用量子支持向量机提升恶意软件检测，对比经典方法表现
Quantum Computing Methods for Malware Detection
- 将量子核方法引入支持向量机，构建量子分类模型
- 在本地模拟器与真实量子硬件上测试，验证了可行性
- 揭示量子计算实际应用中的电路编译和任务大小限制
本文探讨量子计算在恶意软件检测中的潜力，重点研究量子支持向量机（QSVM）相对于传统SVM的性能表现。使用公开的便携式可执行文件（PE）二进制数据集进行分类实验。通过Qiskit SDK在本地模拟器及IBM量子计算机上实现并评估了基于不同特征映射的量子核QSVM算法。模拟器与量子硬件上的实验结果揭示了量子计算在处理大规模恶意软件检测任务时的行为与性能表现。研究总结了通过Qiskit接口使用量子硬件的实际经验，详细描述了遇到的关键问题及解决方案，包括提交至IBM量子系统时电路未正确编译、因一次性提交所有电路导致超出最大作业规模限制等问题。
原文摘要 · Abstract (English)
In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum Support Vector Machine (QSVM) algorithm compared to SVM. A publicly available dataset containing raw binaries of Portable Executable (PE) files was used for the classification. The QSVM algorithm, incorporating quantum kernels through different feature maps, was implemented and evaluated on a local simulator within the Qiskit SDK and IBM quantum computers. Experimental results from simulators and quantum hardware provide insights into the behavior and performance of quantum computers, especially in handling large-scale computations for malware detection tasks. The work summarizes the practical experience with using quantum hardware via the Qiskit interfaces. We describe in detail the critical issues encountered, as well as the fixes that had to be developed and applied to the base code of the Qiskit Machine Learning library. These issues include missing transpilation of the circuits submitted to IBM Quantum systems and exceeding the maximum job size limit due to the submission of all the circuits in one job.
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