初步

只是记录，不是教程（personal reading record）

Classical:

Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474

本文主要来自：

Tychola, K.A.; Kalampokas, T.; Papakostas, G.A. Quantum Machine Learning—An Overview. Electronics 2023, 12, 2379. https://doi.org/10.3390/electronics12112379

TIME	TYPE	ㅤ	ㅤ
2014	QSVM	Rebentrost, P.; Mohseni, M.; Lloyd, S. Quantum Support Vector Machine for Big Data Classification. Phys. Rev. Lett. 2014, 113, 130503.	大数据排序
2017	QML	Biamonte, J.; Wittek, P.; Pancotti, N.; Rebentrost, P.; Wiebe, N.; Lloyd, S. Quantum Machine Learning. Nature 2017, 549, 195–202.	ㅤ
2015	ML-ATTACK	Mozaffari-Kermani, M.; Sur-Kolay, S.; Raghunathan, A.; Jha, N.K. Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare. IEEE J. Biomed. Health Inform. 2015, 19, 1893–1905.	ㅤ
2021	ML-ATTACK	Qayyum, A.; Qadir, J.; Bilal, M.; Al-Fuqaha, A. Secure and Robust Machine Learning for Healthcare: A Survey. IEEE Rev. Biomed. Eng. 2021, 14, 156–180.	ㅤ
2019	ML-ATTACK	Wang, B.; Yao, Y.; Shan, S.; Li, H.; Viswanath, B.; Zheng, H.; Zhao, B.Y. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. In Proceedings of the IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 19–23 May 2019; pp. 707–723.	ㅤ
2016	SAL	da Silva, A.J.; Ludermir, T.B.; de Oliveira, W.R. Quantum Perceptron over a Field and Neural Network Architecture Selection in a Quantum Computer. Neural Netw. 2016, 76, 55–64.	基于叠加的ML架构
2022	QKNN	Li, J., Lin, S., Yu, K. et al. Quantum K-nearest neighbor classification algorithm based on Hamming distance. Quantum Inf Process 21, 18 (2022). https://doi.org/10.1007/s11128-021-03361-0	K近邻分类
2014	tree-classifier	Lu, S., Braunstein, S.L. Quantum decision tree classifier. Quantum Inf Process 13, 757–770 (2014). https://doi.org/10.1007/s11128-013-0687-5	ㅤ
2020	ㅤ	Adhikary, S., Dangwal, S. & Bhowmik, D. Supervised learning with a quantum classifier using multi-level systems. Quantum Inf Process 19, 89 (2020). https://doi.org/10.1007/s11128-020-2587-9	分类器，N维数据编码
2020	HQFSA	Chakraborty, S., Shaikh, S.H., Chakrabarti, A. et al. A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach. Appl Intell 50, 1775–1793 (2020). https://doi.org/10.1007/s10489-019-01604-3	特征选择
2018	ㅤ	Ciliberto, C.; Herbster, M.; Ialongo, A.D.; Pontil, M.; Rocchetto, A.; Severini, S.; Wossnig, L. Quantum Machine Learning: A Classical Perspective. Proc. R. Soc. A 2008, 474, 20170551.	讨论QML局限性，对比经典算法的优势
2018	QSVM	Dunjko, V.; Briegel, H.J. Machine Learning & Artificial Intelligence in the Quantum Domain: A Review of Recent Progress. Rep. Prog. Phys. 2018, 81, 074001.	应用QSVM
2015	ㅤ	Schuld, M.; Sinayskiy, I.; Petruccione, F. An Introduction to Quantum Machine Learning. Contemp. Phys. 2015, 56, 172–185.	处理大数据的量子机器学习算法
2018	ㅤ	Havenstein, C.; Thomas, D.; Chandrasekaran, S. Comparisons of performance between quantum and classical machine learning. SMU Data Sci. Rev. 2018, 1, 11.	经典和量子计算机上的性能比较。QSVM更具优势
2020	QNN, QSVM, QML	Mishra, N. et al. (2021). Quantum Machine Learning: A Review and Current Status. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_8	回顾众多QML算法
2016	ㅤ	McClean, J.R.; Romero, J.; Babbush, R.; Aspuru-Guzik, A. The Theory of Variational Hybrid Quantum-Classical Algorithms. New J. Phys. 2016, 18, 023023.	VQE改进
2021	QNN	Abbas, A.; Sutter, D.; Zoufal, C.; Lucchi, A.; Figalli, A.; Woerner, S. The Power of Quantum Neural Networks. Nat. Comput. Sci. 2021, 1, 403–409.	量子神经网络
2019	QCNN	Cong, I.; Choi, S.; Lukin, M.D. Quantum Convolutional Neural Networks. Nat. Phys. 2018, 15, 1273–1278	量子卷积神经网络
2020	QRNN	Bausch, J. Recurrent Quantum Neural Networks. arXiv 2020, arXiv:2006.14619.	量子递归神经网络
2021	QNN	Zhao, R.; Wang, S. A Review of Quantum Neural Networks: Methods, Models, Dilemma. arXiv 2021, arXiv:2109.01840.	回顾及介绍QNN模型
2022	QNN	Maronese, M., Destri, C. & Prati, E. Quantum activation functions for quantum neural networks. Quantum Inf Process 21, 128 (2022). https://doi.org/10.1007/s11128-022-03466-0	QNN 量子激活函数
2021	QNN	Cerezo, M., Sone, A., Volkoff, T. et al. Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nat Commun 12, 1791 (2021). https://doi.org/10.1038/s41467-021-21728-w	量子电路里的贫瘠高原
2018	QNN	McClean, J.R., Boixo, S., Smelyanskiy, V.N. et al. Barren plateaus in quantum neural network training landscapes. Nat Commun 9, 4812 (2018). https://doi.org/10.1038/s41467-018-07090-4	量子电路里的贫瘠高原
