Quantum Text Encoding for Classification Tasks
Aaranya Alexander, Dominic Widdows
Abstract
This paper explores text classification on quantum computers. Previous results have achieved perfect accuracy on an artificial dataset of 100 short sentences, but at the unscalable cost of using a qubit for each word. This paper demonstrates that an amplitude encoded feature map combined with a quantum support vector machine can achieve 62% average accuracy predicting sentiment using a dataset of 50 actual movie reviews. This is still small, but considerably larger than previously-reported results in quantum NLP.
Topics & Concepts
Computer scienceEncoding (memory)QubitFeature (linguistics)QuantumWord (group theory)Artificial intelligenceNatural language processingSupport vector machinePattern recognition (psychology)MathematicsLinguisticsQuantum mechanicsPhysicsGeometryPhilosophyQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum Mechanics and Applications