Voir la notice de l'article provenant de la source Library of Science
@article{IJAMCS_2019_29_1_a10, author = {Sawerwain, Marek and Wr\'oblewski, Marek}, title = {Recommendation systems with the quantum {k-NN} and {Grover} algorithms for data processing}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {139--150}, publisher = {mathdoc}, volume = {29}, number = {1}, year = {2019}, language = {en}, url = {https://geodesic-test.mathdoc.fr/item/IJAMCS_2019_29_1_a10/} }
TY - JOUR AU - Sawerwain, Marek AU - Wróblewski, Marek TI - Recommendation systems with the quantum k-NN and Grover algorithms for data processing JO - International Journal of Applied Mathematics and Computer Science PY - 2019 SP - 139 EP - 150 VL - 29 IS - 1 PB - mathdoc UR - https://geodesic-test.mathdoc.fr/item/IJAMCS_2019_29_1_a10/ LA - en ID - IJAMCS_2019_29_1_a10 ER -
%0 Journal Article %A Sawerwain, Marek %A Wróblewski, Marek %T Recommendation systems with the quantum k-NN and Grover algorithms for data processing %J International Journal of Applied Mathematics and Computer Science %D 2019 %P 139-150 %V 29 %N 1 %I mathdoc %U https://geodesic-test.mathdoc.fr/item/IJAMCS_2019_29_1_a10/ %G en %F IJAMCS_2019_29_1_a10
Sawerwain, Marek; Wróblewski, Marek. Recommendation systems with the quantum k-NN and Grover algorithms for data processing. International Journal of Applied Mathematics and Computer Science, Tome 29 (2019) no. 1, pp. 139-150. https://geodesic-test.mathdoc.fr/item/IJAMCS_2019_29_1_a10/
[1] Aaronson, S. and Gottesman, D. (2004). Improved simulation of stabilizer circuits, Physical Review A 70(5): 052328, DOI: 10.1103/PhysRevA.70.052328.
[2] Alpaydin, E. (2004). Introduction to Machine Learning (Adaptive Computation and Machine Learning), Massachusetts Institute of Technology Press, Cambridge, MA.
[3] Arikan, E. (2003). An information-theoretic analysis of Grover’s algorithm, in A.S. Shumovsky and V.I. Rupasov (Eds.), Quantum Communication and Information Technologies, Springer Netherlands, Dordrecht, pp. 339–347.
[4] Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M. (2010). A view of cloud computing, Communications of the Association for Computing Machinery 53(4): 50–58, DOI: 10.1145/1721654.1721672.
[5] Barenco, A., Bennett, C.H., Cleve, R., DiVincenzo, D.P., Margolus, N., Shor, P., Sleator, T., Smolin, J.A. and Weinfurter, H. (1995). Elementary gates for quantum computation, Physical Review A 52(5): 3457–3467, DOI: 10.1103/PhysRevA.52.3457.
[6] Biham, E., Biham, O., Biron, D., Grassl, M. and Lidar, D. (1999). Grover’s quantum search algorithm for an arbitrary initial amplitude distribution, Physical Review 60(4): 2742–2745, DOI: 10.1103/PhysRevA.60.2742.
[7] Brassard, G. and Hoyer, P. (1997). An exact quantum polynomial-time algorithm for Simon’s problem, Proceedings of the 5th Israeli Symposium on Theory of Computing and Systems, Ramat Gan, Israel, DOI: 10.1109/ISTCS.1997.595153.
[8] Busemeyer, J. and Bruza, P. (2012). Quantum Models of Cognition and Decision, Cambridge University Press, Cambridge.
[9] Chakrabarty, I., Khan, S. and Singh, V. (2017). Dynamic grover search: Applications in recommendation systems and optimization problems, Quantum Information Processing 16(6): 153, DOI: 10.1007/s11128-017-1600-4.
[10] D’Hondt, E. and Panangaden, P. (2006). Quantum weakest preconditions, Mathematical Structures in Computer Science 16(3): 429–451.
[11] Galindo, A. and Martin-Delgado, M. A. (2000). A family of Grover’s quantum searching algorithms, Physical Review A 62(6): 062303, DOI: 10.1103/PhysRevA.62.062303.
[12] Galindo, A. and Martin-Delgado, M.A. (2002). Information and computation: Classical and quantum aspects, Reviews of Modern Physics 74(2): 347–423, DOI: 10.1103/RevModPhys.74.347.
[13] Gielerak, R. and Sawerwain, M. (2010). Generalised quantum weakest preconditions, Quantum Information Processing 9(4): 441–449, DOI: 10.1007/s11128-009-0151-8.
