Hybrid Quantum-Classical Machine Learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging Sensor Data-Reference-Cited by-同舟云学术

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Hybrid Quantum-Classical Machine Learning for Near Real-time Space to Ground Communication of ISS Lightning Imaging Sensor Data

Published:2023-03-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)
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Author:

Fadli Samih¹,Rawal Bharat S¹

Affiliation:

1. Capitol Technology University,Computer Science Department

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10099037/10098906/10099338.pdf?arnumber=10099338

Reference37 articles.

1. Quantum Technologies for UAS (QTech);grabbe;NASA Quantum Artificial Intelligence Laboratory (QuAIL) AIAA Aviation Conference,2019

2. RSS SSM/I Ocean Product Grids 3-Day Average from DMSP F11 netCDF [indicate subset used];wentz;Data set available online from the Huntsville Alabama U S A NASA Global Hydrometeorology Resource Center DAAC,2012

3. Modeling and simulation of space-based entanglement distribution;chahine;NASA Glenn Research Center,2021

4. LIS/OTD Gridded Lightning Climatology Data Collection;cecil;Data set available online from the Huntsville Alabama U S A NASA Global Hydrometeorology Resource Center DAAC,2014

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