  Quantum sensing exploits quantum phenomena to enhance the detection and
estimation of classical parameters of physical systems and biological entities,
particularly so as to overcome the inefficiencies of its classical
counterparts. A particularly promising approach within quantum sensing is
Quantum Optical Coherence Tomography which relies on non-classical light
sources to reconstruct the internal structure of multilayered materials.
Compared to traditional classical probing, Quantum Optical Coherence Tomography
provides enhanced-resolution images and is unaffected by even-order dispersion.
One of the main limitations of this technique lies in the appearance of
artifacts and echoes, i.e. fake structures that appear in the coincidence
interferogram, which hinder the retrieval of information required for
tomography scans. Here, by utilizing a full theoretical model, in combination
with a fast genetic algorithm to post-process the data, we successfully extract
the morphology of complex multilayered samples and thoroughly distinguish real
interfaces, artifacts, and echoes. We test the effectiveness of the model and
algorithm by comparing its predictions to experimentally-generated
interferograms through the controlled variation of the pump wavelength. Our
results could potentially lead to the development of practical high-resolution
probing of complex structures and non-invasive scanning of photo-degradable
materials for biomedical imaging/sensing, clinical applications, and materials
science.
