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This study explores the use of hybrid quantum-classical neural networks for sentiment analysis, specifically analyzing COVID-19-related tweets. By vectorizing text with TF-IDF and integrating both classical feedforward networks and quantum circuits, the authors reveal that hybrid models not only match classical performance but also exhibit superior learning dynamics. Notably, in a transfer learning scenario for SMS spam classification, the hybrid approach outperformed classical methods by 15 percentage points, underscoring its enhanced generalization capabilities.
Hybrid quantum-classical models can significantly boost sentiment analysis performance, achieving a 15-point accuracy leap in spam classification tasks.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
