Quantum Classifier for Natural Language Processing Applications

Abstract

A deep neural network is a branch of machinelearning that is capable of learning and representingcomplex patterns from a dataset through interconnectedmultiple layers of neurons. This capability makes itapplicable in various fields, such as natural languageprocessing, image processing, and computer vision.Deep learning models show effective performance butface challenges such as complexity and resourcedemands. On the other hand, quantum machine learningalgorithms offer an alternative with potential efficiencycompared to their classical counterparts. This paperproposes a Quantum Recurrent Neural Network (QRNN)for natural language processing tasks, which classify textdata such as parts of speech, named entity recognition,and sentiment analysis. The proposed method utilizesparameterized quantum circuits that contain the tunableparameters. Our approach uses amplitude encodingto represent classical data into quantum states, partialmeasurement for label determination, and ancilla qubitsto transfer the information from the current state to thenext.