Quantum Algorithm of Imperfect KB Self-organization Pt I: Smart Control-Information-Thermodynamic Bounds
Source: By:Sergey Victorovich Ulyanov
DOI: https://doi.org/10.30564/aia.v3i2.3171
Abstract: The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed in Part I. The smart control design with guaranteed achievement of these tradeoff interrelations is main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect in Part I (autonomous robot in unpredicted control situations) and II (swarm robots with imperfect KB exchanging between “master - slaves”) introduced: a new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered. References:[1]Litvintseva, L. V., Ulyanov, I. S., Ulyanov, S. V., Ulyanov, S. S. Quantum Fuzzy Inference for Know-ledge Base Design in Robust Intelligent Controllers [J]. Journal of Computer and Systems Sciences Inter-na-tional, 2007, 46(6): 908-961.DOI: 10.1134/S1064230707060081. [2]Litvintseva, L. V., Ulyanov, S. V. Intelligent Control Systems. I. Quantum Computing and Self-Organization Algorithm [J]. Journal of Computer and Systems Sci-ences International, 2009. 48. (6): 946-984.DOI: 10.1134/S1064230709060112. [3]Ulyanov, S. V. Intelligent Robust Control System Based on Quantum KB-Self-organization: Quantum Soft Computing and Kansei / Affective Engineering Technologies [J]. Springer International Publishing, 2014. pp. 37-48. [4]Ulyanov, S.V. Intelligent self-organized robust control design based on quantum/soft computing technologies and Kansei Engineering [J]. Computer Science J. of Moldova, 2013, 21 (2(62)): 242 - 279. [5]Ulyanov, S.V. Self-organizing quantum robust con-trol methods and systems for situations with uncer-tainty and risk [P]. Patent US 8788450 B2, 2014. [6]Ulyanov, S. V. Self-organized robust intelligent con-trol [M] Saarbrücken: LAP Lambert Academic Pub-lishing, 2015. 412 p. [7]Ulyanov, S.V. Quantum relativistic informatics [M]. LAP LAMBERT Academic Publishing, OmniScrip-tum GmbH & Co. KG, 2015. [8]Ahmadi, B., Salimi, S., Khorashad, A.S., Khei-randish, F. The quantum thermodynamic force re-sponsible for quantum state transformation and the flow and backflow of information [J]. SCIENTIFIC REPORTS, 2019, 9 (8746) DOI:https://doi.org/10.1038/s41598-019-45176-1. [9]Ahmadi, B., Salimi, S., Khorashad, A.S. Irrevers-ible work and Maxwell demon in terms of quantum thermodynamic force [J]. SCIENTIFIC REPORTS, 2021, 11 (2301) .DOI:https://doi.org/10.1038/s41598-021-81737-z. [10]Zhang, K., Wang, X., Zeng, Q. et al. Conditional Ent-ropy Production and Quantum Fluctuation Theorem of Dissipative Information [R]. arXiv:2105.06419v1 [quant-ph] 13 May 2021. [11]Nakamura, T., Hasegawa, H.H., Driebe, D.J., Reconsider-ation of the generalized second law based on information geometry [J]. J. Physics Communications, 2019, 3: 015015 .DOI:https://doi.org/10.1088/2399-6528/aafe1b. [12]Sieniutycz, S., et all. Framework for optimal control in multistage energy systems [J]. Physics Reports, 2000, 326(2). [13]Ulyanov, S.V. System and method for control using quantum soft computing [P]. Patent, US 6,578,018, B1, 2003. [14]van der Schaft, A.J. Theory of port-Hamiltonian sys-tems (Lectures 1, 2, and 3) [M]. Network Modeling and Control of Physical Systems. DISC, 2005. [15]van der Schaft, A., Jeltsema, D. Port-Hamiltonian Systems Theory: An Introductory Overview. Foun-da-tions and Trends R in Systems and Control [M]. 2014. 1(2-3): 173-378. DOI:https://doi.org/10.1561/2600000002. [16]Moxley, III. Quantum Port-Hamiltonian Network Theory: Universal Quantum Simulation with RLC Circuits [R]. Dartmouth College, Dartmouth Digital Commons. Hanover, NH 03755 USA. 2020. [17]Marcolli, M. Motivic information [R]. arX-iv:-1712.08703v1 [math-ph] 23 Dec 2017 [18]Combe, N.C., Manin, Yu.I., Marcolli, M. Geometry of information: classical and quantum aspects [R]. 