Quantum enhanced probing of multilayered-samples

TL;DR

This paper demonstrates a quantum sensing method using Quantum Optical Coherence Tomography combined with a genetic algorithm to accurately reconstruct multilayered sample structures, overcoming artifacts and echoes.

Contribution

It introduces a full theoretical model and data processing algorithm that improve the accuracy of quantum-based multilayered sample imaging.

Findings

Successful extraction of sample morphology from interferograms

Effective distinction between real interfaces, artifacts, and echoes

Validation through experimental data with controlled wavelength variation

Abstract

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.