Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.

中文翻译：

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一种改进的用于恶意代码检测的轻量级量子卷积神经网络


量子神经网络充分利用了量子计算和经典神经网络各自的优势，为人工智能的发展提供了新的路径。在本文中，我们提出了一种改进的轻量级量子卷积神经网络 （QCNN），它包含高可扩展性和参数化的量子卷积层和具有量子比特复用的量子池化电路，有效地利用量子系统的计算优势来加速经典的机器学习任务。实验结果表明，该 QCNN 在 DataCon2020、Ember 和 BODMAS 上的分类准确率（精度，F1-score）分别提高到 96.65%（94.3%、96.74%）、92.4%（91.01%、92.53%）和 95.6%（91.99%，95.78%），表明该 QCNN 具有较强的鲁棒性以及良好的恶意代码检测泛化性能，对网络空间安全具有重要意义。