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The Present and Perspective of Quantum Machine Learning
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 7,  2016, pp.751-762
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.7.751
 Title & Authors
The Present and Perspective of Quantum Machine Learning
Chung, Wonzoo; Lee, Seong-Whan;
 
 Abstract
This paper presents an overview of the emerging field of quantum machine learning which promises an innovative expedited performance of current classical machine learning algorithms by applying quantum theory. The approaches and technical details of recently developed quantum machine learning algorithms that have been able to substantially accelerate existing classical machine learning algorithms are presented. In addition, the quantum annealing algorithm behind the first commercial quantum computer is also discussed.
 Keywords
quantum computing;machine learning;quantum machine learning;artificial intelligence;
 Language
Korean
 Cited by
 References
1.
M. Hilbert and P. Lopez, "The world's technological capacity to store, communicate, and compute information," Science, Vol. 332, No. 6025, pp. 60-65, 2011. crossref(new window)

2.
P. Shor, "Algorithms for quantum computation: Discrete log and factoring," Proc. of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, NM, USA, Nov. 22-24, pp. 124-134, 1994.

3.
S. Lloyd, M. Mohseni, and P. Rebentrost, "Quantum algorithms for supervised and unsupervised machine learning," arXiv:1307.0411, 2013.

4.
M. Johnson et al., "Quantum annealing with manufactured spins," Nature, Vol. 473, pp. 194-198, May 2011. crossref(new window)

5.
R. Feynman, R. Leighton, and M. Sands, Lectures on Physics, Vol. III, Addison Wesley, 1965.

6.
G. Greenstein and A. Zajonc, The Quantum Challenge, Jones and Bartlett Publishers, 1997.

7.
R. Feynman, "Simulating physics with computers," International Journal of Theoretical Physics, Vol. 21, pp. 467-488, 1982. crossref(new window)

8.
M. Nielsen and I. Chuang, Quantum Computation and Quantum Information, Cambridge: Cambridge University Press, 2010.

9.
D. Deutsch, "Quantum theory, the church-turing principle and the universal quantum computer," Proc. of the Royal Society of London, Vol. A400, pp. 97-117, 1985.

10.
L. Grover, "A fast quantum mechanical algorithm for database search," Proc. of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, (Philadelphia, Pennsylvania), pp. 212-219, May 1996.

11.
M. Boyer, G. Brassard, P. Hoyer, and A. Tapp, "Tight bounds on quantum search," Proc. of the Workshop on Physics of Computation: PhysComp' 96, (Los Alamitos, CA), 1996.

12.
P. Shor, "Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer," SIAM Journal on Computing, Vol. 26, No. 5, pp. 1484-1509, 1997. crossref(new window)

13.
M. Ettinger, P. Hoyer, and E. Knill, "The quantum query complexity of the hidden subgroup problem is polynomial," Information Processing Letters, Vol. 91, pp. 43-48, Jul. 2004. crossref(new window)

14.
S. Hallgren, "Polynomial-time quantum algorithms for pell's equation and the principal ideal problem," Journal of the ACM, Vol. 54, No. 4, 2007.

15.
Y. Aharonov, L. Davidovich, and N. Zagury, "Quantum random walks," Phys. Rev. A, Vol. 48, No. 2, pp. 1687-1690, 1993. crossref(new window)

16.
A. Ambainis, "Quantum walk algorithm for element distinctness," SIAM Journal on Computing, Vol. 37, No. 1, pp. 210-239, 2007. crossref(new window)

17.
B. W. Reichardt and R. Spalek, "Span-programbased quantum algorithm for evaluating formulas," Proc. of the 40th Annual ACM symposium on Theory of Computing, pp. 103-112, Association for Computing Machinery, 2008.

18.
E. Martin-Lopez, A. Laing, T. Lawson, R. Alvarez, X.-Q. Zhou, and J. L. O'Brien, "Experimental realization of shor's quantum factoring algorithm using qubit recycling," Nature Photonics, Oct. 2012.

19.
M. Schuld, I. Sinayskiy, and F. Petruccione, "An introduction to quantum machine learning," Contemporary Physics, Vol. 56, No. 2, pp. 172-185, 2015. crossref(new window)

20.
D. Anguita, S. Ridella, F. Rivieccio, and R. Zunino, "Quantum optimization for training support vector machines," Neural Networks, Vol. 16, No. 5, pp. 763-770, 2003. crossref(new window)

21.
E. Aimeur, G. Brassard, and S. Gambs, "Quantum speed-up for unsupervised learning," Machine Learning, Vol. 90, No. 2, pp. 261-287, 2013. crossref(new window)

22.
N. Wiebe, A. Kapoor, and K. M. Svore, "Quantum nearest neighbor algorithms for machine learning," arXiv:1401.2142, 2014.

23.
V. Giovannetti, S. Lloyd, and L. Maccone, "Quantum random access memory," Physical Review Letters, Vol. 100, No. 160501, 2008.

