Artificial intelligence, machine learning, and deep learning in cloud, edge, and quantum computing: A review of trends, challenges, and future directions

Abstract

With an emphasis on current trends, obstacles, and future directions, this research offers a thorough analysis of the intersection of cloud, edge, and quantum computing with artificial intelligence (AI), machine learning (ML), and deep learning (DL). Cloud computing provides scalable infrastructure as AI-driven applications grow quickly, and edge computing moves processing power closer to data sources to improve real-time analytics and reduce latency. Intelligent applications in the healthcare, autonomous systems, and Internet of Things industries can only be made possible by the integration of AI and ML in these environments. Applications that require low latency can't run in cloud environments, and edge computing can't run smoothly on limited power and processing capacity. Concerns about privacy and security are still present in both paradigms, particularly in decentralized edge environments. Even though quantum computing is still in its infancy, it has the potential to transform artificial intelligence (AI) by providing solutions to issues those classical systems are unable to handle. However, errors in hardware scalability and error correction arise. This review delves into new approaches such as early quantum algorithms for AI, hybrid cloud-edge architectures, and federated learning for distributed AI.