Portfolio optimisation and trade evaluation are among the most computationally demanding challenges in financial markets. Accurately forecasting portfolio performance and simulating pricing strategies require processing vast amounts of historical data, market trends and running probabilistic scenarios. Traditional computational methods, while powerful, struggle to efficiently handle the complexity and scale of modern financial instruments, such as call options and futures. The limitations of classical computing result in slower trade evaluations, suboptimal simulations and an inability to react to market changes in real time. Financial institutions need faster, more accurate, and scalable methods to analyse complex trading strategies, optimise portfolios and simulate multiple possible outcomes simultaneously. Current approaches often rely on brute-force methods or approximations, which fail to capture the full range of potential market dynamics. As financial markets grow increasingly volatile and data-driven, there is an urgent need for a computational breakthrough that allows institutions to process and simulate trades with unprecedented speed and accuracy.