Quantum Fast Algorithm Computational Intelligence PT I: SW / HW Smart Toolkit

Authors

  • Sergey Victorovich Ulyanov Dubna State University

DOI:

https://doi.org/10.30564/aia.v1i1.619

Abstract

A new approach to a circuit implementation design of quantum algorithm gates for quantum massive parallel fast computing implementation is presented. The main attention is focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW & HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation is described. The method for performing Grover’s interference without product operations as Benchmark introduced. The background of developed information technology is the "Quantum / Soft Computing Optimizer" (QSCOptKBTM) software based on soft and quantum computational intelligence toolkit. Quantum genetic and quantum fuzzy inference algorithm gate design considered. The quantum information technology of imperfect knowledge base self-organization design of fuzzy robust controllers for the guaranteed achievement of intelligent autonomous robot the control goal in unpredicted control situations is described.

Keywords:

Quantum algorithm gate, Superposition, Entanglement, Interference, Quantum simulator

References

[1] O. Kyriienko, Quantum inverse iteration algorithm for near-term quantum devices [J]. arXiv:1901.09988v1 [quant-ph], 2019.

[2] L. Gyongyosi, Quantum circuit designs for gate-model quantum computer architectures [J]. arXiv:1803.02460v1 [quant-ph], 2018.

[3] R. M. Parrish, J. T. Iosue, A. Ozaeta, and P. L. McMahon, A Jacobi diagonalization and Anderson acceleration algorithm for variational quantum algorithm parameter optimization [J]. arXiv: 1904.03206v1 [quant-ph],2019.

[4] V. Dunjko and H. J. Briegel, Machine learning & artificial intelligence in the quantum domain: a review of recent progress [J]. Rep. Prog. Phys. 2018, 81(7): 074001 (67pp).DOI: 10.1088/1361-6633/aab406

[5] C.D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, Trapped-ion quantum computing: Progress and challenges [J]. arXiv: 1904.04178v1 [quant-ph], 2019.

[6] V. Bergholm, J. Izaac, M. Schuld et all, PennyLane: Automatic differentiation of hybrid quantum-classical computations [J]. arXiv: 1811.04968v2 [quant-ph], 2019.

[7] K. Bertels, I. Ashraf, R. Nane et all, Quantum computer architecture: Towards full-stack quantum accelerators [J]. arXiv: 1903.09575v1 [quant-ph], 2019.

[8] T. Peng, A. W. Harrow, M. Ozols and X. Wu, Simulating large quantum circuits on a small quantum computer [J]. arXiv: 1904.00102v1 [quant-ph], 2019.

[9] P. Krantz, M. Kjaergaard, F. Yan et all, A Quantum engineer’s guide to superconducting qubits [J]. arXiv:1904.06560v1 [quant-ph], 2019.

[10] National Academies of Sciences, Engineering, and Medicine. 2018. Quantum Computing: Progress and Prospects (E. Grumbling and M. Horowitz, Eds) [B]. The National Academies Press, Washington, DC.DOI: https://doi.org/10.17226/25196.

[11] M.A Nielsen and I.L Chuang, Quantum computation and quantum information [M]. Cambridge University Press, Cambridge, England, 2000.

[12] S. Ulyanov, V. Albu and I. Barchatova, Quantum algorithmic gates: Information analysis & design system in MatLab [M]. LAP Lambert Academic Publishing, Saarbrücken, 2014.

[13] S. Ulyanov, V. Albu and I. Barchatova, Design IT of Quantum Algorithmic Gates: Quantum search algorithm simulation in MatLab [M]. LAP Lambert Academic Publishing, Saarbrücken, 2014.

[14] S.V. Ulyanov, System and method for control using quantum soft computing [P]. US Patent No 7,383,235 B1, 2003; EP PCT 1 083 520 A2, 2001; Efficient simulation system of quantum algorithm gates on classical computer based on fast algorithm [P]. US Patent No 2006/0224547 A1, 2006.

[15] S. Ulyanov, Method and hardware architecture for controlling a process or for processing data based on quantum soft computing (Inventors: Ulyanov S., Rizzotto G.G., Kurawaki I., Amato P. and Porto D.) [P]. PCT Patent WO 01/67186 A1, 2000.

[16] D. M. Porto and S.V. Ulyanov, Hardware implementation of fast quantum searching algorithms and its application in quantum soft сomputing and intelligent control [C]. In: Proc. World Automation Congress (5th Intern. Symp. on Soft Computing for Industry). Seville, Spain, 2004 (paper ISSC I31).

[17] S.V.Ulyanov, et al. Quantum information and quantum computational intelligence: Classically efficient simulation of fast quantum algorithms (SW / HW Implementations). [M] Note del Polo, Milan Univ, 2005, 79.

