Supply Chain Malware Detection via Classical and Quantum Kernel Methods in Embedded Systems

The increasing complexity of modern software supply chains has led to heightened security concerns, particularly regarding vulnerabilities hidden within source code. Classical machine learning (ML) algorithms have been extensively used to detect these vulnerabilities, but their effectiveness is often constrained by computational limitations and feature-extraction challenges. Recent advancements in quantum machine learning (QML) offer a promising alternative by leveraging quantum computing's superior computational power and parallelism. This study presents a performance comparison between classical ML and QML algorithms for detecting software supply chain vulnerabilities in source code. We evaluate key metrics, including precision, accuracy, recall, and computational efficiency, to assess their effectiveness in real-world scenarios. Our results demon-strate the potential advantages of QML in handling complex security threats, though practical implementation challenges remain. This research contributes to the growing field of quantum cybersecurity and provides insights into the future adoption of QML in securing software supply chains.