Quantum-Classical Hybrid Approaches for Robust Malware Classification

Reliable detection of malware is a fundamental component of cybersecurity in the modern world. Models have to be able to identify new and sophisticated malware strains, even among large and complex datasets, while having accuracy, efficiency, and understandability. Review of previous research, based on direct comparison, of Quantum>Support Vector Machines, Quantum Neural>Networks,and hybrid modeles such-as Quantum Multilayer Perceptron is the basis of the proposed clear and concise QML framework for malware detection. Basic research by Cai et al. on QSVM demonstrates high classification accuracy. In exploration of bases, QNN has passed the trials and indicated the need for improvement due to data re-uploading. Finally, further experiments on QMLP and QCNN researched the relationship between classification accuracy and model training cost. XAI added the level of interpretability and the analysis demonstrated an O(log n) computational leverage, which is the key ingredient that maintains this field. These combinations of research construct the unified framework of the strong classification strength of QSVM, the architecture flexibility of QNN, and the insights from XAI. This leads to a more robust accuracy, explainability, and reliability of existing and future malware detection systems.