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September 10, 2025

Quantum Machine Learning: Algorithms, Applications, and Limitations

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This paper explores quantum algorithms and applications in drug discovery and finance, highlighting challenges.

Quantum machine learning offers potential advantages over classical ML, enhancing problem-solving capabilities.
Key algorithms include quantum support vector machines and variational quantum classifiers to bridge quantum and classical techniques.
Theoretical foundations are outlined, emphasizing the interplay of quantum phenomena like superposition and entanglement.
There are notable limitations in QML, including hardware instability and challenges in quantum data encoding and interpretability.

Madouri et al. (2024) studied this question.

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September 10, 2025

View Full Paper

Ask AI

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Authors

This paper explores quantum algorithms and applications in drug discovery and finance, highlighting challenges.

Quantum machine learning offers potential advantages over classical ML, enhancing problem-solving capabilities.
Key algorithms include quantum support vector machines and variational quantum classifiers to bridge quantum and classical techniques.
Theoretical foundations are outlined, emphasizing the interplay of quantum phenomena like superposition and entanglement.
There are notable limitations in QML, including hardware instability and challenges in quantum data encoding and interpretability.

Madouri et al. (2024) studied this question.

View Full Paper

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Bookmark