SCIENCE CHINA Information Sciences, Volume 63 , Issue 8 : 189501(2020) https://doi.org/10.1007/s11432-019-2783-7

Quantum speedup of twin support vector machines

More info
  • ReceivedAug 31, 2019
  • AcceptedFeb 6, 2020
  • PublishedApr 23, 2020


There is no abstract available for this article.


This work was supported by the National Natural Science Foundation of China (Grant No. 61772565), the Natural Science Foundation of Guangdong Province (Grant No. 2017A030313378), the Science and Technology Program of Guangzhou City (Grant No. 201707010194), and Key R D Project of Guangdong Province (Grant No. 2018B030325001).


Appendixes A–D.


[1] Jayadeva , Khemchandani R, Chandra S. Twin Support Vector Machines for pattern classification.. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 905-910 CrossRef PubMed Google Scholar

[2] Arun Kumar M, Gopal M. Least squares twin support vector machines for pattern classification. Expert Syst Appl, 2009, 36: 7535-7543 CrossRef Google Scholar

[3] Harrow A W, Hassidim A, Lloyd S. Quantum Algorithm for Linear Systems of Equations. Phys Rev Lett, 2009, 103: 150502 CrossRef PubMed ADS arXiv Google Scholar

[4] Rebentrost P, Mohseni M, Lloyd S. Quantum Support Vector Machine for Big Data Classification. Phys Rev Lett, 2014, 113: 130503 CrossRef PubMed ADS arXiv Google Scholar

[5] Giovannetti V, Lloyd S, Maccone L. Quantum Random Access Memory. Phys Rev Lett, 2008, 100: 160501 CrossRef PubMed ADS arXiv Google Scholar

[6] Lloyd S, Mohseni M, Rebentrost P. Quantum principal component analysis. Nat Phys, 2014, 10: 631-633 CrossRef ADS arXiv Google Scholar

[7] Buhrman H, Cleve R, Watrous J, et al. Quantum fingerprinting. Phys Rev Lett, 2001, 87(16):167902. Google Scholar