Quantum support vector regression for disability insurance

Boualem Djehiche, Björn Löfdahl

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number216
Issue number12
StatePublished - Dec 2021


  • Disability insurance
  • Machine learning
  • Quantum computing
  • Support vector machines

ASJC Scopus subject areas

  • Accounting
  • Economics, Econometrics and Finance (miscellaneous)
  • Strategy and Management


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