Quantum Circuit Learning to Compute Option Prices and Their Sensitivities

Abstract

Quantum circuit learning is applied to computing option prices and their sensitivities. The advantage of this method is that a suitable choice of quantum circuit architecture makes it possible to compute the sensitivities analytically by applying parameter-shift rules. We expect our numerical result to pave the way for using quantum machine learning for option pricing.