Quantum Machine Learning Algorithms for Drug Discovery: A Comparative Study

Abstract

Quantum computing promises transformative impacts on computational chemistry and drug discovery. This paper presents a comparative analysis of quantum machine learning algorithms applied to molecular property prediction and compound screening. The study implements quantum support vector machines (QSVM) and variational quantum classifiers (VQC) on benchmark drug datasets using quantum simulators. Results indicate that quantum algorithms can achieve comparable or superior accuracy with fewer computational resources than classical counterparts, especially in handling high-dimensional data. The findings provide valuable insights into the potential of quantum ML to accelerate pharmaceutical innovation.