  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.
