R-CCS Cafe 第283回（2026年1月13日）

Title:
Quantum Approaches to Financial Tail-Risk Estimation with Correlated Structure

Abstract:
Estimating rare but extreme events is a long-standing challenge in computational science, particularly when such events are driven by correlated fluctuations and cascade mechanisms rather than independent noise. In credit risk modeling, extreme portfolio losses arise from common shocks, heterogeneous responses, and contagion effects, making classical Monte Carlo simulations increasingly inefficient as the dimensionality and dependence increase. In this talk, I will present a quantum-computational framework for tail-risk estimation that builds on Quantum Amplitude Estimation (QAE) while focusing on structural feasibility rather than near-term quantum advantage. The core idea is to embed realistic dependence structures directly into quantum circuits, enabling coherent representation of shocks, default propagation, and loss accumulation. I will discuss how resource-aware quantum arithmetic can be used to aggregate losses, how circuit depth and qubit requirements scale with model complexity, and how preliminary circuit prototypes validate the logical consistency of the approach. The goal is to establish a concrete, analyzable pathway from realistic stochastic models to quantum implementations, and to assess the computational resources required on future quantum devices. More broadly, I will outline how the same framework applies to other rare event problems, positioning quantum computation as a modeling tool for complex financial systems.