[14] Grover, L.K. (1996). A fast quantum mechanical algorithm for database search, Proceedings of the 28th Annual ACM Symposium on Theory of Computing, STOC’96, Philadelphia, PA, USA, pp. 212–219, DOI: 10.1145/237814.237866.
[15] Hechenbichler, K. and Schliep, K. (2004). Weighted k-nearest-neighbor techniques and ordinal classification, Technical report, Ludwig-Maximilians-Universität München, München, https://epub.ub.uni-muenchen.de/1769/1/paper_399.pdf.
[16] IBM (2018). Q Experience, https://quantumexperience.ng.bluemix.net/.
[17] Li, C.-K., Roberts, R. and Yin, X. (2013). Decomposition of unitary matrices and quantum gates, International Journal of Quantum Information 11(1): 1350015, DOI: 10.1142/S0219749913500159.
[18] Montanaro, A. (2017). Quantum pattern matching fast on average, Alghoritmica: An International Journal in Computer Science 77(1): 16–39, DOI: 10.1007/s00453-015-0060-4.
[19] Nielsen, M. and Chuang, I. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition, Cambridge University Press, Cambridge.
[20] Nielsen, P. (2016). Big data analytics—a brief research synthesis, in L. Borzemski et al. (Eds.), Information Systems Architecture and Technology, Springer International Publishing, Cham, pp. 3–9.
[21] OMDb (2018). Homepage, http://www.omdbapi.com/.
[22] Pinkse, P., Goorden, S., Horstmann, M., Skoric, B. and Mosk, A. (2013). Quantum pattern recognition, Conference on Lasers and Electro-Optics Europe (CLEO EUROPE/IQEC) and International Quantum Electronics Conference, Munich, Germany, p. 1–1.
[23] Santucci, E. (2017). Quantum minimum distance classifier, Entropy 19(12): 659, DOI: 10.3390/e19120659.
[24] Sawerwain, M. and Wróblewski, M. (2019). Application of quantum k-nn and Grover’s algorithms for recommendation big-data system, in L. Borzemski et al. (Eds.), Information Systems Architecture and Technology, Springer International Publishing, Cham, pp. 235–244.
[25] Schuld, M., Sinayskiy, I. and Petruccione, F. (2014). Quantum computing for pattern classification, in D.-N. Pham and S.-B. Park (Eds.), PRICAI 2014: Trends in Artificial Intelligence, Springer International Publishing, Cham, pp. 208–220.
[26] Sergioli, G., Bosyk, G.M., Santucci, E. and Giuntini, R. (2017). A quantum-inspired version of the classification problem, International Journal of Theoretical Physics 56(12): 3880–3888, DOI: 10.1007/s10773-017-3371-1.
[27] Sergioli, G., Santucci, E., Didaci, L., Miszczak, J.A. and Giuntini, R. (2018). A quantum-inspired version of the nearest mean classifier, Soft Computing 22(3): 691–705, DOI: 10.1007/s00500-016-2478-2.
[28] Shende, V. and Markov, I.L. (2009). On the CNOT-cost of TOFFOLI gates, Quantum Information Computation 9(5): 461–486.
[29] Shor, P. (1999). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM Review 41(2): 303–332, DOI: 10.1137/S0036144598347011.
[30] Steane, A. (1998). Quantum computing, Reports on Progress in Physics 61(2): 117–173, DOI: 10.1088/0034-4885/61/2/002.
[31] Stefanowski, J., Krawiec, K. and Wrembel, R. (2017). Exploring complex and big data, International Journal of Applied Mathematics and Computer Science 27(4): 669–679, DOI: 10.1515/amcs-2017-0046.
[32] Trugenberger, C.A. (2002). Quantum pattern recognition, Quantum Information Processing 1(6): 471–493, DOI: 10.1023/A:1024022632303.
[33] Veloso, B., Malheiro, B. and Burguillo, J.C. (2015). A multi-agent brokerage platform for media content recommendation, International Journal of Applied Mathematics and Computer Science 25(3): 513–527, DOI: 10.1515/amcs-2015-0038.
[34] Walther, P., Resch, K.J., Rudolph, T., Schenck, E., Weinfurter, H., Vedral, V., Aspelmeyer, M. and Zeilinger, A. (2005). Experimental one-way quantum computing, Nature 434(0): 169–176, DOI: 10.1038/nature03347.
[35] Wiebe, N., Kapoor, A. and Svore, K. (2015). Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning, Quantum Information and Computation 15(3–4): 316–356.
[36] Wiśniewska, J. and Sawerwain, M. (2018). Recognizing the pattern of binary Hermitian matrices by quantum knn and SVM methods, Vietnam Journal of Computer Science 5(3): 197–204, DOI: 10.1007/s40595-018-0115-y.