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Pattern recognition in non-Kolmogorovian structures. (English) Zbl 1398.81054

Summary: We present a generalization of the problem of pattern recognition to arbitrary probabilistic models. This version deals with the problem of recognizing an individual pattern among a family of different species or classes of objects which obey probabilistic laws which do not comply with Kolmogorov’s axioms. We show that such a scenario accommodates many important examples, and in particular, we provide a rigorous definition of the classical and the quantum pattern recognition problems, respectively. Our framework allows for the introduction of non-trivial correlations (as entanglement or discord) between the different species involved, opening the door to a new way of harnessing these physical resources for solving pattern recognition problems. Finally, we present some examples and discuss the computational complexity of the quantum pattern recognition problem, showing that the most important quantum computation algorithms can be described as non-Kolmogorovian pattern recognition problems.

MSC:

81P68 Quantum computation
68Q12 Quantum algorithms and complexity in the theory of computing
68T10 Pattern recognition, speech recognition

Software:

PRMLT

References:

[1] Aaronson, S; Ambainis, A, The need for structure in quantum speedups, Theory of Computing, 10, 133-166, (2014) · Zbl 1298.81045 · doi:10.4086/toc.2014.v010a006
[2] Abramsky, S; Tannen, V (ed.); Wong, L (ed.); Libkin, L (ed.); Fan, W (ed.); Tan, W-C (ed.); Fourman, M (ed.), Relational databases and bell’s theorem, No. 8000, (2013), London
[3] Aerts, D; Aerts, D (ed.); Durt, T (ed.); Czachor, M (ed.), Reality and probability: introducing a new type of probability calculus, (2002), Singapore · Zbl 1068.81503 · doi:10.1142/4885
[4] Aerts, D; Sassoli, M, The unreasonable success of quantum probability I: quantum measurements as uniform fluctuations, Journal of Mathematical Psychology, 67, 51-75, (2015) · Zbl 1354.81005 · doi:10.1016/j.jmp.2015.01.003
[5] Aerts, D; Sozzo, S; Veloz, T, New fundamental evidence of non-classical structure in the combination of natural concepts, Philosophical Transactions of the Royal Society A, 374, 20150095, (2015) · doi:10.1098/rsta.2015.0095
[6] Aïmeur, E; Brassard, G; Gambs, S; Lamontagne, L (ed.); Marchand, M (ed.), Machine learning in a quantum world, 431-442, (2006), Berlin
[7] Al-Adilee, AM; Nánásiová, O, Copula and s-map on a quantum logic, Information Sciences, 179, 4199-4207, (2009) · Zbl 1180.81005 · doi:10.1016/j.ins.2009.08.011
[8] Barnum, H; Wilce, A, Information processing in convex operational theories, Electronic Notes in Theoretical Computer Science, 270, 3-15, (2011) · Zbl 1348.81130 · doi:10.1016/j.entcs.2011.01.002
[9] Barnum, H; Barret, J; Leifer, M; Wilce, A, A generalized no-broadcasting theorem, Physical Review Letters, 99, 240501, (2007) · doi:10.1103/PhysRevLett.99.240501
[10] Barnum, H; Duncan, R; Wilce, A, Symmetry, compact closure and dagger compactness for categories of convex operational models, Journal of Philosophical Logic, 42, 501-523, (2013) · Zbl 1273.81028 · doi:10.1007/s10992-013-9280-8
[11] Beltrametti, E. G., & Cassinelli, G. (1981). The logic of quantum mechanics. Reading: Addison-Wesley. · Zbl 0504.03026
[12] Bengtsson, I., & Zyczkowski, K. (2006). Geometry of quantum states: An introduction to quantum entanglement. Cambridge: Cambridge University Press. · Zbl 1146.81004 · doi:10.1017/CBO9780511535048
[13] Bishop, C. (2006). Pattern recognition and machine learning. Simgapore: Springer. · Zbl 1107.68072
[14] Bosyk, GM; Zozor, S; Holik, F; Portesi, M; Lamberti, PW, A family of generalized quantum entropies: definition and properties, Quantum Information Processing, 15, 3393-3420, (2016) · Zbl 1348.81075 · doi:10.1007/s11128-016-1329-5
[15] Bratteli, O., & Robinson, D. W. (1997). Operator algebras and quantum statistical mechanics (Vol. 1). Berlin: Springer. · Zbl 0903.46066 · doi:10.1007/978-3-662-03444-6
[16] Buhagiar, D; Chetcuti, E; Dvurečenskij, A, On gleason’s theorem without Gleason, Foundations of Physics, 39, 550-558, (2009) · Zbl 1175.81005 · doi:10.1007/s10701-008-9265-6
[17] Chen, TL; Chen, FY, An intelligent pattern recognition model for supporting investment decisions in stock market, Information Sciences, 346, 261-274, (2016) · doi:10.1016/j.ins.2016.01.079
[18] Clifton, R; Halvorson, H, Entanglement and open systems in algebraic quantum field theory, Studies in History and Philosophy of Modern Physics, 32, 1-31, (2001) · Zbl 1222.81108 · doi:10.1016/S1355-2198(00)00033-2
