  Quantum Computing (QC) has gained immense popularity as a potential solution
to deal with the ever-increasing size of data and associated challenges
leveraging the concept of quantum random access memory (QRAM). QC promises
quadratic or exponential increases in computational time with quantum
parallelism and thus offer a huge leap forward in the computation of Machine
Learning algorithms. This paper analyzes speed up performance of QC when
applied to machine learning algorithms, known as Quantum Machine Learning
(QML). We applied QML methods such as Quantum Support Vector Machine (QSVM),
and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks.
Due to the access limitations of real quantum computers, the QML methods were
implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow
Quantum. We evaluated the performance of QML in terms of processing speed and
accuracy and finally, compared with its classical counterparts. Interestingly,
the experimental results differ to the speed up promises of QC by demonstrating
higher computational time and lower accuracy in comparison to the classical
approaches for SSC attacks.

Quantum Physics;Software Engineering