Quantum Computing in Artificial Intelligence Algorithms, Applications and Future Scope

Abstract

Quantum Computing and Artificial Intelligence are two rapidly evolving technologies that have the potential to transform modern computational systems. Traditional artificial intelligence models often face limitations related to computational complexity, scalability, and processing of high-dimensional data. Quantum computing, based on principles such as superposition and entanglement, offers a new computational paradigm capable of improving processing efficiency and solving complex problems more effectively.
This paper presents a comprehensive review of the integration of quantum computing with artificial intelligence, commonly referred to as Quantum Artificial Intelligence (QAI). The study discusses fundamental concepts of quantum computing, major quantum machine learning algorithms, hybrid quantum–classical approaches, and their applications in various domains including healthcare, cybersecurity, finance, image processing, and natural language processing.
The paper also highlights important challenges such as hardware instability, noise, decoherence, limited qubit availability, and scalability issues that currently restrict large-scale implementation of quantum systems. Furthermore, future research directions including quantum error correction, scalable quantum architectures, and real-world deployment of Quantum Machine Learning (QML) models are discussed.
The review concludes that hybrid quantum–classical approaches provide a promising and practical solution for enhancing artificial intelligence systems in the current NISQ era and may play a significant role in the future development of intelligent computational technologies.