Superpolynomial quantum-classical separation for density modeling
Niklas Pirnay, Ryan Sweke, Jens Eisert, Jean‐Pierre Seifert
Abstract
Density modeling is a machine learning task with the goal of learning an underlying probability distribution from samples. Here the authors show that, when solving certain density modeling problems, algorithms that use fault-tolerant quantum computers have an advantage over classical ones.
Topics & Concepts
Computer scienceLearning with errorsQuantumProbability density functionFunction (biology)Theoretical computer scienceStatistical physicsProbability distributionSupervised learningTask (project management)Artificial intelligenceAlgorithmCryptographyMathematicsQuantum mechanicsPhysicsBiologyStatisticsArtificial neural networkManagementEvolutionary biologyEconomicsQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum Mechanics and Applications