# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
###### Abstract
In very recent times, we have been witnessing the introduction of quantum computing Machine Learning, devoted to exploiting quantum computing concepts in the Machine Learning field [15], i.e., to perform classification tasks by considering quantum theory [16]. In this paper, we propose several Android malware detectors based on deep learning architectures and quantum computing ones. The main idea is to introduce the concept of quantum computing in malware detection, comparing quantum and classical convolutional models in terms of accuracy for the malware detection task in the Android environment. At the current state-of-the-art, according to the knowledge of the authors, this paper represents the first attempt to introduce quantum computing in image-based malware detection. This paper represents an extension of the research entitled \"Introducing Quantum Computing in Mobile Malware Detection\" [17], accepted for publication at the 17 th International Workshop on Frontiers in Availability, Reliability, and Security (FARES 2022) to be held in conjunction with the 17 th International Conference on Availability, Reliability, and Security (ARES 2022). Concerning the work in [17], we explain the novel contributions introduced in this paper:  
* A fully quantum Machine Learning network is presented and considered in the experimental analysis, while in the previous paper, a quantum hybrid network was considered. The latter represents the main contribution of the paper: in fact, this represents the first attempt to apply a fully quantum Machine Learning model to a malware detection task;
* A comparison between two different quantum architectures, i.e., the full and the hybrid quantum one;
* Comparison between state-of-the-art Convolutional Neural Network and quantum architectures;
* We extend the number of experiments presented in [17] by tuning the models with the aim to empirically obtain better detection performances;
* Three state-of-the-art deep learning models are added (i.e., VGG19, MobileNet, and EfficientNet) to perform a more complete comparison:
* To provide explainability behind the model decision, we resort to an algorithm aimed to highlight the areas of the application under analysis (represented as an image) that mostly contributed to a certain prediction (i.e., malware or Trusted). To the best of the knowledge of the authors, this is the second main contribution of the paper. Indeed, this is the first attempt to apply explainability to a quantum Machine Learning model;
* We freely release for research purposes the source code we developed for the fully quantum architecture, to encourage researchers to investigate this area. The remainder of the paper proceeds as follows: In the next session, preliminary notions about malware detection, quantum computing, quantum Machine Learning, and Gradient-weighted Class Activation Mapping are reported. In Section 3, we present the models we considered; the results are reported in Section 4, while in Section 5, the latter are discussed. Finally, in the last section, the conclusions and future research plans are drawn.