  Recent developments have led to the possibility of embedding machine learning
tools into experimental platforms to address key problems, including the
characterization of the properties of quantum states. Leveraging on this, we
implement a quantum extreme learning machine in a photonic platform to achieve
resource-efficient and accurate characterization of the polarization state of a
photon. The underlying reservoir dynamics through which such input state
evolves is implemented using the coined quantum walk of high-dimensional
photonic orbital angular momentum, and performing projective measurements over
a fixed basis. We demonstrate how the reconstruction of an unknown polarization
state does not need a careful characterization of the measurement apparatus and
is robust to experimental imperfections, thus representing a promising route
for resource-economic state characterisation.