[18] L.K. Grover, A fast quantum mechanical algorithm for database search [P]. US Patent US 6,317,766 B1, 2001.

[19] S. Bose, L. Rallan, V. Vedral, Communication capacity of quantum computation [J]. Phys Rev Lett. 2000, (85): 5448-5451.

[20] E. Arikan, An information-theoretic analysis of Grover’s algorithm [J]. arXiv:quant-ph/0210068v2, 2002.

[21] F. Ghisi and S. Ulyanov, The information role of entanglement and interference operators in Shor quantum algorithm gate dynamics [J]. J. of Modern Optics, 2000. 47(12): 2079-2090.

[22] M. Branciforte, A. Calabrò, D. M. Porto, and S.V. Ulyanov, Hardware design of main quantum algorithm operators and application in quantum search algorithm of unstructured large data bases [C]. In: Proc. of the 7th World Multi-Conference on Systems, Cybernetics and Informatics (SCI ’2003), Florida, Orlando, USA, 2003.

[23] J. Niwa, K. Matsumoto, H. Imai, General-purpose parallel simulator for quantum computing [J]. Physical Review A, 2002. 66(6).

[24] L. Valiant, Quantum computers that can be simulated classically in polynomial time [C]. In: ACM Proc. STOC’01, Greece, 2001: 114-123.

[25] L. Valiant, Quantum circuits that can be simulated classically in polynomial time [J]. SIAM J. Comput. 2002, 31(4):1229-1254.

[26] C. Huang, M. Newman, and M. Szegedy, Explicit lower bounds on strong quantum simulation [R]. arXiv:1804.10368v2 [quant-ph], 2018.

[27] A. Rybalov, E. Kagan, A. Rapoport and I. Ben-Gal, Fuzzy implementation of qubits operators [J]. Computer Science and Systems Biology. 2014. 7(5): 163-168.DOI:10.4172/jcsb.1000151

[28] T.M. Forcer, et all Superposition, entanglement and quantum computation [J]. Quantum Information and Computation, 2002, 2(2): 97-116.

[29] J. Pilch, J. Długopolski, An FPGA-based real quantum computer emulatorю [J]. J. of Computational Electronics, 2018. available:DOI:https://doi.org/10.1007/s10825-018-1287-5

[30] A.J. McCaskey, E.F. Dumitrescu, D. Liakh, M. Chen, W. Feng, T.S. Humble, A language and hardware independent approach to quantum–classical computing [J]. Software X 7, 2018, 2: 245-254.

[31] A.R. Colm, R.J. Blake R., B.D. Diego and T. A. Ohki, Hardware for dynamic quantum computing [R]. arXiv:1704.08314v1 [quant-ph], 2017.

[32] Zeng-Bing Chen, Quantum Neural network and soft quantum computing [R]. arXiv:1810.05025v1 [quant-ph], 2018.

[33] J. B. Vega, D. Hangleiter, M. Schwarz, R. Raussendorf, and J. Eisert, Architectures for quantum simulation showing a quantum speedup [J]. Physical Review, 2018, X 8: 021010.

[34] A.M. Childs, D. Maslov, Y. Nam, N. J. Ross and Y. Su, Toward the first quantum simulation with quantum speedup [J]. PNAS, 2018. 115(38): 9456-9461.DOI:https://doi.org/10.1073/pnas.1801723115

[35] K. Michielsen, M. Nocon, D. Willsch, F. Jin, T.Lippert, H. De Raedt, Benchmarking gate-based quantum computers [J.] . Computer Physics Communication, 2017, 220: 44-55.DOI:http://dx.doi.org/10.1016/j.cpc.2017.06.011

[36] Patrick J. Coles et all, Quantum algorithm implementations for beginners [R]. arXiv:1804.03719v1 [cs.ET], 2018

[37] K. A. Britt, F. A. Mohiyaddin, and T. S. Humble, Quantum accelerators for high-performance computing systems [R]. arXiv:1712.01423v1 [quant-ph], 2017.

[38] Zhao-Yun Chen and Guo-Ping Guo [R], QRunes: High-level language for quantum-classical hybrid programming [R]. arXiv:1901.08340v1 [quant-ph], 2019.

[39] Y. H. Lee, M. Khalil-Hani, and M. N.Marsono, An FPGA-based quantum computing emulation framework based on serial-parallel architecture [J]. Intern. J. of Reconfigurable Computing, 2016. Vol. 2016, Article, ID: 5718124.DOI:http://dx.doi.org/10.1155/2016/5718124

[40] F. K. Wilhelm et all, Entwicklungsstand Quantencomputer [M]. Federal Office for Information Security. Bonn, 2017.https://www.bsi.bund.de

[41] G. D. Paparo, V. Dunjko, A Makmal, M. A. Martin-Delgad, and H. J. Briegel [J], Quantum speedup for active learning agents. Pysical Review, 2014, X 4: 031002.

