跳转到内容

量子機器學習

维基百科，自由的百科全书
系列条目
量子力学
薛定谔方程
背景
基本原理
实验
表述
方程
诠释
高阶
科学家
机器学习与数据挖掘
范式
问题
聚类分析
降维
结构预测（英语：Structured prediction）
异常检测
人工神经网络
强化学习
与人类学习
模型诊断
数学基础
大会与出版物
相关条目

量子机器学习，是将量子算法整合到机器学习程序中。[1][2][3][4][5][6][7]该术语最常见的用法是指用于分析量子计算机上执行的经典数据的机器学习算法，即量子增强机器学习。[8][9][10][11]常规机器学习算法被用来计算海量数据，而量子机器学习利用量子位和量子運算或专门的量子系统来提高算法在程序中完成的计算速度和数据存储。[12]在实际操作中，量子机器学习会混合常规机器学习，先用常规计算机执行机器学习程序，然后将无法通过常规计算机完成的子程序交由量子计算机完成。[13][14][15]這些子程序可能比較複雜，在量子計算機上執行會有著更顯著的速度提升。[2]此外，量子算法可以用来分析量子态而不仅仅局限于常规数据。[16][17]

参考文献

[编辑]
  1. ^ Schuld, Maria; Petruccione, Francesco. Supervised Learning with Quantum Computers. Quantum Science and Technology. 2018. ISBN 978-3-319-96423-2. doi:10.1007/978-3-319-96424-9. 
  2. ^ 2.0 2.1 Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco. An introduction to quantum machine learning. Contemporary Physics. 2014, 56 (2): 172–185. Bibcode:2015ConPh..56..172S. CiteSeerX 10.1.1.740.5622 可免费查阅. S2CID 119263556. arXiv:1409.3097 可免费查阅. doi:10.1080/00107514.2014.964942. 
  3. ^ Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Academic Press. 2014 [2021-11-10]. ISBN 978-0-12-800953-6. （原始内容存档于2022-03-02）. 
  4. ^ Adcock, Jeremy; Allen, Euan; Day, Matthew; Frick, Stefan; Hinchliff, Janna; Johnson, Mack; Morley-Short, Sam; Pallister, Sam; Price, Alasdair; Stanisic, Stasja. Advances in quantum machine learning. 2015. arXiv:1512.02900 可免费查阅 [quant-ph]. 
  5. ^ Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth. Quantum machine learning. Nature. 2017, 549 (7671): 195–202. Bibcode:2017Natur.549..195B. PMID 28905917. S2CID 64536201. arXiv:1611.09347 可免费查阅. doi:10.1038/nature23474. 
  6. ^ Perdomo-Ortiz, Alejandro; Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak. Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Science and Technology. 2018, 3 (3): 030502. Bibcode:2018QS&T....3c0502P. S2CID 3963470. arXiv:1708.09757 可免费查阅. doi:10.1088/2058-9565/aab859. 
  7. ^ Das Sarma, Sankar; Deng, Dong-Ling; Duan, Lu-Ming. Machine learning meets quantum physics. Physics Today. 2019-03-01, 72 (3): 48–54 [2021-11-10]. Bibcode:2019PhT....72c..48D. ISSN 0031-9228. S2CID 86648124. arXiv:1903.03516 可免费查阅. doi:10.1063/PT.3.4164. （原始内容存档于2022-10-06）. 
  8. ^ Wiebe, Nathan; Kapoor, Ashish; Svore, Krysta. Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning. Quantum Information & Computation. 2014, 15 (3): 0318–0358. Bibcode:2014arXiv1401.2142W. arXiv:1401.2142 可免费查阅. 
  9. ^ Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick. Quantum algorithms for supervised and unsupervised machine learning. 2013. arXiv:1307.0411 可免费查阅 [quant-ph]. 
  10. ^ Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung. A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification. New Journal of Physics. 2014, 16 (10): 103014. Bibcode:2014NJPh...16j3014Y. S2CID 4956424. arXiv:1303.6055 可免费查阅. doi:10.1088/1367-2630/16/10/103014. 
  11. ^ Lee, Joong-Sung; Bang, Jeongho; Hong, Sunghyuk; Lee, Changhyoup; Seol, Kang Hee; Lee, Jinhyoung; Lee, Kwang-Geol. Experimental demonstration of quantum learning speedup with classical input data. Physical Review A. 2019, 99 (1): 012313. Bibcode:2019PhRvA..99a2313L. S2CID 53977163. arXiv:1706.01561 可免费查阅. doi:10.1103/PhysRevA.99.012313. 
  12. ^ Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco. An introduction to quantum machine learning. Contemporary Physics. 2014-10-15, 56 (2): 172–185. Bibcode:2015ConPh..56..172S. CiteSeerX 10.1.1.740.5622 可免费查阅. ISSN 0010-7514. S2CID 119263556. arXiv:1409.3097 可免费查阅. doi:10.1080/00107514.2014.964942 （英语）. 
  13. ^ Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models. Physical Review X. 2017-11-30, 7 (4): 041052. Bibcode:2017PhRvX...7d1052B. ISSN 2160-3308. S2CID 55331519. arXiv:1609.02542 可免费查阅. doi:10.1103/PhysRevX.7.041052. 
  14. ^ Farhi, Edward; Neven, Hartmut. Classification with Quantum Neural Networks on Near Term Processors. 2018-02-16. arXiv:1802.06002 可免费查阅 [quant-ph]. 
  15. ^ Schuld, Maria; Bocharov, Alex; Svore, Krysta; Wiebe, Nathan. Circuit-centric quantum classifiers. Physical Review A. 2020, 101 (3): 032308. Bibcode:2020PhRvA.101c2308S. S2CID 49577148. arXiv:1804.00633 可免费查阅. doi:10.1103/PhysRevA.101.032308. 
  16. ^ Yu, Shang; Albarran-Arriagada, F.; Retamal, J. C.; Wang, Yi-Tao; Liu, Wei; Ke, Zhi-Jin; Meng, Yu; Li, Zhi-Peng; Tang, Jian-Shun. Reconstruction of a Photonic Qubit State with Quantum Reinforcement Learning. Advanced Quantum Technologies. 2018-08-28, 2 (7–8): 1800074. S2CID 85529734. arXiv:1808.09241 可免费查阅. doi:10.1002/qute.201800074. 
  17. ^ Ghosh, Sanjib; Opala, A.; Matuszewski, M.; Paterek, T.; Liew, Timothy C. H. Quantum reservoir processing. NPJ Quantum Information. 2019, 5 (35): 35. Bibcode:2019npjQI...5...35G. S2CID 119197635. arXiv:1811.10335 可免费查阅. doi:10.1038/s41534-019-0149-8. 
检索自“https://zh.wikipedia.org/w/index.php?title=量子機器學習&oldid=80244637”