Abstract
This study conducts a comparative analysis of two quantum classifiers, namely the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC), within the framework of sentiment analysis on both real-world and synthetic datasets. The primary aim is to assess their performance and delineate the current limitations and challenges associated with the application of these classifiers to complex tasks. The IMDB movie review dataset serves as a real-world example, while a generated dataset is employed for a simplified benchmark. The findings indicate that, although both classifiers exhibit potential in sentiment analysis, their performance on real-world datasets is impeded by factors such as limited qubit numbers, noise, and error rates in contemporary quantum hardware. This study underscores the necessity for advancements in quantum hardware and algorithms to enhance performance in sentiment analysis tasks and offers insights into potential avenues for future research.
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Belhadef, H., Benchiheb, H., Lebdjiri, L. (2023). Exploring the Capabilities and Limitations of VQC and QSVC for Sentiment Analysis on Real-World and Synthetic Datasets. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_36
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DOI: https://doi.org/10.1007/978-3-031-42941-5_36
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