We have published our latest work on arXiv: “Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach.”

In this paper, we study whether current quantum hardware can already be used for practical financial modeling. We propose a fully quantum approach to option pricing based on Quantum Neural Networks and implement it end-to-end on real quantum devices. The model is benchmarked within the Black–Scholes–Merton model framework, which allows us to evaluate accuracy in a controlled setting.

The results show that even with today’s NISQ hardware, it is possible to approximate option pricing functions with a good level of precision. The broader goal is not to replace classical models, but to understand where quantum approaches can start to add value, especially for more complex models such as stochastic volatility or interest rate frameworks.