Exploring Quantum Neural Networks: A Hybrid AI Model for Enhanced Learning

Abstract

This paper delves into the emerging field of Quantum Neural Networks (QNNs), presenting a hybrid Artificial Intelligence (AI) model that integrates the principles of quantum computing with classical neural networks to enhance learning and computational efficiency. By leveraging quantum phenomena such as superposition and entanglement, QNNs offer the potential to process vast amounts of data simultaneously and solve complex problems more efficiently than traditional AI systems. The study explores the architecture of QNNs, their theoretical underpinnings, and the unique advantages they present in various applications, from optimization tasks to complex pattern recognition.