Optimization Techniques of Quantum Neural Network for Image Classification

Accurate image classification holds immense importance across various applications, including computer vision and medical diagnostics. Traditional approaches often struggle to handle complex datasets and achieve high classification accuracy. To overcome these limitations, researchers are turning to quantum neural networks, which leverage the principles of quantum mechanics. However, the current state of quantum computing, marked by insufficient qubits for practical applications, render the performance of quantum neural networks in image classification tasks with notable inaccuracies and time inefficiencies, emphasizing the need to employ and evaluate optimization techniques in order to address these challenges. The ultimate objective is to enhance classification accuracy through this novel approach. This research endeavors to fulfill the growing demand for more precise image classification algorithms through an exploration of the capabilities of quantum neural networks with the best optimization algorithms for improving classification accuracy.