Regularization of Riemannian optimization: Application to process tomography and quantum machine learning

Felix Soest, Konstantin Beyer, Walter T. Strunz

Gradient descent algorithms on Riemannian manifolds have recently been proposed as a practical tool for optimizing quantum channels. We add rank-penalizing regularization terms to…