Quantum Classifiers for Video Quality Delivery

Abstract

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion state and the kernel method performs 6.1% and 10.1% better for the medium and high congestion states. Empirical evidence suggests that when the SVC is trained on a very low amount of data, the classification accuracy varies significantly depending on the quality of the training data, however, the variance in classification accuracy of quantum models is significantly lower. Classical video QoD classifiers benefit from the quantum data embedding techniques. They learn better decision regions using less training data.