We study the partially ordered set of equivalence classes of quantum measurements endowed with the post-processing partial order. The post-processing order is fundamental as it enables to compare measurements by their intrinsic noise and it gives grounds to define the important concept of quantum incompatibility. Our approach is based on mapping this set into a simpler partially ordered set using an order preserving map and investigating the resulting image. The aim is to ignore unnecessary details while keeping the essential structure, thereby simplifying e.g. detection of incompatibility. One possible choice is the map based on Fisher information introduced by Huangjun Zhu, known to be an order morphism taking values in the cone of positive semidefinite matrices. We explore the properties of that construction and improve Zhu's incompatibility criterion by adding a constraint depending on the number of measurement outcomes. We generalize this type of construction to other ordered vector spaces and we show that this map is optimal among all quadratic maps.

We present results concerning decompositions of positive operators acting on finite-dimensional Hilbert spaces. Our motivation is the study of a generalized version of the SIC–POVM problem, which has applications to Quantum Information Theory. We relax some of the conditions in the SIC–POVM setting (the elements sum up to the identity, resp. the elements have unit rank), and we focus on equiangular decompositions (the elements of the decomposition should have the same length, and pairs of distinct elements should have constant angles). We characterize all such decompositions, comparing our results with the case of SIC–POVMs. We also generalize some existing Welch-type inequalities.

Projective Norms are a class of tensor norms that map from the input to the output spaces. These norms are useful for providing a measure of entanglement. Calculating the projective norms is an NP-hard problem, which creates challenges in computing due to the complexity of the exponentially growing parameter space for higher-order tensors. We develop a novel gradient descent algorithm to estimate the projective norm of higher-order tensors. The algorithm exhibits stable convergence to a minimum nuclear-rank decomposition of the given tensor in all our numerical experiments. We further extend the algorithm to symmetric tensors and to density matrices. We demonstrate the performance of our algorithm by computing the nuclear rank and the projective norm for both pure and mixed states and provide numerical evidence supporting these results.