Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.

中文翻译：

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用于恶意代码检测的混合量子集成学习模型

量子计算作为一种具有并行计算能力和高信息承载能力的新型计算模式，引起了研究者的广泛关注。集成学习是机器学习中经常使用的一种有效策略，用于提高弱分类器的性能。目前，由于量子电路深度、量子噪声、量子编码方法等因素，量子分类器的分类性能还不够理想。为此，本文将集成学习思想与量子分类器相结合，设计了一种量子分类器。新颖的混合量子机器学习模型。首先，我们运行经典机器学习中的Stacking方法，实现高纬度数据的降维，同时保证数据特征的有效性。其次，我们将Bagging方法和贝叶斯超参数优化方法应用于量子支持向量机（QSVM）、量子K最近邻（QKNN），变分量子分类器（VQC）。再次，采用投票的方法将QSVM、QKNN、VQC的预测结果进行融合作为最终结果。我们将混合量子集成机器学习模型应用于恶意代码检测。实验结果表明，该模型的分类精度（accuracy、F1-score）提高到了98.9%（94.5%、94.24%）。结合量子计算的加速和更高的精确率，可以有效应对恶意代码日益增长的趋势，对网络空间安全具有重要意义。