Quantum Computing and Artificial Intelligence Convergence: Foundations for Scientific Innovation

Review Article

Abstract

The convergence of quantum computing and artificial intelligence (AI) has emerged as a transformative paradigm with the potential to revolutionize scientific discovery across diverse domains, including materials science, pharmaceutical research, and computational chemistry. While classical computing and conventional AI have significantly accelerated data-driven research, they often face limitations in solving highly complex optimization, simulation, and molecular modelling problems due to computational constraints. Quantum computing introduces fundamentally new computational capabilities based on quantum mechanical principles such as superposition, entanglement, and quantum interference, enabling efficient exploration of complex solution spaces that are intractable for classical computers. When integrated with AI techniques, these capabilities provide powerful frameworks for accelerating knowledge discovery, predictive modelling, and decision-making in scientific research. This review presents the fundamental principles underlying Quantum-AI convergence, focusing on the foundational concepts of quantum computing, artificial intelligence, quantum machine learning, hybrid quantum-classical computation, and quantum optimization. The paper examines how these principles collectively establish a new computational paradigm capable of addressing scientific challenges with improved efficiency and scalability. By emphasizing theoretical foundations rather than application-specific implementations, this review provides researchers with a comprehensive understanding of the core concepts driving Quantum-AI convergence and its significance in enabling future scientific innovation.