Malware detection is an important topic of current cybersecurity, and Machine
Learning appears to be one of the main considered solutions even if certain
problems to generalize to new malware remain. In the aim of exploring the
potential of quantum machine learning on this domain, our previous work showed
that quantum neural networks do not perform well on image-based malware
detection when using a few qubits. In order to enhance the performances of our
quantum algorithms for malware detection using images, without increasing the
resources needed in terms of qubits, we implement a new preprocessing of our
dataset using Grayscale method, and we couple it with a model composed of five
distributed quantum convolutional networks and a scoring function. We get an
increase of around 20 \% of our results, both on the accuracy of the test and
its F1-score.

通过使用灰度处理方法与由五个分布式量子卷积网络和评分函数组成的模型相结合，我们实现了对数据集的新预处理，以提高基于图像的恶意软件检测的性能，而不增加量子位所需的资源，结果表明测试的准确率和 F1 评分分别提高了约 20%。