Variation Quantum Eigensolver (VQE) for Molecular Simulation and Drug Discovery in Traditional Medicine

Abstract

Drug discovery in traditional medicine is constrained by the structural complexity and diversity of bioactive compounds, often rendering classical computational approaches inadequate for accurate molecular simulation. The Variational Quantum Eigensolver (VQE), a hybrid quantum-classical algorithm, offers a scalable method to approximate molecular ground-state energies on near-term quantum hardware. This study examines the application of VQE for simulating the electronic structures of bioactive molecules derived from traditional medicinal plants and natural products. By integrating quantum variational circuits with classical optimization routines, VQE demonstrates improved accuracy in predicting electronic energies, molecular properties, and potential binding affinities relative to classical methods. The manuscript further explores hybrid quantum-AI frameworks for candidate prioritization and drug discovery, assessing computational efficiency, noise mitigation strategies, and algorithmic scalability. Results suggest that VQE can accelerate the early stages of drug development, providing a pathway for rational design of therapeutics grounded in traditional medicine while highlighting limitations imposed by current quantum hardware.