  Support vector machine algorithms are considered essential for the
implementation of automation in a radio access network. Specifically, they are
critical in the prediction of the quality of user experience for video
streaming based on device and network-level metrics. Quantum SVM is the quantum
analogue of the classical SVM algorithm, which utilizes the properties of
quantum computers to speed up the algorithm exponentially. In this work, we
derive an optimized preprocessing unit for a quantum SVM that allows
classifying any two-dimensional datasets that are linearly separable. We
further provide a result readout method of the kernel matrix generation circuit
to avoid quantum tomography that, in turn, reduces the quantum circuit depth.
We also derive a quantum SVM system based on an optimized HHL quantum circuit
with reduced circuit depth.
