This project introduces the fundamental concepts of antithetic maps and indices to achieve optimal variance reduction by maximizing negative correlation while preserving the underlying probability distribution. By establishing a direct geometric connection to optimal transport theory, the research provides rigorous theoretical guarantees for practical applications like Monte Carlo integration, function approximation, and stochastic optimization.