Efficiently Optimizing the Quantum Value of Bell Inequalities using Batched Gradient Descent
The research developed a Batched Gradient Descent (BGD) optimizer to efficiently find lower bounds for quantum values of Bell inequalities, overcoming limitations of traditional methods like the see-saw method. BGD uses direct tensor contraction and GPU parallelization to handle large problems quickly, successfully proving quantum advantages in specific multi-agent coordination games.