Document Type

Conference Paper

Disciplines

2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING

Publication Details

https://ieeexplore.ieee.org/document/10188314/keywords#keywords

T. Lisas and R. De Fréin, "Quantum Classifiers for Video Quality Delivery," 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Gothenburg, Sweden, 2023, pp. 448-453,

doi: 10.1109/EuCNC/6GSummit58263.2023.10188314.

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.

DOI

doi: 10.1109/EuCNC/6GSummit58263.2023.10188314

Funder

SFI and TU Dublin

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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