Quadratic Speedup

Quadratic speedup in computation refers to algorithms achieving a performance improvement proportional to the square of the input size compared to classical methods. Current research focuses on achieving this speedup in diverse areas, including linear regression, game theory (specifically zero-sum games), financial modeling (option pricing), and machine learning (recommendation systems and active learning), often leveraging quantum algorithms, photonic computing, or novel optimization techniques like modified multiplicative weight updates. These advancements offer significant potential for accelerating computationally intensive tasks across various scientific disciplines and practical applications, leading to more efficient solutions for complex problems.