This thesis investigates the potential of quantum generative adversarial networks (qGANs) for option pricing in the nancial industry. Traditional numerical methods such as Monte Carlo simulations require considerable computational eort, and with the sheer number of options to be priced, more e‑cient computational methods are needed. By integrating quantum algorithms, we demonstrate quadratically better error convergence in theory compared to Monte Carlo simulations. To achieve this, the distribution of the price of the underlying asset at maturity is loaded into a quantum state using qGANs. Furthermore, the evaluation of these distributions and ultimately the option price of actual stock options is carried out on real quantum hardware and compared with conventional methods. Our main contribution is demonstrating the exibility and scalability of qGANs for loading arbitrary distributions into quantum states with high precision. Presenting a signicant step towards the use of quantum information in the nancial industry on current quantum hardware.     «

This thesis investigates the potential of quantum generative adversarial networks (qGANs) for option pricing in the nancial industry. Traditional numerical methods such as Monte Carlo simulations require considerable computational eort, and with the sheer number of options to be priced, more e‑cient computational methods are needed. By integrating quantum algorithms, we demonstrate quadratically better error convergence in theory compared to Monte Carlo simulations. To achieve this, the distribu...     »