Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantum-classical models -- a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data re-uploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -- API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification -- 95-96% on API-Graph, 91-92% on AZ-Domain, and 77% on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.

翻译：经典机器学习（CML）在恶意软件分类领域已得到广泛研究。随着量子计算的出现，量子机器学习（QML）为改进恶意软件检测提供了范式转换的机遇，尽管其在该领域的应用仍基本处于探索阶段。本研究针对恶意软件分类任务，探究了两种混合量子-经典模型——量子多层感知器（QMLP）与量子卷积神经网络（QCNN）。两种模型均采用角度嵌入技术将恶意软件特征编码为量子态。QMLP通过全量子比特测量与数据重上传机制捕获复杂模式，而QCNN则借助量子卷积层与池化层减少活跃量子比特数量，从而实现更快的训练速度。我们在五个广泛使用的恶意软件数据集——API-Graph、EMBER-Domain、EMBER-Class、AZ-Domain与AZ-Class上，对两种模型在二分类与多分类任务中的性能进行了评估。实验结果显示：在二分类任务中，模型在API-Graph数据集上达到95-96%的准确率，在AZ-Domain数据集上达到91-92%，在EMBER-Domain数据集上达到77%；在多分类场景中，API-Graph数据集上的准确率范围为91.6-95.7%，AZ-Class数据集为41.7-93.6%，EMBER-Class数据集为60.7-88.1%。总体而言，QMLP在复杂多分类任务中表现优于QCNN，而QCNN则以牺牲部分准确率为代价提供了更高的训练效率。