Quantum computing for pattern classification

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Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco;
  • Subject: Quantum Physics

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This pap... View more
  • References (34)
    34 references, page 1 of 4

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

    [2] 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.

    [3] Nathan Wiebe, Ashish Kapoor, and Krysta Svore. Quantum nearestneighbor algorithms for machine learning. arXiv preprint arXiv:1401.2142, 2014.

    [4] G Sent s, J Calsamiglia, Ramon Mun~oz-Tapia, and E Bagan. Quantum learning without quantum memory. Scienti c Reports, 2, 2012.

    [5] Madalin Guta and Wojciech Kotlowski. Quantum learning: asymptotically optimal classi cation of qubit states. New Journal of Physics, 12(12):123032, 2010.

    [6] Gerasimos G Rigatos and Spyros G Tzafestas. Neurodynamics and attractors in quantum associative memories. Integrated Computer-Aided Engineering, 14(3):225{242, 2007.

    [7] Jerome R Busemeyer and Peter D Bruza. Quantum models of cognition and decision. Cambridge University Press, 2012.

    [8] Masahide Sasaki, Alberto Carlini, and Richard Jozsa. Quantum template matching. Physical Review A, 64(2):022317, 2001.

    [9] Sahibsingh A Dudani. The distance-weighted k-nearest-neighbor rule. Systems, Man and Cybernetics, IEEE Transactions on, (4):325{327, 1976.

    [10] Carlo A Trugenberger. Probabilistic quantum memories. Physical Review Letters, 87:067901, Jul 2001.

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