Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading

2025-07-25

AI Summary

This work addresses the efficient quantum pricing of autocallable options. We propose a novel quantum algorithm integrating numerical integration, exponential amplitude loading, and quantum amplitude estimation. The key methodological innovation is an integral-based exponential amplitude loading technique, which substantially reduces circuit complexity: under typical parameter settings, the T-depth of the payoff function module is reduced by approximately 50× compared to state-of-the-art approaches. The full quantum circuit is implemented and validated for convergence on a high-performance quantum simulator, yielding high-precision quantum estimates of the option price. Experimental results demonstrate both robust convergence and superior computational efficiency. Our approach establishes a scalable technical pathway toward practical quantum pricing of complex path-dependent financial derivatives.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Quantum algorithm for pricing autocallable options efficiently
• Reduced circuit depth via exponential amplitude loading
• 50x T-depth reduction in payoff calculation

Innovation

• Quantum algorithm for autocallable options pricing
• Integration-based exponential amplitude loading
• 50x T-depth reduction in payoff component