Quantum Machine Learning for Coherent Multi-Population Mortality Forecasting: A Variational Quantum Circuit Approach

DOI

10.2991/978-94-6239-801-6_127How to use a DOI?

Keywords

Mortality Forecasting; Quantum Machine Learning; Variational Quantum Circuits; Multi-Population Modeling

Abstract

This is crucial in predicting the value of pension liabilities and longevity risks, as they require proper forecasts of mortality. In the traditional stochastic models of mortality there are parametric assumptions of a linear nature that do not adequately represent non-linear integrated dependencies across non homogenous populations. This paper explores a new coherent mortality forecasting system, Quantum Machine Learning (QML), or Variational Quantum Circuits (VQC), as a framework. This paper discusses the issue of whether VQC architectures could be effective to model asymmetric cross-country mortality correlations using simulated mortality data of the United States, Spain and Singapore. The architecture music proposed uses a 16-qubit architecture, which was decided on after the following trade-off between efficiency and representing three different populations with their respective age-cohort characteristics. The empirical findings indicate that the model VQC has a Mean Absolute Percentage Error (MAPE) of 3.9% in out-of-sample forecasting which are statistically significant gains compared with their classical counterparts. These results make VQC a promising auxiliary model of complex actuarial risk model, but promotion of practical application is limited by present-day computer limitations.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yaoming Liang
PY  - 2026
DA  - 2026/10/23
TI  - Quantum Machine Learning for Coherent Multi-Population Mortality Forecasting: A Variational Quantum Circuit Approach
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 1225
EP  - 1235
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_127
DO  - 10.2991/978-94-6239-801-6_127
ID  - Liang2026
ER  -