Quantum Machine Learning for Phishing Detection: A Systematic Review of Current Techniques, Challenges, and Future Directions

A systematic review of Quantum Machine Learning techniques for phishing detection, synthesizing existing studies to extract methodological patterns and design principles rather than benchmark performance. The review examines QML paradigms, feature encoding strategies, and the impact of Noisy Intermediate-Scale Quantum constraints on learning behavior and feasibility. It highlights fragmentation, scalability limits, and limited practical gains over classical methods, and outlines research directions toward more robust, theoretically grounded, and application relevant quantum approaches for cybersecurity.