Quantum Machine Learning for Phishing URLs Detection: Implementation and Evaluation with Qiskit
Yi Wei, Yuji Sekiya, Masaya Nakayama, Synge Todo · 2025
Phishing is a kind of cybercrime where attackers trick unsuspecting network users into revealing sensitive information, resulting in identity theft and financial damage. As this threat continues to grow, artificial intelligence strategies have emerged as a promising solution for its detection in recent years. However, conventional machine learning approaches are starting to show limitations because training on large datasets in standard computing environments may either take too long for accurate results or produce poor accuracy with quick training. The recent advances in the study of the application of quantum computing to machine learning tasks have demonstrated that existing solutions can be strengthened by utilizing the synergy of the two fields, known as Quantum Machine Learning (QML). To explore the potential of this emerging technique, this study implements various QML models integrated with the latest version of Qiskit and presents a detailed execution process along with a comprehensive comparative analysis. The quantitative results obtained from the experiments demonstrate the significant potential of QML models in phishing detection. Furthermore, these state-of-the-art assessments also contribute to future research in improving the composition of QML models applied in the cyber defense field.