PQKLP: Projected Quantum Kernel based Link Prediction in Dynamic Networks

Link prediction in dynamic networks finds new or future links based on the previously seen structure of the network. Its study is crucial to comprehending network evolution and its effects on individual nodes. Accuracy and efficiency of link prediction on dynamic networks are the two aspects research. We present Projected Quantum Kernel-based Link Prediction (PQKLP), a quantum-enhanced feature-based framework for solving link prediction problems in dynamic networks. According to our study, the Projected Quantum Kernel has not been utilized in the field of link prediction. Thus, we propose this method that combines the disciplines of social networks and quantum computing. We employed high-dimensional Hilbert spaces to enhance the prediction data in this model, which otherwise we only have access to via inner products provided by measurements. Such enhancement leads to better prediction results from machine learning-based link prediction techniques. We trained six classical machine learning models and their quantum-enhanced counterparts based on the enhanced features generated by the Projected Quantum Kernel (PQK) technique. The proposed model outperforms traditional link prediction methods, classical machine learning approaches, and current state-of-the-art methods on five well-known dynamic network datasets, as per the results of four performance metrics.