https://github.com/bolunzhangzbl/hybrid_qae_e2e
A hybrid QAE for End-to-End Communication Systems
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.2%) to scientific vocabulary
Repository
A hybrid QAE for End-to-End Communication Systems
Basic Info
- Host: GitHub
- Owner: BolunZhangzbl
- Language: Python
- Default Branch: main
- Size: 598 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Hybrid QAE E2E
This repository contains the implementation of a Hybrid Quantum Autoencoder (QAE) for End-to-End communication. The model integrates classical deep learning techniques with quantum computing to enhance the performance of communication systems.
Table of Contents
Requirements
The following packages are required to run the code:
pennylanetensorflow
Installation
Option 1 (Basic Installation):
bash
pip install tensorflow==2.15.0 pennylane
Option 2 (For Lightning Plugins with lightning.qubit, lightning.gpu):
bash
pip install tensorflow==2.15.0
pip install pennylane pennylane-lightning pennylane-lightning-gpu --upgrade
pip install custatevec-cu12
Train the model
bash
python main.py --train --channel_type rayleigh
Retrain the model
bash
python main.py --train --retrain --channel_type rayleigh
Test the model
bash
python main.py --test --channel_type rayleigh
bash
python main.py --test --num_qiubits 7 --channel_type rayleigh
Run the Inference Time Comparisons (Execution Time)
bash
python test_runtime.py
GitHub Events
Total
- Push event: 10
- Public event: 1
Last Year
- Push event: 10
- Public event: 1