Derivative Pricing and Credit Risk in a Regime Switching Economy Using Quantum Amplitude Estimation

Quantum computers are not yet capable of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper, we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks and more amenable to potential quantum computational advantages. The models we study are based on a regime-switching volatility model driven by a Markov chain with observable states. We investigate algorithms to estimate credit risk and option pricing on a gate-based quantum computer. These models bring us closer to realistic market settings, thus bringing quantum computing closer to the realm of practical applications. We demonstrate the accuracy of the state preparation circuits outlined for each model using the IBM_torino and Honeywell H1 processors.