WeChat Mini Program
Old Version Features

Experimental Progress Using Quantum Binary Waveforms and Immediate Idler Detection Techniques for Remote Sensing

RADAR SENSOR TECHNOLOGY XXVIII(2024)

Penn State Univ

Cited 0|Views3
Abstract
Two current hurdles of quantum RADAR/LiDAR technology are i.) The use of joint measurement techniques, whereby the idler remains in a delay line or a quantum memory to be measured later with the returning signal, and ii.) The difficulty in creating high photon flux signals for long range sensing. Our measurement and detection protocol using immediate-idler-detection (IID) helps to alleviate both of these issues. We present our recent experimental data from characterizing our proof-of-concept IID quantum LiDAR system and show that similar to delay line approaches, we achieve strong correlation even in extremely noisy channels where the noise level exceeds the signal strength by as much as one hundred times. We have found that even in very lossy channels, the integration time remains extremely short and roughly the same value even as the noise is increased. We also show preliminary results through foggy free space channels and found positive correlation SNR even when the visibility was as low as 15%. Our measurement and detection protocol was designed to align closely with classical RADAR and LiDAR signal processing to better align the quantum and classical sensor regimes and allows for the potential to scale upwards and produce higher photon-flux signals from multiple photon pair sources.
More
Translated text
Key words
quantum radar,quantum lidar,experiment,remote sensing,signal processing,matched filter,quantum sensing,quantum science
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined