Comparative Evaluation of Quantum Machine Learning and Classical AI Models for Optimization Problems
Abstract
Quantum computing is emerging as a promising field for accelerating AI computations. This paper presents a comparative study of quantum machine learning (QML) models and classical AI algorithms in solving complex optimization problems. We evaluate the performance of quantum neural networks, quantum support vector machines, and classical deep learning techniques on combinatorial optimization tasks. The results highlight the current limitations and advantages of QML, providing insights into its future potential in AI-driven decision-making.
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