We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data is mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

翻译：我们提出一种混合的古典-量子方法,用以模拟健康和残疾保险的过渡概率。逻辑残疾初始概率模型是一个辅助矢量回归问题。使用量子特征图,数据被映射为属于量子特征空间的量子状态,其中相关的内核由量子状态之间的内产物决定。这个量子内核可以在量子计算机上有效估算。我们在IBM Yorktown量子计算机上进行实验,将模型与瑞典一家保险公司的残疾初始数据相匹配。