The rate of heart morbidity and heart mortality increases significantly which affect the global public health and world economy. Early prediction of heart disease is crucial for reducing heart morbidity and mortality. This paper proposes two quantum machine learning methods i.e. hybrid quantum neural network and hybrid random forest quantum neural network for early detection of heart disease. The methods are applied on the Cleveland and Statlog datasets. The results show that hybrid quantum neural network and hybrid random forest quantum neural network are suitable for high dimensional and low dimensional problems respectively. The hybrid quantum neural network is sensitive to outlier data while hybrid random forest is robust on outlier data. A comparison between different machine learning methods shows that the proposed quantum methods are more appropriate for early heart disease prediction where 96.43% and 97.78% area under curve are obtained for Cleveland and Statlog dataset respectively.

翻译：心脏发病率和死亡率显著上升，严重影响了全球公共卫生和世界经济。早期预测心脏病对于降低发病率和死亡率至关重要。本文提出了两种量子机器学习方法，即混合量子神经网络（hybrid quantum neural network）和混合随机森林量子神经网络（hybrid random forest quantum neural network），用于早期检测心脏病。这些方法应用于Cleveland和Statlog数据集。结果表明，混合量子神经网络和混合随机森林量子神经网络分别适用于高维和低维问题。混合量子神经网络对异常数据敏感，而混合随机森林对异常数据具有鲁棒性。不同机器学习方法的比较显示，所提出的量子方法更适合早期心脏病预测，在Cleveland和Statlog数据集上分别获得了96.43%和97.78%的曲线下面积。