Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading

TL;DR

This paper introduces a quantum algorithm for autocallable options pricing, featuring an improved integration-based exponential amplitude loading technique that significantly reduces circuit depth and enhances computational efficiency.

Contribution

The paper presents a novel quantum algorithm with a new amplitude loading method that decreases circuit depth, validated through simulations and empirical convergence analysis.

Findings

50x reduction in T-depth for payoff computation

Successful quantum simulation on HPC hardware

Demonstrated convergence to classical pricing estimates

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 an approximately 50x 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.