Machine Learning and Quantum Intelligence for Health Data Scenarios

TL;DR

This paper explores how quantum machine learning techniques can improve healthcare data analysis, specifically in heart disease prediction and COVID-19 detection, by leveraging quantum properties to enhance pattern recognition.

Contribution

It introduces the application of quantum kernel methods and hybrid quantum-classical networks to healthcare problems, demonstrating their potential advantages over classical methods.

Findings

Quantum methods show promising accuracy improvements.

Feasibility of quantum algorithms in healthcare scenarios.

Potential for quantum-enhanced pattern recognition.

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.