Efficient Quantum Process Tomography via Riemannian Gradient Descent

A data-driven approach for the constrained optimization task of quantum process tomography, utilizing advanced stochastic objective optimizers and considering the quantum process as residing in a Stiefel manifold to perform local updates that respect the geometry.