Keywords
Quantum Machine Learning (QML)
Hybrid Quantum-Classical Computing
Computational Advantage
NISQ Era Challenges
Categories
How to Cite
Abstract
The convergence of Artificial Intelligence (AI) and Quantum Computing (QC) marks a potentially transformative technological frontier. This study explores the synergistic integration of these fields, analyzing the landscape of opportunities and challenges arising from their combination. Quantum computing offers the promise to enhance AI by overcoming computational bottlenecks and enabling novel algorithms, particularly within machine learning and optimization. This analysis reveals significant opportunities in areas like accelerated machine learning, tackling intractable problems, and processing quantum data. However, substantial challenges currently impede progress, primarily due to limitations in Noisy Intermediate-Scale Quantum (NISQ) hardware, algorithmic complexities in demonstrating practical quantum advantage, and practical hurdles in implementation and interdisciplinary expertise. Despite these challenges, the synergistic potential of AI-QC integration remains immense, promising a paradigm shift in computational capabilities with the continued advancement of both fields, ultimately poised to revolutionize science, industry, and society
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