Quantum Feature Extraction for THz Multi-Layer Imaging
arXiv:2207.09285v12 citationsh-index: 35
Originality Incremental advance
AI Analysis
This addresses contactless 3D positioning and encoding for imaging applications, but appears incremental as it builds on existing quantum machine learning approaches.
The paper tackled the problem of depth variation, shadow effect, and double-sided content recognition in THz multi-layer imaging by demonstrating a quantum machine learning framework, achieving a proof-of-concept experimental validation.
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.