2019	~IBM	Havlíˇcek, V.; Córcoles, A.D.; Temme, K.; Harrow, A.W.; Kandala, A.; Chow, J.M.; Gambetta, J.M. Supervised Learning with Quantum-Enhanced Feature Spaces. Nature 2019, 567, 209–212.	量子增强特征空间-量子变分分类器和量子核估计器
2014	QNN	Schuld, M.; Sinayskiy, I.; Petruccione, F. The Quest for a Quantum Neural Network. Quantum Inf. Process. 2014, 13, 2567–2586	截至2014，讨论QNN模型
2019	QML	Schuld, M.; Killoran, N. Quantum Machine Learning in Feature Hilbert Spaces. Phys. Rev. Lett. 2019, 122, 040504.	希尔伯特空间-核方法（文章提到在QML领域很少被研究）
2021	QSVM	Vashisth, S.; Dhall, I.; Aggarwal, G. Design and Analysis of Quantum Powered Support Vector Machines for Malignant Breast Cancer Diagnosis. J. Intell. Syst. 2021, 30, 998–1013.	应用
2021	QML	Liu, Y., Arunachalam, S. & Temme, K. A rigorous and robust quantum speed-up in supervised machine learning. Nat. Phys. 17, 1013–1017 (2021). https://doi.org/10.1038/s41567-021-01287-z	量子核估计QKE，量子加速
2023	QML	Jerbi, S., Fiderer, L.J., Poulsen Nautrup, H. et al. Quantum machine learning beyond kernel methods. Nat Commun 14, 517 (2023). https://doi.org/10.1038/s41467-023-36159-y	QML模型比较，线性量子模型
2023	QNN	Moussa, C., Patel, Y.J., Dunjko, V. et al. Hyperparameter importance and optimization of quantum neural networks across small datasets. Mach Learn (2023). https://doi.org/10.1007/s10994-023-06389-8	小数据集，超参数重要性验证
2021	ㅤ	Acampora, G., Schiattarella, R. Deep neural networks for quantum circuit mapping. Neural Comput & Applic 33, 13723–13743 (2021). https://doi.org/10.1007/s00521-021-06009-3	专用于量子电路映射到实际机器上的神经网络，例IBM 127-Qubit 芯片
2023	ㅤ	Watkins, W.M., Chen, S.YC. & Yoo, S. Quantum machine learning with differential privacy. Sci Rep 13, 2453 (2023). https://doi.org/10.1038/s41598-022-24082-z	差分隐私QML
2021	QFL	Li, W., Lu, S. & Deng, DL. Quantum federated learning through blind quantum computing. Sci. China Phys. Mech. Astron. 64, 100312 (2021). https://doi.org/10.1007/s11433-021-1753-3	量子联邦学习，安全角度探索
2022	QML, QSVM, QCNN	Kalinin, M., Krundyshev, V. Security intrusion detection using quantum machine learning techniques. J Comput Virol Hack Tech 19, 125–136 (2023). https://doi.org/10.1007/s11416-022-00435-0	应用，对比经典的与QCNN和QSVM训练效果
2021	ㅤ	Li, W., Deng, DL. Recent advances for quantum classifiers. Sci. China Phys. Mech. Astron. 65, 220301 (2022). https://doi.org/10.1007/s11433-021-1793-6	分类器
2023	QML	Senokosov, A., Sedykh, A., Sagingalieva, A., & Melnikov, A. (2023). Quantum machine learning for image classification. arXiv preprint arXiv:2304.09224.	应用，MNIST图像分类
2023	*QML	Kharsa, Ruba, Ahmed Bouridane, and Abbes Amira. "Advances in Quantum Machine Learning and deep learning for image classification: A survey." Neurocomputing (2023): 126843.	QML图像分类综述
2013	unsupervised	Lloyd, Seth, Masoud Mohseni, and Patrick Rebentrost. "Quantum algorithms for supervised and unsupervised machine learning." arXiv preprint arXiv:1307.0411 (2013).	监督学习、非监督学习
2022	ㅤ	Zhang, Yichi, et al. "Federated Learning with Quantum Secure Aggregation." arXiv preprint arXiv:2207.07444 (2022).	联邦学习，量子安全
2019	unsupervised	Kerenidis, Iordanis, et al. "q-means: A quantum algorithm for unsupervised machine learning." Advances in neural information processing systems 32 (2019).	k-means
2022	unsupervised	Kyriienko, Oleksandr, and Einar B. Magnusson. "Unsupervised quantum machine learning for fraud detection." arXiv preprint arXiv:2208.01203 (2022).	欺诈检测应用，PCA预处理
2017	unsupervised	Otterbach, Johannes S., et al. "Unsupervised machine learning on a hybrid quantum computer." arXiv preprint arXiv:1712.05771 (2017).	偏物理
2022	ㅤ	Jing, Y., Li, X., Yang, Y. et al. RGB image classification with quantum convolutional ansatz. Quantum Inf Process 21, 101 (2022). https://doi.org/10.1007/s11128-022-03442-8	RGB 图像分类
2023	QML	West, M. T., Erfani, S. M., Leckie, C., Sevior, M., Hollenberg, L. C., & Usman, M. (2023). Benchmarking adversarially robust quantum machine learning at scale. Physical Review Research, 5(2), 023186.	对抗攻击
2023	ㅤ	West, M. T., Sevior, M., & Usman, M. (2023). Reflection equivariant quantum neural networks for enhanced image classification. Machine Learning: Science and Technology, 4(3), 035027.	复杂、高分辨率图像
2023	ㅤ	Xiong, H., Duan, X., Yu, Y., Zhang, J., & Yin, H. (2023, May). Image Classification Based on Quantum Machine Learning. In 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) (pp. 891-895). IEEE.	图像分类
2024	ㅤ	Pirnay, N., Ulitzsch, V., Wilde, F., Eisert, J., & Seifert, J. P. (2024). An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory. Science Advances, 10(11), eadj5170.	组合优化优势证明