[19] Dalla Chiara, M. L., Giuntini, R., & Greechie, R. (2004). Reasoning in quantum theory. Dordrecht: Kluwer Academic Publishers. · Zbl 1059.81003 · doi:10.1007/978-94-017-0526-4
[20] D’Espagnat, D. (1976). Conceptual foundations of quantum mechanics. Reading, MA: Benjaming.
[21] Döring, A, Kochen-Specker theorem for von Neumann algebras, International Journal of Theoretical Physics, 44, 139-160, (2005) · Zbl 1099.81011 · doi:10.1007/s10773-005-1490-6
[22] Gleason, A, Measures on the closed subspaces of a Hilbert space, Journal of Mathematics Mechanics, 6, 885-893, (1957) · Zbl 0078.28803
[23] Gudder, S. P. (1979). Stochastic methods in quantum mechanics. New York: Elsevier North Holland. · Zbl 0439.46047
[24] Guţă, M; Kotlowski, W, Quantum learning: asymptotically optimal classification of qubit states, New Journal of Physics, 12, 12303, (2010) · Zbl 1448.68386
[25] Haag, R. (1996). Local quantum physics fields, particles, algebras. Book texts and monographs in physics. Berlin: Springer. · Zbl 0857.46057
[26] Halvorson, H; Müger, M; Butterfield, JB (ed.); Earman, JE (ed.), Algebraic quantum field theory, 731-922, (2006), Amsterdam
[27] Hamhalter, J. (2003). Quantum measure thoery. Berlin: Springer. · doi:10.1007/978-94-017-0119-8
[28] Holik, F; Bosyk, G; Bellomo, G, Quantum information as a non-Kolmogorovian generalization of shannon’s theory, Entropy, 17, 7349-7373, (2015) · doi:10.3390/e17117349
[29] Holik, F; Plastino, A; Saenz, M, Natural information measures in cox’ approach for contextual probabilistic theories, Quantum Information & Computation, 16, 0115-0133, (2016)
[30] Horn, D; Assaf, G, Algorithm for data clustering in pattern recognition problems based on quantum mechanics, Physical Review Letters, 88, 018702, (2002) · doi:10.1103/PhysRevLett.88.018702
[31] Kalmbach, G. (1983). Orthomodular lattices. San Diego: Academic Press. · Zbl 0512.06011
[32] Kochen, S; Specker, E, On the problem of hidden variables in quantum mechanics, Journal of Mathematics and Mechanics, 17, 59-87, (1967) · Zbl 0156.23302
[33] Kolmogorov, A. N. (1933). Foundations of probability theory. Berlin: Springer.
[34] Ledda, A; Sergioli, G, Towards quantum computational logics, International Journal of Theoretical Physics, 49, 3158-3165, (2010) · Zbl 1204.81046 · doi:10.1007/s10773-010-0368-4
[35] Monràs, A., Sentís, G., & Wittek, P. (2016). Inductive quantum learning: Why you are doing it almost right. arXiv:1605.07541v1
[36] Nielsen, M. A., & Chuang, I. L. (2010). Quantum computation and quantum information. Cambridge: Cambridge University Press. · Zbl 1288.81001 · doi:10.1017/CBO9780511976667
[37] Rédei, M. (1998). Quantum logic in algebraic approach. Dordrecht: Kluwer Academic Publishers. · Zbl 0910.03038 · doi:10.1007/978-94-015-9026-6
[38] Rédei, M., & Summers, S. (2007). Quantum probability theory. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 38(2), 390-417. · Zbl 1223.46058 · doi:10.1016/j.shpsb.2006.05.006
[39] Sasaki, M; Carlini, A, Quantum learning and universal quantum matching machine, Physical Review A, 66, 022303, (2002) · doi:10.1103/PhysRevA.66.022303
[40] Sasaki, M; Carlini, A; Jozsa, R, Quantum template matching, Physical Review A, 64, 022317, (2001) · doi:10.1103/PhysRevA.64.022317
[41] Schuld, M; Sinayskiyab, I; Petruccione, F, An introduction to quantum machine learning, Contemporary Physics, 56, 172-185, (2014) · doi:10.1080/00107514.2014.964942
[42] Schützhold, R, Pattern recognition on a quantum computer, Physical Review A, 67, 062311, (2003) · doi:10.1103/PhysRevA.67.062311
[43] Sentís, G; Guţă, M; Adesso, G, Quantum learning of coherent states, EPJ Quantum Technology, 2, 17, (2015) · doi:10.1140/epjqt/s40507-015-0030-4
[44] Sergioli, G., Santucci, E., Didaci, L., Miszczak, J., & Giuntini, R. A quantum-inspired version of the nearest mean classifier. Soft Computing. · Zbl 1398.68458
[45] Svozil, K, Quantum scholasticism: on quantum contexts, counterfactuals, and the absurdities of quantum omniscience, Information Sciences, 179, 535-541, (2009) · Zbl 1162.81322 · doi:10.1016/j.ins.2008.06.012
[46] Trugenberger, CA, Quantum pattern recognition, Quantum Information Processing, 1, 471-493, (2002) · doi:10.1023/A:1024022632303
[47] von Neumann, J. (1996). Mathematical foundations of quantum mechanics (12th ed.). Princeton: Princeton University Press.
[48] Yang, YG; Tian, J; Lei, H; Zhou, YH; Shi, WM, Novel quantum image encryption using one-dimensional quantum cellular automata, Information Sciences, 345, 257-270, (2016) · doi:10.1016/j.ins.2016.01.078
[49] Yngvason, J, The role of type III factors in quantum field theory, Reports on Mathematical Physics, 55, 135-147, (2005) · Zbl 1140.81427 · doi:10.1016/S0034-4877(05)80009-6
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