A General Framework for Constructing Local Hidden-state Models to Determine the Steerability
This machine learning framework uses batch sampling and gradient optimization to build an optimal Local Hidden-State (LHS) model for any quantum state under arbitrary measurements. It determines steerability by minimizing a loss function that measures the difference between the LHS assemblages and actual quantum assemblages, certifying unsteerability when the loss is near zero.