Power Law Amplitude Estimation for Option Pricing with NISQ Devices

Abstract

The flourishing development of quantum computing has brought breakthroughs to many classical algorithms, particularly in fields like quantum finance, where quantum computers are employed for estimating the pricing of financial options. Compared to traditional Monte Carlo methods, quantum amplitude estimation (AE) algorithms offer exponential acceleration in estimating the pricing of financial options. However, the execution of quantum AE algorithms is limited by the quantum depth available in current Noisy Intermediate Scale Quantum (NISQ) devices. To explore more applications of quantum algorithms with NISQ devices, a power law AE algorithm for option pricing with NISQ devices is proposed and simulated on Qiskit to validate the theoretical performance. Compared with other AE algorithms, the power law AE algorithm achieves an overall accuracy improved by about 13.12%.