  Artefacts in quantum-mimic Optical Coherence Tomography are considered
detrimental because they scramble the images even for the simplest objects.
They are a side effect of autocorrelation which is used in the quantum
entanglement mimicking algorithm behind this method. Interestingly, the
autocorrelation imprints certain characteristics onto an artefact - it makes
its shape and characteristics depend on the amount of dispersion exhibited by
the layer that artefact corresponds to. This unique relationship between the
artefact and the layer's dispersion can be used to determine Group Velocity
Dispersion (GVD) values of object layers and, based on them, build a
dispersion-contrasted depth profile. The retrieval of GVD profiles is achieved
via Machine Learning. During training, a neural network learns the relationship
between GVD and the artefacts' shape and characteristics, and consequently, it
is able to provide a good qualitative representation of object's dispersion
profile for never-seen-before data: computer-generated single dispersive layers
and experimental pieces of glass.
