Quantum N-Gram Language Models for Tweet Classification

This paper presents a novel approach to text classification using n-grams and quantum machine learning models. We demonstrate the effectiveness of our approach by applying it to classify tweets, a challenging and noisy text classification task. Specifically, we show that using quantum models can significantly improve classification performance compared to classical ones. Additionally, we leverage quantum hardware to compute higher-order n-grams (more than bi-grams) and observe a boost in computation performance. Our experimental results demonstrate the potential of quantum computing for text classification tasks and pave the way for further exploration of quantum machine learning methods in natural language processing.