維基百科

量子機器學習

量子機器學習，是將量子算法整合到機器學習程序中。[1][2][3][4][5][6][7]該術語最常見的用法是指用於分析量子計算機上執行的經典數據的機器學習算法，即量子增強機器學習。[8][9][10][11]常規機器學習算法被用來計算海量數據，而量子機器學習利用量子位和量子運算或專門的量子系統來提高算法在程序中完成的計算速度和數據存儲。[12]在實際操作中，量子機器學習會混合常規機器學習，先用常規計算機執行機器學習程序，然後將無法通過常規計算機完成的子程序交由量子計算機完成。[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着更顯著的速度提升。[2]此外，量子算法可以用來分析量子態而不僅僅局限於常規數據。[16][17]

參考文獻

  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. （原始內容存檔於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. （原始內容存檔於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 （英語）. 
  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. 
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