  In this paper, we investigate how to reduce the number of measurement
configurations needed for sufficiently precise entanglement quantification.
Instead of analytical formulae, we employ artificial neural networks to predict
the amount of entanglement in a quantum state based on results of collective
measurements (simultaneous measurements on multiple instances of the
investigated state). This approach allows us to explore the precision of
entanglement quantification as a function of measurement configurations. For
the purpose of our research, we consider general two-qubit states and their
negativity as entanglement quantifier. We outline the benefits of this approach
in future quantum communication networks.
