Learning-based quantum state reconstruction using biased quantum state distributions

We derive the Dirichlet concentration parameters for mixtures of Haar-random pure states that recover mean purities equal to standard measures, demonstrating how tailored distributions attain appreciable performance advantages in machine-learning-based and Bayesian quantum state reconstruction.