Age Prediction Using Quantum Fourier Transform and Convolutional Neural Network

This paper presents the utilization of quantum computing in conjunction with deep learning, which constitutes a novel approach to surmounting the constraints of conventional methods in age prediction. This proposed method aspires to substantially enhance the efficiency and reduce the computational time of age prediction models by harnessing the inherent traits of quantum computing, such as superposition and entanglement. The development of quantum feature encoding techniques facilitates the representation of classical age-related data in quantum form, enabling more effective processing and analysis. Furthermore, implementing a hybrid classical-quantum architecture supplemented by quantum-enhanced classical algorithms highlights the synergistic relationship between classical and quantum computing paradigms. A comprehensive performance assessment has been carried out against conventional deep learning techniques. This paper demonstrates the current potential of quantum fourier transform and convolutional neural network in age prediction and lays a robust foundation for future advancements in quantum-enhanced applications across various domains.