A Quantum Multimodal Neural Network Model for Sentiment Analysis on Quantum Circuits

Abstract

This article proposes a quantum multimodal neural network (QMNN) model that can be implemented on parameterized quantum circuits (PQCs), providing a novel avenue for processing multimodal data and performing advanced multimodal sentiment analysis tasks. The comprehensive QMNN model is structured into four fundamental blocks: multimodal data preprocessing, unimodal feature extraction, multimodal feature fusion, and optimization. Through these blocks, multimodal data are initially preprocessed and encoded into quantum states. Subsequently, visual and textual features are extracted from the quantum states and are then integrated to learn the interactions between different modalities. Finally, the model parameters are fine-tuned to optimize the sentiment analysis performance. Simulation results confirm that QMNN surpasses state-of-the-art baselines, using significantly lower input dimensions and substantially fewer parameters than classical models. Furthermore, the entanglement, integrity, robustness, and scalability of the model are analyzed in depth. Internally, the strong entanglement within the multimodal fusion block enhances interactions between textual and visual features, and the integrity of the model reflects the indispensable contribution of each component to the overall performance. Externally, robustness ensures the model operates stably under noisy conditions and incomplete inputs, and scalability enables it to efficiently adapt to varying architectural depths and widths. The above simulation results and performance analyses showcase the comprehensive strength of our proposed model.