Enabling breakthroughs in various scientific domains, including cryptography, big data analysis, machine learning, optimization, the Internet of Things (IoT), and Blockchain.

近期的研究主要集中在量子机器学习的各种技术和方法上，包括监督和无监督算法。这些研究旨在通过对具有复杂特征的不同数据集进行实验来结合和比较经典和量子机器学习算法。

量子计算专注于研究量子力学系统中编码的信息的存储、处理和传输问题。因此，这种信息模式被称为量子信息。量子计算模型提出了（时间）可逆计算的概率版本，即输出与输入一一对应的计算。根据量子理论，物理状态在数学上由密度矩阵表示，密度矩阵是迹、正半定矩阵，概括了概率分布的概念。然后，量子计算模型使用的逻辑状态被识别为实现它们的量子系统的物理状态。通过将酉矩阵序列应用到初始化状态来可逆地执行计算。根据最终密度矩阵编码的分布获得概率输出。

当今监督QML主要两种方法：QSVM（a kernel-based method）和VQC（a variational approach）。

变分量子电路 Variational Quantum Circuit（VQC）是一种具有自由参数的量子门电路，可以近似、优化和分类各种算术任务。基于 VQC 的算法被称为变分量子算法 (VQA)，这是一种经典的量子混合算法，其中参数优化通常发生在经典计算机上。 VQA 使用具有量子特性的学习参数来接近目标函数，例如可逆线性门操作和使用接合层的多层结构。 VQC 已被用来取代现有的卷积神经网络 (CNN)，QNN 被定义为 VQA 的子集。

虽然 VQA 仍然是设计 QNN 的重要方法，但它也继承了它的一些缺点。例如，QNN框架目前面临着贫瘠高原的问题，但尚未提出针对该问题的具体解决方案。

QSVM算法，特征空间中估计内核，二元或多元分类，聚类等。QSVM可以有效地探索高维空间。当数据集非线性，通过创建新的特征——每个点与中心的距离，提升维度后在高维空间中进行分类。

挑战：

NISQ，考虑噪声影响，新的机器学习方法有量子噪声学习。

纠错，量子位增多，信息编码数量增加。

针对大型数据集的量子机器学习模型是困难的，悖论：QML在某些情况下能超越经典模型，但无法处理大量数据，难以证明QML相对经典ML有优势。一种常用的解决办法：直接在经典计算机上模拟QML模型，结果显示仍表现出相对于经典模型的优越性。

二分类问题用的数据集，例如：乳腺癌数据集、电离层数据集、垃圾邮件数据集。