Machine Learning and Quantum Intelligence for Health Data Scenarios
It addresses challenges in high-dimensional or limited-quality healthcare datasets, but appears incremental as it focuses on feasibility and performance assessment of existing quantum methods in a new domain.
This paper tackles the problem of applying quantum machine learning to healthcare data, specifically for heart disease prediction and COVID-19 detection, by exploring quantum kernel methods and hybrid quantum-classical networks to potentially surpass classical approaches.
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.