Quantum Physics

    

Quantum-Enhanced Machine Learning Models for Pricing Climate-Linked Financial Derivatives in Emerging Markets

Authors: Christ Abnoosian

Climate change exacerbates financial uncertainties in emerging markets, where economies are particularly vulnerable to environmental disruptions like droughts, floods, and extreme weather events. Traditional models for pricing climate-linked derivatives, such as catastrophe (CAT) bonds and weather-indexed insurance, often fail to capture the non-linear, high-dimensional nature of climate risks. This paper proposes a quantum-enhanced machine learning (QEML) framework integrating Quantum Amplitude Estimation (QAE), Variational Quantum Eigensolvers (VQE), and Quantum Support Vector Machines (QSVM) with classical techniques like Gaussian Process Regression and deep neural networks. Evaluations on datasets from Brazil, India, and South Africa show up to 35% improved pricing accuracy and 60% faster computation versus classical methods. This approach advances sustainable finance in climate-vulnerable regions.

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[v1] 2026-01-05 20:41:57

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