Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach

ArXi:2604.19832v1 Announce Type: cross In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capital allocation, and regulatory compliance. In this work, we employ the Black-Scholes-Merton (BSM) framework not as an end in itself, but as a controlled benchmark environment in which to rigorously assess the capabilities of quantum machine learning methods.