Reproduce “Quantum Amplitude Estimation for Catastrophe Insurance Tail-Risk Pricing: Empirical Convergence and NISQ Noise Analysis” on OpenResearch
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About this paper
Alexis Kirke
Alexis Kirke empirically evaluates Quantum Amplitude Estimation (QAE) for pricing catastrophe insurance tail risk, demonstrating its superior convergence (1.5–2.5× lower RMSE) over classical Monte Carlo for black-box loss distributions. The work also identifies discretisation error and Near-Term Intermediate-Scale Quantum (NISQ) noise as primary practical limitations, with low noise levels degrading QAE advantage by 66×.
Key insights
- QAE provides a genuine algorithmic advantage in the 'oracle model' where loss distributions are accessed as black-box simulators, confirming its theoretical quadratic speedup over classical Monte Carlo.
- Classical variance-reduced methods (e.g., Importance Sampling, Quasi-Monte Carlo) can outperform QAE when analytical access to the distribution's CDF is available, highlighting the importance of fair baseline comparisons.
- Discretisation error (from binning continuous loss distributions) and even low levels of NISQ noise are significant practical bottlenecks, severely limiting QAE's near-term effectiveness.
Summary by alphaXiv.
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