Quantum Advantage in Machine Learning: A Comparative Study of Quantum and Classical Algorithms
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Abstract
Quantum computing holds the promise of revolutionizing various fields, including machine learning, by leveraging quantum phenomena to perform computations beyond the capabilities of classical computers. In this paper, we present a comparative study of quantum and classical algorithms in the context of machine learning tasks. We examine the advantages of quantum algorithms, such as quantum parallelism and entanglement, and their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization. Through theoretical analysis and empirical experiments, we demonstrate the strengths and limitations of quantum machine learning algorithms, highlighting their potential for achieving quantum advantage in certain scenarios. Additionally, we discuss the challenges and opportunities for integrating quantum computing into machine learning pipelines and outline future research directions in this rapidly evolving field.
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