# Quantum Feature Extraction for THz Multi-Layer Imaging

arXiv: https://arxiv.org/abs/2207.09285 · HTML: https://mlsanity.com/paper/2207.09285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications](https://mlsanity.com/paper/2205.09115/) (2022)
- [Quantum Transfer Learning for Wi-Fi Sensing](https://mlsanity.com/paper/2205.08590/) (2022)
- [Learning to Learn Quantum Turbo Detection](https://mlsanity.com/paper/2205.08611/) (2022)

## Abstract (arXiv)

A learning-based THz multi-layer imaging has been recently used for contactless three-dimensional (3D) positioning and encoding. We show a proof-of-concept demonstration of an emerging quantum machine learning (QML) framework to deal with depth variation, shadow effect, and double-sided content recognition, through an experimental validation.