[42] Yi-Lin Ju, I-Ming Tsai, and Sy-Yen Kuo, Quantum circuit design and analysis for database search applications [J]. IEEE Trans. On Circuits and Systems. 2007, 54(11): 2552-2563.

[43] M. Suchara, Y. Alexeev, F. Chong, H. Finkel, H. Hoffmann, J. Larson, J. Osborn, and G. Smith, Hybrid quantum-classical computing architectures [C]. In: Proc. 3rd INTERN. WORKSHOP ON POST-MOORE’S ERA SUPERCOMPUTING (PMES), PMES Workshop, Dallas, 2018.http://j.mp/pmes18

[44] D. Koch, L. Wessing, P. M. Alsing, Introduction to coding quantum algorithms: A tutorial series using Qiskit [R]. arXiv:1903.04359v1 [quant-ph]. 2019

[45] S.V. Ulyanov, Quantum soft computing in control processes design: Quantum genetic algorithms and quantum neural network approaches [C]. In: Proc. WAC (ISSCI’) 2004 (5th Intern. Symp. on Soft Computing for Industry), Seville Spain, 2004, 17: 99-104.

[46] S.V. Ulyanov, Self-organizing quantum robust control methods and systems for situations with uncertainty and risk [P]. Patent US 8788450 B2, 2014.

[47] P. Chandra Shill, F. Amin, K. Murase, Parameter optimization based on quantum genetic algorithms for fuzzy logic controller [R]. Department of System Design Engineering University of Fukui, 3-9-1 Bunkyo, Fukui-910-8507, 2011.

[48] P. Chandra Shill, B. Sarker, M. Chowdhury Urmi, K. Murase. Quantum fuzzy controller for inverted pendulum system based on quantum genetic optimization [J] Intern. J. of Advanced Research in Computer Science, 2012.

[49] R. Lahoz-Beltra, Quantum genetic algorithms for computer scientists [J]. Computers. – 2016, 5: 24.

[50] Cheng-Wen Lee, Bing-Yi Lin. Applications of the chaotic quantum genetic algorithm with support vector regression in load forecasting [M], Switzerland, 2017.

[51] A. Arjmandzadeh, M. Yarahmadi, Quantum genetic learning control of quantum ensembles with Hamiltonian uncertainties [R], Switzerland, 2017.

[52] H. Wang, J. Liu, J. Zhi. The improvement of quantum genetic algorithm and its application on function optimization [R]. College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China, 2013.

[53] A. Malossini, E. Blanzieri, T. Calarco, QGA: A quantum genetic algorithm [R]. Technical Report # DIT-04-105. – University of Toronto, 2004.

[54] S.V. Ulyanov, K. Takahashi, L.V. Litvintseva and T. Hagiwara, Design of self-organized intelligent control systems based on quantum fuzzy inference: Intelligent system of systems engineering approach [C]. Proc. of IEEE Intern. Conf. SMC’ , Hawaii, USA, 2005, 4: 3835- 3840.

[55] L.V. Litvintseva and S.V. Ulyanov, Quantum fuzzy inference for knowledge base design in robust intelligent controllers [J]. J. of Computer and Systems Sciences Intern. 2007, 46(6): 908-961.

[56] S.V. Ulyanov, K. Takahashi, G.G. Rizzotto and I. Kurawaki, Quantum soft computing: Quantum global optimization and quantum learning processes – Application in AI, informatics and intelligent control processes [C]. In: Proc. of the 7th World Multi-Conference on Systems, Cybernetics and Informatics, (SCI ’2003), Florida, Orlando, USA, 2003.

[57] S. Ulyanov, F. Ghisi, V. Ulyanov, I. Kurawaki and L. Litvintseva [M] Simulation of Quantum Algorithms on Classical Computers, Universita degli Studi di Milano, Polo Didattico e di Ricerca di Crema, Note del Polo, 2000, 32.

[58] D.M. Porto, S.V. Ulyanov, K. Takahashi, and I.S. Ulyanov, Hardware implementation of fast quantum searching algorithms and its applications in quantum soft computing and intelligent control [C]. Proc. Word Automation Congress (WAC’2004), Seville, Spain, 2004.

[59] Ch. H. Papadimitriou and J. Tsitsiklis, Intractable problems in control theory [J]. SIAM J. Control and Optimization, 1986. 24 (4): 639-654.

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How to Cite

Ulyanov, S. V. (2019). Quantum Fast Algorithm Computational Intelligence PT I: SW / HW Smart Toolkit. Artificial Intelligence Advances, 1(1), 18–43. https://doi.org/10.30564/aia.v1i1.619

Issue

Vol. 1 , Iss. 1 (April 2019)

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Articles

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Copyright © 2019 Author(s)

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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