Advancements in Quantum Machine Learning: A New Paradigm for Data Processing
Abstract
Quantum computing is poised to revolutionize data processing by harnessing the principles of superposition and entanglement to perform computations at unprecedented speeds. This paper examines the emerging field of Quantum Machine Learning (QML) and its potential impact on solving complex computational problems. We explore various QML algorithms, including quantum neural networks and variational quantum circuits, highlighting their advantages over classical approaches. The study also discusses the challenges of quantum hardware limitations and noise interference. Experimental results demonstrate the feasibility of QML in enhancing pattern recognition, optimization, and cryptographic security.
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