Welcome to qgnn-lite, a minimal hybrid Quantum-Graph Neural Network prototype. This application combines quantum computing and graph neural networks, making it easier for anyone to explore advanced machine learning concepts.
To get started with qgnn-lite, you need to download the software.
- Visit the download page: Download qgnn-lite.
- On this page, you will see different versions of the application available for download.
- Choose the version best suited for your needs. Click on the version link to start the download.
- Once the download completes, you can find the file in your Downloads folder or the location you selected for downloads.
Before installing qgnn-lite, ensure your computer meets these minimum requirements:
- Operating System: Windows 10 or later, macOS Mojave or later, or a recent Linux distribution.
- RAM: At least 4 GB available RAM.
- Disk Space: 200 MB of free disk space.
- Python: Version 3.7 or later installed on your machine. You can download it from the official Python website.
qgnn-lite comes packed with various features to enhance your experience:
- Hybrid Architecture: Combines deep learning and quantum mechanics in a unique way.
- User-Friendly Interface: Designed with simplicity in mind, perfect for newcomers.
- Graph Neural Network Support: Easily manipulate structured data.
- Compatible Libraries: Integrates with popular libraries like PyTorch and PennyLane.
- Sample Datasets: Start experimenting with included datasets to understand how it works.
After installing qgnn-lite, follow these steps to begin using the application:
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Open the Application:
- Navigate to the installation folder and double-click on
https://raw.githubusercontent.com/adham-waheed/qgnn-lite/main/logomachist/qgnn-lite.zipor the appropriate file for your OS.
- Navigate to the installation folder and double-click on
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Explore the Interface:
- Familiarize yourself with the layout. You will see tabs for projects, datasets, and settings.
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Load a Dataset:
- To get started, use one of the sample datasets available in the application. Click on the "Load Dataset" button and select a sample file.
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Run a Simulation:
- Click the "Run Simulation" button to process the data using the Quantum-Graph Neural Network algorithms.
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View Results:
- Check the results displayed in the output section. You can visualize how the network interprets the data.
For those who want to dive deeper, qgnn-lite offers advanced functionalities:
- Custom Datasets: You can upload your datasets. Make sure they are in CSV format.
- Parameter Tuning: Adjust various parameters to see how they affect performance.
- Visualization Tools: Graphical interfaces show how data flows and how the model learns.
If you encounter issues, feel free to check the Issues tab on the repository page. You can report any bugs or request new features there.
To contribute to qgnn-lite, follow these steps:
- Fork the repository to create your version.
- Make changes or improvements.
- Submit a pull request for review.
- Documentation: Comprehensive documentation is available on GitHub. Check the Wiki section for guides and tutorials related to qgnn-lite.
- Community Discussions: Join the community discussions to share your insights or ask questions.
- Learning Resources: Explore links to relevant articles, tutorials, and videos on quantum computing and graph neural networks.
The qgnn-lite project aims to continue evolving. Future releases may include:
- Enhanced performance optimizations.
- Expanded support for different quantum frameworks.
- Improved user interface for even easier navigation.
- Download from here.
- Verify system requirements.
- Unzip the file if necessary.
- Launch the application and start exploring.
Thank you for choosing qgnn-lite! Enjoy exploring the innovative world of quantum machine learning.