2021 [available http: www.its.caltech.edu]. [19]Tribus, M., Shannon, P.T., Evans, R.B. Why ther-mo-dynamics is a logical consequence of information theory [J]. A. I. Ch. E. Journal. 1966, 12( 2): 244 - 248. [20]Bais, F.A., Farmer, J.D. Physics of information [R]. SFI WORKING PAPER: 2007-08-029. [www.santafe.edu]. The Handbook on the philosophy of information, Eds by J. van Benthem and P. Adriaans. 2009. [21]Sagawa, T., Ueda, M. Minimal Energy Cost for Ther-modynamic Information Processing: Measure-ment and Information Erasure[J]. Phys. Rev. Lett., 2009, 102(25): 250602. [Erratum. Phys. Rev. Lett. 106, 189901, 2011.]. [22]Horowitz, J. M., Sandberg, H. Second-law-like in-equalities with information and their interpretations [J]. New Journal of Physics, 2014, 16: 125007. [23]Sandberg, H, et al. Maximum work extraction and implementation costs for nonequilibrium Maxwell’s demon [J]. Physical Review E, 2014, (4): pp. 042119. [24]Haddad, W. M., Chellaboina, V., Nersesov, S. G. Thermodynamics: A Dynamical Systems Approach [M]. Princeton Series in Applied Mathematics Princ-eton. NJ: Princeton University Press, 2005. [25]Fukuda, T., Kawamoto, A., Arai, F. Micro mobile ro-bot in fluid,2nd report, Acquisition of swimming mo-tion by RBF Fuzzy neuro with unsupervised learning [J]. Trans. Japan Society of Mechanical Engineers, 1995, 61(591): 274-279. [26]Ulyanov, S.V., Fukuda, T. et al. Quantum and ther-modynamic conditions for artificial life of biological mobile micro-nano-robot with AI control (Report 2) [C]. Proc. 7th Int. Symposium on Micromachine and Human science, 1996, Nagoya, Japan: 241-248. [27]Ulyanov, S.V., Rizzotto, G.G., Fukuda, T. et al. Advanced intelligent control systems in non-linear mechatronics and robotics: From macro-to-mi-cro-systems [C]. Proc. of Eur. Conference on Circuit Theory and Design (ECCTD’99), 1999, Stresa, Italy, 2: pp. 983-986. [28]Xiao, J., Yang, L. Thermodynamic properties of α-helix protein: a soliton approach [J]. Phys. Rev., 1991, 44A(12): 8375-8379. [29]Bolterauer, H., Tuszynski, J.A., Sataric, M.V. Frölich and Davydov regimes in the dynamic of dipolar os-cillations of biological membranes [J]. Phys. Rev., 1991, 44A(2): 1366-1381. [30]Ravazy, M. Equation of motion approach to the prob-lem of damped motion in quantum mechanics [J]. Phys. Rev., 1990, 44A(3): 1211-1217. [31]Schuch, D. Nonunitary connection between explic-itly time-depended and nonlinear approaches for the description of dissipative quantum systems [J]. Phys. Rev., 1992, 55A(2): 935-940. [32]Ulyanov, S.V. Dynamic systems with fuzzy and ran-dom time-variant structures (stochastic vibrations, coherent states and solitons in classical, relativistic and quantum control systems) [M]. Eng. Cybernetics. 1992. Vol. 15. pp. 3-145. [33]Korsh, H.J., Steffen, H. Dissipative quantum dynam-ics, entropy production and irreversible evolution to-wards equilibrium [J]. J. Phys., 1987, 20A(12): 3787-3803. [34]Korsh, H.J., Steffen, H. Dissipative quantum dynam-ics: solution of the generalized von Neumann equa-tion for the damped harmonic oscillator [J]. J. Phys., 1992, 25(7): 2043-2064. [35]Ulyanov, S.V., Litvintseva, L.V. Design of self-orga-nized intelligent control systems based on quantum fuzzy inference: Intelligent system of systems engi-neering approach [C]. IEEE International Conference on Systems, Man and Cybernetics. 