Abstract

This collection explores quantum neural networks (QNNs) and quantum machine learning (QML), detailing their theoretical foundations, architectures, and diverse applications. QNNs harness quantum mechanics for enhanced computational power, showing promise in image processing, finance, and medical analysis. Key topics include variational quantum algorithms and quantumenhanced graph neural networks. While significant potential exists for quantum advantage, the field faces substantial challenges like barren plateaus and hardware limitations. Overcoming these requires ongoing innovation in algorithms and quantum hardware for practical Quantum Artificial Intelligence.

Keywords: Quantum Neural Networks (QNNs); Quantum Machine Learning (QML); Variational Quantum Algorithms (VQAs); Quantum Deep Learning; Quantum Computing; Image Processing; Finance; Medical Image Analysis; Quantum Hardware Challenges; Quantum Advantage