用量子机器学习提升心病与新冠预测准确率
Machine Learning and Quantum Intelligence for Health Data Scenarios
- 结合量子核方法与量子经典混合网络建模
- 在医疗数据上实现比传统方法更优的分类性能
- 适合对量子计算与医疗AI交叉研究感兴趣者
量子计算的发展为数据科学带来了新可能，能有效应对复杂、数据密集型问题。传统机器学习在高维或低质量医疗数据上表现受限，而量子机器学习利用量子叠加与纠缠特性，增强模式识别与分类能力，有望超越经典方法。本文探讨量子机器学习在医疗场景的应用，聚焦量子核方法与混合量子-经典网络在心脏病预测和新冠疫情检测中的可行性与性能评估。
原文摘要 · Abstract (English)
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.
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