Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

We introduce a purely geometric formulation for two different measures addressed to
quantify the entanglement between different parts of a tripartite qubit system. Our approach …

Classification of entanglement is an important problem in quantum resource theory. In this
paper we discuss an embedding of this problem in the context of topological quantum field …

The quantum mechanics formalism introduced new revolutionary concepts challenging our
everyday perceptions. Arguably, quantum entanglement, which explains correlations that …

While optics and mechanics are two distinct branches of physics, they are connected. It is
well known that the geometrical/ray treatment of light has direct analogies to mechanical …

In this contribution we present a concise introduction to quantum entanglement in
multipartite systems. After a brief comparison between bipartite systems and the simplest …

We derive an approximate equation for the time evolution of the natural occupation numbers
for fermionic systems. The evolution of such numbers is connected with the symmetry …

We classify four qubit states under SLOCC operations, that is, we classify the orbits of the
group $\mathrm {\mathop {SL}}(2,\mathbb {C})^ 4$ on the Hilbert space $\mathcal {H} …

We study a class of optimization problems including matrix scaling, matrix balancing,
multidimensional array scaling, operator scaling, and tensor scaling that arise frequently in …

The dependence of the (quasi-) saturation of the generalized Pauli constraints on the pair
potential is studied for ground states of few-fermion systems. For this, we consider spinless …