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Quantum Machine Learning

Machine Learning and Quantum Intelligence for Health Data Scenarios

arXiv

Authors: Sanjeev Naguleswaran

Year

2024

Paper ID

37600

Status

Preprint

Abstract Read

~2 min

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Abstract

The advent of quantum computing has opened new possibilities in data science, offering unique capabilities for addressing complex, data-intensive problems. Traditional machine learning algorithms often face challenges in high-dimensional or limited-quality datasets, which are common in healthcare. Quantum Machine Learning leverages quantum properties, such as superposition and entanglement, to enhance pattern recognition and classification, potentially surpassing classical approaches. This paper explores QML's application in healthcare, focusing on quantum kernel methods and hybrid quantum-classical networks for heart disease prediction and COVID-19 detection, assessing their feasibility and performance.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
The advent of quantum computing has opened new possibilities in data science, offering unique capabilities for addressing complex, data-intensive problems.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.21339 [2] arXiv https://arxiv.org/abs/2410.21339 [3] Publisher https://arxiv.org/abs/2410.21339

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