Unsupervised quantum circuit learning in high energy physics
Andrea Delgado, Kathleen E. Hamilton
Abstract
Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.
Topics & Concepts
Generative grammarQuantumTask (project management)Computer scienceUnsupervised learningQuantum circuitQuantum machine learningEnergy (signal processing)Artificial intelligencePhysicsQuantum computerMachine learningQuantum mechanicsQuantum networkSystems engineeringEngineeringQuantum Computing Algorithms and ArchitectureComputational Physics and Python ApplicationsParticle physics theoretical and experimental studies