In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks. We implement two models of Quantum Machine Learning algorithms, and we compare them to classical models for the classification of a dataset composed of malicious and benign executable files. We try to optimize our algorithms based on methods found in the literature, and analyze our results in an exploratory way, to identify the most interesting directions to explore for the future.

翻译：在恶意软件检测的背景下，机器学习被广泛用于泛化识别新型恶意软件。然而，已有研究表明，机器学习模型可能被欺骗，或在面对从未见过的恶意软件时出现泛化问题。我们探究了量子算法在分类任务中的潜在优势。我们实现了两种量子机器学习算法模型，并将其与经典模型进行对比，用于对包含恶意和良性可执行文件的数据集进行分类。我们尝试基于文献中的方法优化算法，并以探索性方式分析结果，以确定未来最值得研究的方向。