Hybrid LLM-GNN Model to Enhance the Efficiency of ADAPT-QAOA for Quantum Circuit Optimization - Demo

Combinatorial optimization problems are central to applications such as logistics and network design, yet they become increasingly difficult for classical algorithms as problem size grows.

Hybrid quantum–classical methods like QAOA (Quantum Approximate Optimization Algorithm) offer a promising alternative. Its adaptive variant improves flexibility by dynamically constructing circuits, but still faces two major challenges:

Efficient circuit structure generation
- Stable and effective parameter initialization
- Existing approaches typically treat these problems separately, limiting generalization and performance—especially on weighted graphs.

This project introduces a unified generative–predictive framework that:
- Encodes graph structure via embeddings (NetLSD, FEATHER, GNNs)
- Uses transformer models (nanoGPT, LLaMA) to autoregressively generate circuits
- Jointly models circuit structure and parameters in a single sequence

The result is:
- Faster convergence
- Improved approximation quality
- Reduced circuit depth with maintained performance

My source code is in: https://github.com/mrzaizai2k/Adapt-LLM
Hashtag: #quantumphysics #qaoa #llm
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Thanks For Watching
✅LIKE ✅SHARE ✅ SUBSCRIBE
✅If you've got any suggestions for future projects, let me know in the comments section.
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Catch me on the web with the links below:

📞Phone/Zalo: +84 32 762 0475
📧 Email: chibao24.12.1999@gmail.com
☎️Telegram: @Chibao_mrzaizai2k
💻 GitHub: https://github.com/mrzaizai2k
Upwork/ LinkedIn/ Kaggle: https://bit.ly/Mrzaizai2k_AIchannel
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
If my YouTube videos, GitHub, Kaggle projects help you,
consider helping me out by BUYING ME A COFFEE!
https://bit.ly/Mrzaizai2k_AIchannel

Видео Hybrid LLM-GNN Model to Enhance the Efficiency of ADAPT-QAOA for Quantum Circuit Optimization - Demo канала Mrzaizai2k - AI