A computational quantum chemistry platform utilizing the Variational Quantum Eigensolver (VQE) algorithm to simulate molecular ground-state energy surfaces and evaluate optimal catalytic reaction pathways. Bridges quantum circuit simulation with classical machine learning models for high-throughput materials discovery.

Built with PennyLane and Qiskit, incorporating an indexed Hamiltonian database to benchmark noisy quantum emulator convergence against classical Scikit-learn regressors. Features parameter-shift gradient computation, ansatz optimization, and an interactive Streamlit visualization suite for exploring potential energy curves.