维基百科

量子机器学习

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运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]这些子程序可能比较复杂，在量子计算机上执行会有著更显著的速度提升。[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

  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   [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  . 
  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. 
检索自“https://zh.wikipedia.org/w/index.php?title=量子機器學習&oldid=80244637”