhysicsQuantum mechanicsMathematical analysisEngineeringMechanical engineeringGeometryQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum-Dot Cellular Automata