Quantum Machine Learning for High-Dimensional Data Processing
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Abstract
Quantum computing holds immense potential for revolutionizing machine learning by tackling computationally intensive problems in high-dimensional data spaces. This paper explores the integration of quantum algorithms with machine learning techniques, focusing on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Variational Quantum Circuits (VQC). We present experimental results demonstrating the advantages of quantum computing in tasks such as classification, clustering, and optimization. The study highlights challenges in quantum hardware limitations, algorithmic efficiency, and practical implementations, offering future directions for hybrid quantum-classical machine learning models.
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References
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