QMLSC: A quantum multimodal learning model for sentiment classification

Abstract

Sentiment classification research is gaining prominence for enhancing user experience, facilitating targeted marketing, and supporting mental health assessments while driving technological innovation. Due to the complexity and diversity of emotional expression, this study proposes quantum multimodal learning for sentiment classification (QMLSC), a novel quantum–classical hybrid model that integrates text and speech data to capture emotional signals more effectively. To address the limitations of the noisy intermediate-scale quantum era, we designed advanced variational quantum circuit (VQC) architectures to efficiently process high-dimensional data, maximizing feature retention and minimizing information loss. Our approach employs a residual structure that fuses quantum and classical components, enhancing the benefits of quantum features and conventional machine learning attributes. By using randomized expressive circuits, we improve system flexibility, accuracy, and robustness in sentiment classification tasks. Integrating VQC significantly reduces the number of parameters compared to fully connected layers, resulting in improved accuracy and computational efficiency. Empirical findings validate the superior performance of our fusion approach in effectively mitigating noise and error impacts associated with quantum computing and demonstrate strong potential for future applications in complex emotional information processing. This study provides new insights and methodologies for advancing sentiment classification technology and highlights the broad application potential for advancing quantum computing in information processing fields.