Toward a curated, living repository for Quantum Computing, Quantum Optimization, and Quantum AI.
This repository accompanies the paper
“New AI for Quantum Computing: From Quantum Optimization to Quantum Intelligence” (under submission to IEEE/CAA Journal of Automatica Sinica (JAS)).
It is a companion, awesome-style resource that curates taxonomies, representative systems, and references spanning:
- Quantum Computing Systems (QPU hardware & hybrid architectures)
- Quantum Optimization (VQE/QAOA, annealing, hybrid solvers)
- Quantum AI (QAI) ()
If you find it useful, please star ⭐ this repo and consider a citation.
- [2025-11-20] Repository initialized with structure, tables, and placeholders.
- [TBA] Camera-ready materials and extended references will be added after acceptance.
Quantum computing has entered a stage of hybrid intelligence, in which CPU–GPU–QPU resources are woven into unified computational fabrics for near–real-time quantum–classical workflows. Beyond pure algorithm execution, the field is rapidly moving toward learning-capable, self-optimizing quantum systems—from quantum optimization to quantum intelligence.
This repository tracks that evolution with structured categories, clean tables, and high-quality references, serving as a living index for researchers and practitioners.
| Title / System | Type | Venue / Year | Key Specs / Notes | Refs |
|---|---|---|---|---|
| IBM Condor | Superconducting QPU | — | 1,121 qubits; roadmap node | |
| Google Sycamore | Superconducting QPU | Nature 2019 | 53 qubits; supremacy experiments | |
| Quantinuum H-Series | Ion-trap QPU | — | High-fidelity gates | |
| IonQ Forte | Ion-trap QPU | — | Advanced optical addressing | |
| PsiQuantum Omega | Photonic QPU | Nature 2025 | Manufacturable photonics platform | |
| Microsoft Majorana-1 | Topological (proto) | — | Toward Majorana zero modes | |
| PSNC Hybrid Cluster | Hybrid system | — | Multi-user, multi-QPU/GPU/CPU | |
| IBM Q-Centric SC | Hybrid fabric | — | Qiskit Runtime; near-real-time | |
| NVIDIA CUDA-Q & NVQLink | SW + Interconnect | — | 400 Gb/s; < 4 μs latency |
| Method / Benchmark | Class | Venue / Year | Notes (problem class / scale) | Refs |
|---|---|---|---|---|
| VQE family | Variational | — | Chemistry / materials | |
| QAOA variants | Variational | — | Combinatorial optimization | |
| Quantum annealing | Annealing | — | Ising/QUBO; industrial cases | |
| Tensorized QML | QML | — | TN circuits, efficiency | |
| Hybrid solvers | Hybrid | — | CPU–GPU–QPU pipelines |
| System / Idea | Category | Venue / Year | Notes (LLM–QPU, agents, runtime) | Refs |
|---|---|---|---|---|
| LLM-driven QPU orchestration | Agentic runtime | — | Scheduling, prompting, CoT | |
| AI-assisted QEC / calibration | Control | — | Feedback/stability | |
| Quantum-enhanced learning | QML/Agent | — | Variational policies | |
| Foundation models for circuits | FM | — | Tokenization/encoding |
- Yonglin Tian
- Fei Lin
- Xinyu Liu
For questions, contact Yonglin Tian (yonglin.tian@ia.ac.cn) or Fei Lin (feilin@ieee.org).
If you would like to contribute, please open an Issue or Pull Request.
If you find this repository useful, please consider citing this paper:
@misc{lin2025_new_ai_for_qc,
title={New AI for Quantum Computing: From Quantum Optimization to Quantum Intelligence},
author={collaborators},
year={2025},
note={Under review at IEEE/CAA Journal of Automatica Sinica (JAS)},
url={https://github.com/Hub-Tian/Quantum-Intelligence},
}This project is licensed under the MIT License.