Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Towards Building a Facial Identification System Using Quantum Machine Learning Techniques
AU - Easom-Mccaldin, P.
AU - Bouridane, A.
AU - Belatreche, A.
AU - Jiang, R.
PY - 2022/4/30
Y1 - 2022/4/30
N2 - 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.
AB - 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.
KW - Facial identification
KW - Quantum computing
KW - Quantum machine learning
U2 - 10.12720/jait.13.2.198-202
DO - 10.12720/jait.13.2.198-202
M3 - Journal article
VL - 13
SP - 198
EP - 202
JO - Journal of Advances in Information Technology
JF - Journal of Advances in Information Technology
IS - 2
ER -