Quantum Support Vector Regression for Disability Insurance

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.

Quantum support vector machines for classification and regression on a trapped-ion quantum computer

“…In recent years, there has been remarkable progress in quantum hardware (de Leon et al 2021), opening the path for the implementation of NISQ algorithms. Previous studies on quantum kernels have explored the use of various quantum hardware platforms, such as superconducting qubits (Havlíček et al 2019;Djehiche and Löfdahl 2021;Heredge et al 2021;Peters et al 2021;Wang et al 2021;Hubregtsen et al 2022;Krunic et al 2022), trapped-ion qubits (Moradi et al 2022), Gaussian Boson Sampling (Schuld et al 2020;Giordani et al 2023), neutral-atom qubits (Albrecht et al 2023), and nuclear-spin qubits (Kusumoto et al 2021). Owing to quantum decoherence and the noise of quantum gates, one can typically perform a limited number of quantum operations on NISQ devices.…”