This content, based on the QAOA topic, will be updated regularly.
1. Different
ㅤ | AI for Quantum | Quantum for AI |
目标 | AI改进QC | QC改进AI |
效果 | 量子硬软件和算法优化 | 加速AI训练和优化 |
方向 | 量子线路设计、噪声建模、误差改进 | 增强优化模型泛化能力，减少计算资源 |
问题 | 如何针对QC开发AI模型，需要选取什么信息，以构造适合的模型。模型是否通用。 | 在NISQ设备运行算法对结果与模拟结果有一定偏差。设计的量子算法能否实际应用。 |
1.1 AI for Quantum
借助人工智能技术的优势去优化和改进量子计算的硬件、算法设计和应用等。
目标：
提升量子算法在真实量子硬件上的可实用性。
应用场景：
- 量子线路：将所设计的量子线路（算法）利用AI进行优化，降低算法复杂度。部分任务可通过AI生成量子线路。
- 量子硬件：借助AI优化在含噪量子计算机的门操作精度，降低误差等。还可用AI改进量子算法用于构建更多数量的逻辑量子比特。
- 模拟量子系统：在经典计算机上模拟一定规模的量子系统，通过模拟噪声环境去研究量子算法的有效性。
研究领域：
- 图神经网络GNN：
- 量子物理关系建模：Kong, L., Feng, J., Liu, H., Tao, D., Chen, Y., & Zhang, M. (2024). Mag-gnn: Reinforcement learning boosted graph neural network. Advances in Neural Information Processing Systems, 36.
- 量子架构（线路拓扑）搜索：He, Z., Zhang, X., Chen, C., Huang, Z., Zhou, Y., & Situ, H. (2023). A GNN-based predictor for quantum architecture search. Quantum Information Processing, 22(2), 128.
- 强化学习：
- 通用量子控制：Niu, M. Y., Boixo, S., Smelyanskiy, V. N., & Neven, H. (2019). Universal quantum control through deep reinforcement learning. npj Quantum Information, 5(1), 33.
- Zhang, X. M., Wei, Z., Asad, R., Yang, X. C., & Wang, X. (2019). When does reinforcement learning stand out in quantum control? A comparative study on state preparation. npj Quantum Information, 5(1), 85.
- 生成对抗网络GAN：
- Borras, K., Chang, S. Y., Funcke, L., Grossi, M., Hartung, T., Jansen, K., ... & Vallecorsa, S. (2023, February). Impact of quantum noise on the training of quantum generative adversarial networks. In Journal of Physics: Conference Series (Vol. 2438, No. 1, p. 012093). IOP Publishing.
- Kao, P. Y., Yang, Y. C., Chiang, W. Y., Hsiao, J. Y., Cao, Y., Aliper, A., ... & Lin, Y. C. (2023). Exploring the advantages of quantum generative adversarial networks in generative chemistry. Journal of Chemical Information and Modeling, 63(11), 3307-3318.
1.2 Quantum for AI
借助量子计算的优势去加速或改进人工智能技术。
目标：
发挥量子优势去提升AI性能，使得能处理更复杂的问题。
应用场景：
- 机器学习：大规模数据处理、优化传统ML模型。
- 优化问题：组合优化等。
- 量子神经网络：以量子为主的机器学习架构，或具有自动优化的全量子架构。
研究领域：
- 变分量子算法如QAOA、VQA
- 量子版本经典机器学习算法如QSVM
- Rebentrost, P., Mohseni, M., & Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical review letters, 113(13), 130503.
- Lloyd, S., & Weedbrook, C. (2018). Quantum generative adversarial learning. Physical review letters, 121(4), 040502.
- 大规模数据：HHL线性方程求解，Grover搜索算法。
- Duan, B., Yuan, J., Yu, C. H., Huang, J., & Hsieh, C. Y. (2020). A survey on HHL algorithm: From theory to application in quantum machine learning. Physics Letters A, 384(24), 126595.
- Grassl, M., Langenberg, B., Roetteler, M., & Steinwandt, R. (2016, February). Applying Grover’s algorithm to AES: quantum resource estimates. In International Workshop on Post-Quantum Cryptography (pp. 29-43). Cham: Springer International Publishing.
- Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., ... & Zoufal, C. (2024). Challenges and opportunities in quantum optimization. Nature Reviews Physics, 1-18.
- Sankar, K., Scherer, A., Kako, S., Reifenstein, S., Ghadermarzy, N., Krayenhoff, W. B., ... & Yamamoto, Y. (2024). A benchmarking study of quantum algorithms for combinatorial optimization. npj Quantum Information, 10(1), 64.
2. QAOA & AI
Blekos, K., Brand, D., Ceschini, A., Chou, C. H., Li, R. H., Pandya, K., & Summer, A. (2024). A review on quantum approximate optimization algorithm and its variants. Physics Reports, 1068, 1-66.
3. QGNN
Ceschini, A., Mauro, F., De Falco, F., Sebastianelli, A., Verdone, A., Rosato, A., ... & Ullo, S. L. (2024). From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks. arXiv preprint arXiv:2408.06524.