  We simulate a feasible experimental scenario that reconstruct the density
matrix of an electronic qubit via spin-polarized tunneling currents. Our system
is composed of a single level quantum dot, attached to ferromagnetic leads. By
properly rotating the leads magnetizations we can select the spin component to
be detected. Counting the number of tunneling events taken in four different
magnetic alignments we find the Stokes parameters, that are used as a train
data set into a supervised machine learning algorithm. The predicted model
allows the complete knowledge of the open system density matrix, including both
probability amplitudes and relative phases.
