Quantum Amplitude Estimation Methods for Option Pricing

Quantum computing enables the resolution of certain problems more quickly and simply compared to classical computers or even supercomputers while utilizing lower power: they are particularly powerful in situations where is required the analysis of different combinations simultaneously. Specifically, in the case of Option Pricing, it allows for the replacement of traditional models like Monte Carlo Simulation, delivering results much faster by leveraging Amplitude Estimation. This thesis aims to analyze the characteristics of different methods of implementing Amplitude Estimation and determine which one best suits the analyzed problem. The current state of the art of quantum computer is predominantly experimental, with several obstacles to most useful applications. One major challenge to widespread quantum computing adoption is the qubit’s fragile state. Existing technologies can only maintain the information in a quantum state for short periods, limiting the duration of calculations in practice. This experiment was conducted on simple call options; however, the true improvement stemming from the use of quantum computing for option pricing lies primarily in cases involving multiple underlying assets or more complex structures for payoff calculation. Employing this method, as opposed to traditional ones, will enable better risk management and investment decision-making.