Classification of texts based on classical and quantum features

Abstract

This article proposes classical and quantum vectorization methods for text data classification, along with corresponding neural network architectures for classification based on these representations. The study applies classical and quantum variants of one-hot encoding, bag-of-words, TF-IDF, and word2vec vectorization techniques, which are respectively integrated with CNN, LSTM, MLP, and BiLSTM neural network models for text classification. All models are trained on the same dataset under identical experimental conditions, with classification accuracy and computational efficiency adopted as the primary evaluation metrics. Classical vectorization methods represent semantic and contextual properties of text in a limited manner, whereas quantum vectorization overcomes these limitations. When neural networks are trained using quantum-based representations, higher classification accuracy and significantly reduced computation time are achieved compared to classical approaches. The combined use of quantum word2vec and the BiLSTM architecture yields the highest performance among all evaluated models. The obtained results demonstrate that this approach is the most effective for text data classification, as it accurately captures semantic relationships and leverages deep neural network architectures. This study may serve as a scientific foundation for future research aimed at developing more advanced neural architectures based on quantum computing paradigms.