Home > Published Issues > 2022 > Volume 13, No. 2, April 2022 >
JAIT 2022 Vol.13(2): 198-202
doi: 10.12720/jait.13.2.198-202

Towards Building a Facial Identification System Using Quantum Machine Learning Techniques

Philip Easom-McCaldin 1, Ahmed Bouridane 1, Ammar Belatreche 1, and Richard Jiang 2
1. Dept. of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
2. Dept. of Computing and Communications, Lancaster University, Lancaster, UK

Abstract—In the modern world, facial identification is an extremely important task, in which many applications rely on high performing algorithms to detect faces efficiently. Whilst commonly used classical methods of SVM and k-NN may perform to a good standard, they are often highly complex and take substantial computing power to run effectively. With the rise of quantum computing boasting large speedups without sacrificing large amounts of much needed performance, we aim to explore the benefits that quantum machine learning techniques can bring when specifically targeted towards facial identification applications. In the following work, we explore a quantum scheme which uses fidelity estimations of feature vectors in order to determine the classification result. Here, we are able to achieve exponential speedups by utilizing the principles of quantum computing without sacrificing large proportions of performance in terms of classification accuracy. We also propose limitations of the work and where some future efforts should be placed in order to produce robust quantum algorithms that can perform to the same standard as classical methods whilst utilizing the speedup performance gains.
Index Terms—facial identification, quantum computing, quantum machine learning

Cite: Philip Easom-McCaldin, Ahmed Bouridane, Ammar Belatreche, and Richard Jiang, "Towards Building a Facial Identification System Using Quantum Machine Learning Techniques," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 198-202, April 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.