Towards Building a Facial Identification System Using Quantum Machine Learning Techniques

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 classical methods of SVM and k-NN commonly used may perform a good standard, they are often highly complex take substantial computing power run effectively. With rise quantum boasting large speedups without sacrificing amounts much needed performance, we aim explore benefits that machine learning techniques can bring when specifically targeted towards applications. following work, scheme uses fidelity estimations feature vectors order determine classification result. Here, able achieve exponential by utilizing principles proportions performance terms accuracy. We also propose limitations work where some future efforts should be placed produce robust same standard as whilst speedup gains.