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Transforming Modern Computing With Quantum and AI: Vision and Challenges
Abstract
Modern computing systems are undergoing rapid transformation through theoretical advances and technical innovations. This article explores the technological and application trends in quantum-driven artificial intelligence (AI) innovations. The authors explore the mathematical frameworks underlying AI and quantum computing systems, with a particular focus on the role of algebraic topology in quantum circuit optimization and error correction. From neural networks to transformers, they investigate how AI architectures are reshaping computational capabilities, such as in healthcare, autonomous systems, and real-time computing. They highlight key hardware advances such as 3D stacked memory, neuromorphic chips, and quantum computing integration. They identify key challenges and limitations by focusing on ethical considerations, computation constraints, and scaling issues. This article looks ahead at research in quantum and AI, highlighting emerging technologies, potential breakthroughs and emerging trends, and spotlighting technological convergence and research trajectories.
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