publication . Other literature type . Article . Preprint . 2014

An introduction to quantum machine learning

Schuld, M.; Sinayskiy, I.; Petruccione, F.;
  • Published: 15 Oct 2014
  • Publisher: Informa UK Limited
Abstract
Comment: to appear in Contemporary Physics; 19 pages, 10 figures
Subjects
free text keywords: General Physics and Astronomy, Theoretical computer science, Quantum information, Stability (learning theory), Robot learning, Quantum computer, Artificial intelligence, business.industry, business, Active learning (machine learning), Computational learning theory, Machine learning, computer.software_genre, computer, Physics, Theoretical physics, Quantum machine learning, Algorithmic learning theory, Quantum Physics
51 references, page 1 of 4

[1] Martin Hilbert and Priscila Lopez. The world's technological capacity to store, communicate, and compute information. Science, 332(6025):60{65, 2011.

[2] Michael A Nielsen and Isaac L Chuang. Quantum computation and quantum information. Cambridge University Press, 2010.

[11] Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost. Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411, 2013.

[12] Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd. Quantum support vector machine for big feature and big data classi cation. arXiv preprint arXiv:1307.0471, 2013.

[13] Nathan Wiebe, Ashish Kapoor, and Krysta Svore. Quantum nearest-neighbor algorithms for machine learning. arXiv preprint arXiv:1401.2142, 2014.

[15] Kristen L Pudenz and Daniel A Lidar. Quantum adiabatic machine learning. Quantum Information Processing, 12(5):2027{2070, 2013.

[16] Rodion Neigovzen, Jorge L Neves, Rudolf Sollacher, and Ste en J Glaser. Quantum pattern recognition with liquid-state nuclear magnetic [27] Jiangfeng Du, Hui Li, Xiaodong Xu, Mingjun resonance. Physical Review A, 79(4):042321, Shi, Jihui Wu, Xianyi Zhou, and Rongdian Han. 2009. Experimental realization of quantum games on a quantum computer. Physical Review Letters, 88(13):137902, 2002. [OpenAIRE]

[17] G Sent s, J Calsamiglia, Ramon Mun~oz-Tapia, and E Bagan. Quantum learning without quantum memory. Scienti c Reports, 2(708):1{8, [28] Edward W Piotrowski and Jan Sladkowski. 2012. An invitation to quantum game theory. International Journal of Theoretical Physics, 42(5):1089{1099, 2003.

[18] Lewis A Clark, Wei Huang, Thomas M Barlow, and Almut Beige. Hidden quantum markov models and open quantum systems [29] Christopher M Bishop et al. Pattern recognition with instantaneous feedback. arXiv preprint and machine learning, volume 1. springer New arXiv:1406.5847, 2014. York, 2006.

[25] Jens Eisert, Martin Wilkens, and Maciej Lewenstein. Quantum games and quantum strategies. Physical Review Letters, 83(15):3077, 1999.

[26] Hans J Briegel and Gemma De las Cuevas. Projective simulation for arti cial intelligence. Scienti c Reports, 2, 2012.

[19] Stuart Jonathan Russell, Peter Norvig, John F Canny, Jitendra M Malik, and Douglas D Edwards. Arti cial intelligence: A modern approach, volume 3. Prentice Hall Englewood Cli s, 2010.

[20] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386, 1958. [OpenAIRE]

[30] Geo rey Hinton, Simon Osindero, and YeeWhye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527{1554, 2006.

[31] David E Rumelhart, Geo rey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. Cognitive Modeling, 1988.

51 references, page 1 of 4
Abstract
Comment: to appear in Contemporary Physics; 19 pages, 10 figures
Subjects
free text keywords: General Physics and Astronomy, Theoretical computer science, Quantum information, Stability (learning theory), Robot learning, Quantum computer, Artificial intelligence, business.industry, business, Active learning (machine learning), Computational learning theory, Machine learning, computer.software_genre, computer, Physics, Theoretical physics, Quantum machine learning, Algorithmic learning theory, Quantum Physics
51 references, page 1 of 4

[1] Martin Hilbert and Priscila Lopez. The world's technological capacity to store, communicate, and compute information. Science, 332(6025):60{65, 2011.

[2] Michael A Nielsen and Isaac L Chuang. Quantum computation and quantum information. Cambridge University Press, 2010.

[11] Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost. Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411, 2013.

[12] Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd. Quantum support vector machine for big feature and big data classi cation. arXiv preprint arXiv:1307.0471, 2013.

[13] Nathan Wiebe, Ashish Kapoor, and Krysta Svore. Quantum nearest-neighbor algorithms for machine learning. arXiv preprint arXiv:1401.2142, 2014.

[15] Kristen L Pudenz and Daniel A Lidar. Quantum adiabatic machine learning. Quantum Information Processing, 12(5):2027{2070, 2013.

[16] Rodion Neigovzen, Jorge L Neves, Rudolf Sollacher, and Ste en J Glaser. Quantum pattern recognition with liquid-state nuclear magnetic [27] Jiangfeng Du, Hui Li, Xiaodong Xu, Mingjun resonance. Physical Review A, 79(4):042321, Shi, Jihui Wu, Xianyi Zhou, and Rongdian Han. 2009. Experimental realization of quantum games on a quantum computer. Physical Review Letters, 88(13):137902, 2002. [OpenAIRE]

[17] G Sent s, J Calsamiglia, Ramon Mun~oz-Tapia, and E Bagan. Quantum learning without quantum memory. Scienti c Reports, 2(708):1{8, [28] Edward W Piotrowski and Jan Sladkowski. 2012. An invitation to quantum game theory. International Journal of Theoretical Physics, 42(5):1089{1099, 2003.

[18] Lewis A Clark, Wei Huang, Thomas M Barlow, and Almut Beige. Hidden quantum markov models and open quantum systems [29] Christopher M Bishop et al. Pattern recognition with instantaneous feedback. arXiv preprint and machine learning, volume 1. springer New arXiv:1406.5847, 2014. York, 2006.

[25] Jens Eisert, Martin Wilkens, and Maciej Lewenstein. Quantum games and quantum strategies. Physical Review Letters, 83(15):3077, 1999.

[26] Hans J Briegel and Gemma De las Cuevas. Projective simulation for arti cial intelligence. Scienti c Reports, 2, 2012.

[19] Stuart Jonathan Russell, Peter Norvig, John F Canny, Jitendra M Malik, and Douglas D Edwards. Arti cial intelligence: A modern approach, volume 3. Prentice Hall Englewood Cli s, 2010.

[20] Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6):386, 1958. [OpenAIRE]

[30] Geo rey Hinton, Simon Osindero, and YeeWhye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527{1554, 2006.

[31] David E Rumelhart, Geo rey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. Cognitive Modeling, 1988.

51 references, page 1 of 4
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publication . Other literature type . Article . Preprint . 2014

An introduction to quantum machine learning

Schuld, M.; Sinayskiy, I.; Petruccione, F.;