Hybrid quantum-classical neural network for sentiment analysis

2026-07-02

AI Summary

This study investigates the applicability of quantum machine learning to natural language processing, with a focus on sentiment analysis. The authors propose a hybrid quantum-classical architecture that integrates parameterized quantum circuits with a classical feedforward neural network, processing text data represented as TF-IDF vectors. The model is evaluated on a real-world dataset of COVID-19-related tweets, achieving sentiment classification accuracy comparable to purely classical approaches. Furthermore, through transfer learning, the framework is successfully applied to SMS spam detection, where it improves spam-class identification accuracy by 15 percentage points—from 66% to 81%—demonstrating enhanced generalization capability and distinctive training dynamics relative to conventional models.

Abstract

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.

Problem

• sentiment analysis
• quantum machine learning
• natural language processing
• hybrid quantum-classical neural network
• text classification

Innovation

• hybrid quantum-classical neural network
• quantum machine learning
• sentiment analysis
• transfer learning
• natural language processing