Quantum Machine Learning: Prospects and Challenges
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Abstract
Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning, holding the potential to revolutionize computational paradigms. This paper provides an overview of the prospects and challenges associated with QML. We explore how quantum algorithms, such as quantum support vector machines and quantum neural networks, promise exponential speedup in solving certain computational tasks. However, QML also faces significant obstacles, including the need for error correction, hardware limitations, and the integration of quantum and classical systems. The interplay between quantum principles and machine learning concepts presents exciting opportunities for addressing complex problems in fields like cryptography, optimization, and data analysis. This paper discusses the current state of QML research and offers insights into its future directions.
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