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维基百科

量子機器學習

十月 06, 2023

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  . S2CID 119263556. arXiv:1409.3097  . 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   [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  . 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  . 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  . doi:10.1063/PT.3.4164. （原始内容于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  . 
  9. ^ Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick. Quantum algorithms for supervised and unsupervised machine learning. 2013. arXiv:1307.0411   [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  . 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  . 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  . ISSN 0010-7514. S2CID 119263556. arXiv:1409.3097  . 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  . 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   [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  . 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  . 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  . doi:10.1038/s41534-019-0149-8. 

十月 06, 2023
量子機器學習, 此條目可参照英語維基百科相應條目来扩充, 2021年11月12日, 若您熟悉来源语言和主题, 请协助参考外语维基百科扩充条目, 请勿直接提交机械翻译, 也不要翻译不可靠, 低品质内容, 依版权协议, 译文需在编辑摘要注明来源, 或于讨论页顶部标记, href, template, translated, page, html, title, template, translated, page, translated, page, 标签, 量子机器学习, 是将量子算法整合到机器学习程序中, 该术语最. 此條目可参照英語維基百科相應條目来扩充 2021年11月12日 若您熟悉来源语言和主题 请协助参考外语维基百科扩充条目 请勿直接提交机械翻译 也不要翻译不可靠 低品质内容 依版权协议 译文需在编辑摘要注明来源 或于讨论页顶部标记 a href Template Translated page html title Template Translated page Translated page a 标签 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 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 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 nbsp S2CID 119263556 arXiv 1409 3097 nbsp doi 10 1080 00107514 2014 964942 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 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 nbsp quant ph 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 nbsp doi 10 1038 nature23474 Perdomo Ortiz Alejandro Benedetti Marcello Realpe Gomez 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 amp T 3c0502P S2CID 3963470 arXiv 1708 09757 nbsp doi 10 1088 2058 9565 aab859 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 nbsp doi 10 1063 PT 3 4164 原始内容存档于2022 10 06 Wiebe Nathan Kapoor Ashish Svore Krysta Quantum Algorithms for Nearest Neighbor Methods for Supervised and Unsupervised Learning Quantum Information amp Computation 2014 15 3 0318 0358 Bibcode 2014arXiv1401 2142W arXiv 1401 2142 nbsp Lloyd Seth Mohseni Masoud Rebentrost Patrick Quantum algorithms for supervised and unsupervised machine learning 2013 arXiv 1307 0411 nbsp quant ph 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 nbsp doi 10 1088 1367 2630 16 10 103014 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 nbsp doi 10 1103 PhysRevA 99 012313 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 nbsp ISSN 0010 7514 S2CID 119263556 arXiv 1409 3097 nbsp doi 10 1080 00107514 2014 964942 英语 Benedetti Marcello Realpe Gomez 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 nbsp doi 10 1103 PhysRevX 7 041052 Farhi Edward Neven Hartmut Classification with Quantum Neural Networks on Near Term Processors 2018 02 16 arXiv 1802 06002 nbsp quant ph 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 nbsp doi 10 1103 PhysRevA 101 032308 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 nbsp doi 10 1002 qute 201800074 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 nbsp doi 10 1038 s41534 019 0149 8 取自 https zh wikipedia org w index php title 量子機器學習 amp oldid 75218089, 维基百科，wiki，书籍，书籍，图书馆，

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