Link prediction is a fundamental problem in network science, aiming to forecast missing and future connections within a graph. Traditional approaches on classical graph theory and often struggle with complex networks. Quantum graphs, incorporating quantum features such as superposition and entanglement, offer a more powerful framework for link prediction. By using quantum concept to represent graph edges and vertices. This study explores the application of quantum graphs to link prediction, focusing on how entangled edges and dynamic weights enhance predictive capabilities. Additionally, we discuss the practical implementation of quantum link prediction in real-world networks.