Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading
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Abstract
We present a comprehensive quantum algorithm tailored for pricing autocallable options, offering a full implementation and experimental validation. Our experiments include simulations conducted on high performance computing (HPC) hardware, along with an empirical analysis of convergence to the classically estimated value. Our key innovation is an improved integration-based exponential amplitude loading technique that reduces circuit depth compared to state-of-the-art approaches. A detailed complexity analysis in a relevant setting shows a $\sim 50 x$ reduction in T-depth for the payoff component relative to previous methods. These contributions represent a step toward more efficient quantum approaches to pricing complex financial derivatives.