Quantum Computing Methods for Malware Detection
Eliška Krátká, Aurél Gábor Gábris
TL;DR
This work investigates applying quantum kernel methods to malware detection via QSVM, comparing it to classical SVM on a publicly available PE dataset. The authors implement QSVM using Qiskit and run experiments on both local simulators and IBM quantum hardware, addressing practical issues such as circuit transpilation and job-size limits. Key findings show QSVM with feature maps like ZZ and ZZphi can match or exceed classical kernels in simulator experiments, while hardware trials reveal significant operational challenges but demonstrate feasibility with targeted fixes. The study contributes practical guidance for deploying QSVM in security contexts and highlights areas for future optimization, including hardware-aware circuit design and feature-map exploration. The potential impact is to inform security-oriented quantum ML workflows and to quantify real-world considerations when moving from simulation to quantum devices.
Abstract
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.