伤残保险的量子支持向量回归
Quantum support vector regression for disability insurance
摘要：
我们提出了一个混合的经典-量子方法建模转移概率在健康和残疾保险。将逻辑失能起始概率的建模表示为支持向量回归问题。使用量子特征映射，数据被映射到属于量子特征空间的量子态，其中相关联的核由量子态之间的内积确定。这个量子核可以在量子计算机上有效地估计。我们在IBM Yorktown量子计算机上进行实验，将模型拟合到瑞典保险公司的残疾初始数据。
英文摘要：
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.
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