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 nonclassical 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 postprocess 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 noninvasive scanning of photodegradable materials for biomedical imaging/sensing, clinical applications, and materials science.
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