2005. Hawaii, USA, 10-12 Oct. 2005, 4: 3835-3840. [36]Ulyanov, S. V. Quantum fast algorithm computation-al intelligence PT I: SW / HW smart toolkit [J]. Arti-ficial Intelligence Advances, 2019, 1(1): 18-43. [37]Ito, S. Thermodynamics of information geometry as a generalization of the Glansdor-Prigogine criterion for stability [R]. arXiv:1908.09446v1 [cond-mat.stat-mech] 26 Aug 2019. [38]Brandão, F. The second laws of quantum thermo-dynamics [J]. PNAS, 2015, 112(11): 3275-3279. DOI:https://doi.org/10.1073/pnas.1411728112. [39]Zozor, S. On Generalized Stam Inequalities and Fish-er-Rényi Complexity Measures [J]. Entropy, 2017, 19(493): 1-31. DOI: https://doi.org/10.3390/e19090493. [40]Robinett, R. D., Wilson, D. G. Exergy and Irrevers-ible Entropy Production Thermodynamic Concepts for Control Design: Nonlinear Systems [C]. 14thMediterranean Conf. on Control and Automation, 2006: 1-8.DOI: https://doi.org/10.1109/MED.2006.328728. [41]Sagawa, T., Masahito, U. Generalized Jarzynski Equality under Nonequilibrium Feedback Control [J]. Phys. Rev. Lett., 2010, 104: 090602. DOI: https://doi.org/10.1103/PhysRevLett.104.090602. [42]Goold, J. The role of quantum information in ther-modynamics—a topical review [J]. J. Phys. A: Math. Theor., 2019, 49: 143001 (50pp).DOI: https://doi.org/10.1088/1751-8113/49/14/143001 [43]Vanchurin, V. The World as a Neural Network [J]. Entropy, 2020, 22(1210).DOI: https://doi.org/10.3390/e22111210. [44]Sagawa, T. Thermodynamic and logical reversibili-ties revisited [R]. arXiv: 131П2.1886v1 [cond- mat.stat-mech] 8 Nov 2013. [45]Yamano, T. Phase space gradient of dissipated work and information: A role of relative Fisher information [R]. arXiv: 131П2.2176v1 [cond-mat.stat-mech] 9 Nov 2013. [46]Ilgin, I.,Yang, I-Sh. Energy carries information [R]. arXiv:1402.0878v1 [hep-th] 4 Feb 2014. [47]Horowitz, J. M., Esposito M. Thermodynamics with continuous information flow [R]. arXiv:1402.3276v2 [cond-mat.stat-mech] 14 Feb 2014. [48]Renes, J. M. Work Cost of thermal operations in quantum and nano thermodynamics [R]. arX-iv:1402.3496v1 [math-ph] 14 Feb 2014. [49]Horowitz, J. M. Sagawa, T. Equivalent definitions of the quantum nonadiabatic entropy production [R]. arXiv:1403.7778v1 [quant-ph] 30 Mar 2014. [50]Lang, A.H., Fisher, Ch.K., Mehta, P. Thermody-namics of statistical inference by cells [R]. arX-iv:1405.4001v1 [physics.bio-ph] 15 May 2014. [51]Apollaro, T. J. G., Francica, G., Paternostro M., Campisi, M. Work statistics, irreversible heat and correlations build-up in joining two spin chains [R]. arXiv: 1406.0648v1 [cond-mat.stat-mech] 3 Jun 2014. [52]Gomez, C. Complexity and time [J]. Phys. Rev., 2020, D 101: 065016. [53]Funo, K., Watanabe, Yu., Ueda, M. Thermodynamic work gain from entanglement [J]. Phys. Rev., 2013, A88(5): 052319. [54]Toyabe, S., Sagawa, T., Ueda, M., Muneyuki, E., Sano, M. Experimental demonstration of informa-tion-to-energy conversion and validation of the gen-eralized Jarzynski equality [J]. Nature Physics, 2010. 6: 988-992. [55]Van der Meer, R., Ng, N. H. Y., Wehner, S. Smoothed generalized free energies for thermodynamics [J]. Phys. Rev. A, 2017, 96(6): Pp. 062135. [56]Mirkes, E.M. Universal Gorban’s Entropies: Geomet-ric Case Study. [J]. Entropy, 2020, 22(3):264; DOI:https://doi.org/10.3390/e22030264. [57]Khrennikov, A. Quantum-like model for uncon-scious-conscious interaction and emotional color-ing of perceptions and other conscious experiences [R]. arxiv.org/abs/2106.05191v1 [q-bio. NC] 6 June 2021 [58]Korenkov, V.V., Ulyanov, S.V., Shevchenko, A.A., Shevchenko, A.V. Intelligent cognitive robotic: Quantum cognitive computing technologies [M]. M.: Kurs, 2021.