24.
P. Rebentrost, M. Mohseni, and S. Lloyd, "Quantum support vector machine for big data classification," Physical Review Letters, Vol. 113, No. 130503, 2014.

25.
A. W. Harrow, A. Hassidim, and S. Lloyd, "Quantum algorithm for linear systems of equations," Physical Review Letters, Vol. 103, Oct. 2009.

26.
C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, Vol. 20, pp. 273-297, 1995.

27.
Z. Li, X. Liu, N. Xu, and J. Du, "Experimental realization of a quantum support vector machine," Physical Review Letters, Vol. 114, Apr. 2015.

28.
S. Lloyd, "Least square quantization in pcm," IEEE Transactions on Information Theory, Vol. 28, No. 2, 1982.

29.
N. Wiebe, D. Braun, and S. Lloyd, "Quantum algorithm for data fitting," Physical Review Letters, Vol. 109, Aug. 2012.

30.
A. Monras, A. Beige, and K. Wiesner, "Hidden quantum markov models and nonadaptive read-out of many-body states," Applied Mathematical and Computational Sciences, Vol. 3, pp. 93-122, 2010.

31.
J. Barry, D. Barry, and S. Aaronson, "Quantum partially observable markov decision processes," Physical Review A, Vol. 90, pp. 032311-1-032311-11, 2014. crossref(new window)

32.
A. Ezhov, A. Nifanova, and D. Ventura, "Quantum associative memory with distributed queries," Information Sciences, Vol. 128, No. 271-293, 2000. crossref(new window)

33.
J. Howell, J. Yeazell, and D. Ventura, "Optically simulating a quantum associative memory," Physical Review A, Vol. 62, 2000.

34.
N. Wiebe, A. Kapoor, and K. M. Svore, "Quantum deep learning," arXiv:1412.3489, 2015.

35.
E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, "Quantum computation by adiabatic evolution," arXiv:quant-ph, Vol. 0405098, 2000.

36.
E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda, "A quantum adiabatic evolution algorithm applied to random instances of an np-complete problem," Science, Vol. 292, pp. 472-476, Apr. 2001. crossref(new window)

37.
D. Aharonov, W. van Dam, J. Kempe, Z. Landau, S. Lyold, and O. Regev, "Adiabatic quantum computation is equivalent to standard quantum computation," SIAM Journal of Computing, Vol. 37, 2007.

38.
J. Roland and J. N. Cerf, "Quantum search by local adiabatic evolution," Physical Review A, Vol. 65, 2002.

39.
V. Bapst, L. Foini, F. Krzakala, G. Semerjian, and F. Zamponi, "The quantum adiabatic algorithm applied to random optimization problems: the quantum spin glass perspective," Physics Reports, Vol. 523, No. 127, 2013.

40.
S. G. Brush, "History of the lenz-ising model," Rev. Mod. Phys., Vol. 39, Oct. 1967.

41.
F. Barahona, "On the computational complexity of ising spin glass models," Journal of Physics, Vol. A15, No. 3241, 1982.

42.
A. Lucas, "Ising formulations of many np problems," Frontiers in Physics, Vol. 2, 2014.

43.
J. D. Biamonte and P. J. Love, "Realizable hamiltonians for universal adiabatic quantum computers," Physical Review A, Vol. 78, No. 012352, 2008.

44.
J. D. Whitfield, M. Faccin, and J. D. Biamonte, "Ground state spin logic," Europhysics Letters, Vol. 99, No. 57004, 2012.

45.
T. Kadowaki and H. Nishimori, "Quantum annealing in the transverse ising model," Phys. Rev. E, Vol. 58, No. 5355, 1998.

46.
J. Brooke, D. Bitko, T. F. Rosenbaum, and G. Aeppli, "Quantum annealing of a disordered magnet," Science, Vol. 284, No. 779, 1999.

47.
M. W. Johnson et al., "Quantum annealing with manufactured spins," Nature, Vol. 473, No. 194, 2011.

48.
S. Boixo, T. F. Ronnow, S. V. Isakov, Z. Wang, D. Wecker, D. A. Lidar, J. M. Martinis, and M. Troyer, "Quantum annealing with more than one hundred qubits," Nature Phys., Vol. 10, No. 218, 2014.

49.
S. W. Shin, G. Smith, J. A. Smolin, and U. Vazirani, "How "quantum" is the d-wave machine?" arXiv: 1401.7087, 2015.

50.
A. Cho, "Quantum or not, controversial computer yields no speedup," Science, Vol. 344, Jun. 2014.

51.
H. G. Katzgraber, F. Hamze, Z. Zhu, A. J. Ochoa, and H. Munoz-Bauza, "Seeking quantum speedup through spin glasses: The good, the bad, and the ugly," Phys. Rev. X, Vol. 5, No. 031026, 2015.

52.
E. Cohen and B. Tamir, "Quantum annealing-foundations and frontiers," The European Physical Journal, Vol. 224, pp. 89-110, Feb. 2015.