Variational Quantum Circuits for Molecular Classification Using Graph Neural Network

The study explores the integration of quantum-enhanced feature mapping and optimization within Graph Neural Networks (GNNs) for molecular classification tasks. Utilizing IBM's Qiskit platform, we implement Variational Quantum Circuits (VQCs) to transform classical molecular features into quantum states, capturing complex correlations through quantum entanglement. Additionally, the Quantum Approximate Optimization Algorithm (QAOA) is employed for hyperparameter tuning to enhance model convergence and performance. Comparative analyses against traditional machine learning models demonstrate the superiority of the quantum-enhanced GNN, highlighting the potential of quantum computing in advancing molecular property prediction.