Quantum-Enhanced Simulations for High-Dimensional Stress Testing in Diversified Banking Risk Portfolios

Abstract

The complications of diversified banking portfolios have compounded the difficulties of properly rating systemic risk in extreme market regimes. Conventional stress testing techniques are not always able to achieve a high degree of predictive and computational efficiency because of the combinatorial explosion of high-dimensional risk factors. This paper introduces a quantum-enhanced stress testing theory in high-dimensional representations with the help of quantum algorithms like Quantum Amplitude Estimation (QAE) and quantum-classical quantum-classical systems, to effectively simulate tail-risk conditions. The framework brings together credit, market, liquidity, and operational risk dimensions to show faster convergence in Monte Carlo-type simulations without compromising on the accuracy of extreme-event forecasts. Comparative experiments to classical ones show large computational speedup, and better scalability to the complexly correlated portfolios. The results indicate that quantum computing could provide a revolutionary instrument in banking risk management, allowing regulators and institutions to more effectively predict systemic weaknesses and allocate capital efficiently in the face of extreme stress events. Further efforts will focus on quantum-native machine learning models to implement adaptive scenario generation and monitor portfolio resilience in real-time.