# Machine Learning and Quantum Intelligence for Health Data Scenarios

arXiv: https://arxiv.org/abs/2410.21339 · HTML: https://mlsanity.com/paper/2410.21339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)

## Abstract (arXiv)

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.