Support Vector Machines on Noisy Intermediate Scale Quantum Computers
This work addresses quantum algorithm efficiency for telecommunications applications, though it appears incremental with specific optimizations rather than paradigm-shifting.
The authors tackled the challenge of implementing support vector machines on noisy intermediate-scale quantum computers by developing optimized preprocessing and circuit designs that reduce quantum circuit depth, achieving classification of linearly separable two-dimensional datasets without requiring quantum tomography.
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.