  With the increasing number and sophistication of malware attacks, malware
detection systems based on machine learning (ML) grow in importance. At the
same time, many popular ML models used in malware classification are supervised
solutions. These supervised classifiers often do not generalize well to novel
malware. Therefore, they need to be re-trained frequently to detect new malware
specimens, which can be time-consuming. Our work addresses this problem in a
hybrid framework of theoretical Quantum ML, combined with feature selection
strategies to reduce the data size and malware classifier training time. The
preliminary results show that VQC with XGBoost selected features can get a
78.91% test accuracy on the simulator. The average accuracy for the model
trained using the features selected with XGBoost was 74% (+- 11.35%) on the IBM
5 qubits machines.
