# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 6 Related Work  
In this section, we provide the current state-of-art use of deep learning to detect mobile malware and the adoption of quantum Machine Learning for general tasks. The use of quantum computing is significant in several fields, such as medicine. In fact, after the global pandemic in 2020, the usage of deep learning helped during the evaluation of the thoracic CT exam. To improve that performance, [PERSON] et al. in [42] used _QML_. The authors concluded that, through the use of _quantum algorithms_, the performances in spotting the infection in its early stages increased. The authors in [25] implemented Support Vector Machine (SVM) in a quantum computer with the complexity logarithmic in the size of the vectors and the number of training examples. In circumstances when classical sampling algorithms need polynomial time, an exponential speedup is obtained. At the core of this quantum network, the big data algorithm is a non-sparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix. In contrast to them, we used a Hybrid QCNN model with a high level of accuracy, i.e., 0.905. The researchers in [26] demonstrated that, by using quantum computing, the time required to train a deep restricted Boltzmann machine was reduced and the learning results were better than classical computing. As a result, the optimization of the underlying objective function improved significantly. In addition, quantum computer techniques have been demonstrated to efficiently handle some problems that are intractable on ordinary, classical computers. Furthermore, the quantum method demonstrated may efficiently train entire Boltzmann machines and multilayer, fully connected models for which there are no efficient classical counterparts. In [43], [PERSON] and his colleague created a minimalist malware classifier using cross-validation. The accuracy obtained was comparable with the standard Machine Learning algorithms. The authors in [27] showed that quantum Machine Learning algorithms exponentially increased their speed, opposite the classical models, performing their tasks in logarithmic time both with the number of vectors and their dimension. This study was conducted on supervised and unsupervised quantum Machine Learning algorithms for cluster assignment. In [44], the researchers proposed a mix of software used to discover the most common hashes and n-grams between benign and malicious software and a quantum search method. The latter will be utilized to find the desired hash value by searching through every permutation of the entangled key and value pairs. This eliminates the need to recompute hashes for a set of n-grams. The authors in [45] proposed a federated learning approach aimed to detect malware in IoT devices, by modeling the network traffic of several real IoT devices affected by  
Figure 11: The wrong classification of sample Dowgin (6 afbe9d4c41 fd46a3c516a9203 dc4393) misclassified as Trusted with 100%, where we can identify really small yellow areas are in the middle part of the image, obtained with the model built using the Hybrid-QCNN network. malware. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) were exploited by the authors. The researchers in [46] compared the performance obtained from 26 state-of-the-art pre-trained Convolutional Neural Network models in Android malware detection, by also including the performance obtained by large-scale learning with the SVM and RF classifiers. The model obtaining the best performance was the EfficientNet-B4 one. The authors in [47] proposed a hybrid multi-level deep learning model, formed by unsupervised and supervised DL models, to encode X86 malware binary files and classify them. It uses similarity matrices to detect relevant similarity patterns between input samples. Several studies tried to propose methods for malicious malware family detection. More specifically, the authors in [48] presented a solution that is based on the BIRCH clustering algorithm.