Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258298 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 12 [Article No.:] 216 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-9
Publisher: 
MDPI, Basel
Abstract: 
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 are 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.
Subjects: 
disability insurance
machine learning
support vector machines
quantum computing
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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