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Quantum Support Vector Regression for Disability Insurance

Author

Listed:
  • Boualem Djehiche

    (Department of Mathematics, KTH Royal Institute of Technology, Lindstedtsvägen 25, 114 28 Stockholm, Sweden)

  • Björn Löfdahl

    (Department of Mathematics, KTH Royal Institute of Technology, Lindstedtsvägen 25, 114 28 Stockholm, Sweden)

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.

Suggested Citation

  • Boualem Djehiche & Björn Löfdahl, 2021. "Quantum Support Vector Regression for Disability Insurance," Risks, MDPI, vol. 9(12), pages 1-9, December.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:12:p:216-:d:693148
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    References listed on IDEAS

    as
    1. Maria Schuld, 2019. "Machine learning in quantum spaces," Nature, Nature, vol. 567(7747), pages 179-181, March.
    2. Christiansen, Marcus C. & Denuit, Michel & Lazar, Dorina, 2012. "The Solvency II square-root formula for systematic biometric risk," LIDAM Reprints ISBA 2012002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Christiansen, Marcus C. & Denuit, Michel M. & Lazar, Dorina, 2012. "The Solvency II square-root formula for systematic biometric risk," Insurance: Mathematics and Economics, Elsevier, vol. 50(2), pages 257-265.
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