Research Cell: An International Journal of Engineering Sciences

Open Access Open Access  Restricted Access Subscription Access

Implementation of Random nature of Qubits for Random Number Generation via Simulation


Affiliations
1 Department of Computer Science International Islamic University, Islamabad 44000, Pakistan
2 Center of Theoretical Physics, Jamia Millia Islamia., New Delhi, India
3 Department of Physics, National University of Singapore 2 Science Drive 3, 117551, Singapore
4 Jordan University of Science and Technology, Jordan
 

Random Number Generation implemented through Quantum-Classical integration. The system includes a plural source of light with coherent states such that each state has an indeterminate number of photons. This varying Photon number produces varying current when input to an avalanche photodiode and the characteristics of this hardware element (Avalanche Photodiode) is changed by varying the temperature, pressure and electric field of the electronic system. The varying characteristics introduce Classical noise of Quantum origin that forms the basic idea for Random Number Generation. The varying electric field on the other hand increases the reverse voltage and hence acts as a gain for the photon incident on its hardware Every state has different photon number and corresponding photodiode characteristic that is being fetch to analog to digital converter that eventually generates an absolute random number. The gain value of photon is multiplexed with the actual message and acts as a modulation technique. We utilize dye laser simulation and rhodium molecule as a site for this implementation via test particle. As the parameters are shifted and varied, there is an increase in the quantum nature of particle and then starts becoming classical due to decoherence and noise factor and then eventually randomize. This property can be best utilized for random number generation owing to this randomization.

Keywords

Topology, Qubit, Dye Laser, Simulation.
User
Notifications
Font Size

  • Nisim Ofek, et al, Demonstrating Quantum Error Correction that extends the lifetime of Quantum Information, [arXi: 1602.04768v1]

  • Millisecond Coherence Time in a Tunable Molecular Electronic Spin Qubit, Joseph M. Zadrozny et al ACS Cent Sciv.1(9); 2015 Dec 23 PMC4827467

  • Luca Chirolli, Guido Burkand , Solid State Qubit, [arXiv: 0809.4716]

  • A short introduction to topological quantum computation Ville T. Lahtinen1 and Jiannis K. Pachos Sci post phy , 3 021,2017

  • A. Kitaev and J Preskill, Topological entanglement entropy, Phys.Rev.Lett, 96,110404 (2006)

  • I Bloch et al, Quantum Simulation with ultra cold Quantum Gases, Nat.Phys 8, 267 (2012)

  • Niklas M Gerges, Larz Firtz and Drik Schuricht, Topoloical order in the Kitaev/Majorana Chain in the presence of disorder and interaction, [arXiv: 1511.02817]

  • J.K Pahos et al, Revealing Anyonic features in toric code quantum simulation, New . Phys, 11, 083010 (2009)

  • A short introduction to topological quantum computation Ville T. Lahtinen1 and Jiannis K. Pachos Sci post phy , 3 021,2017

  • J.S Xu, K Sun, Y.J Han,C.E-Li, J.K Pachos and G.C Guo, Simuating the Exchange of Majorana Zero Modes with a Photonic System, Nat. Commun, 7, 13194(2016)

  • Barry Coyle, D., Guerra, D. V. & Kay, R. B., 1995. An interactive numerical model of diode-pumped, Q-switched/cavity-dumped lasers.. Journal of Physics D: Applied Physics, 28(3), p. 452.

  • Kay, R. B. & Waldman, G. S., 1965. Complete Solutions to the Rate Equations Describing Q‐Spoiled and PTM Laser Operation.. Journal of Applied Physics, 36(4), pp. 1319-1323.

  • Djurdje, Jacek, C. & Klinowski, 1995. Taboo search: an approach to the multiple minima problem. Science, 267(5198), pp. 664-666.

  • Edward, F., Goldstone, J. & Gutmann, S., 2002. Quantum adiabatic evolution algorithms versus simulated annealing.. arXiv: arXiv preprint quant-ph/0201031.

  • Farhi, E., J, G., S, G. & D., N., 2008. How to make the quantum adiabatic algorithm fail.. International Journal of Quantum Information., 06(03), pp. 503-516.

  • Farhi, E. et al., 2001. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem.. Science, 292(5516), pp. 472-475.

  • Farhi, E., J, G., S, G. & M., S., 2000. Quantum computation by adiabatic evolution.. arXiv: arXiv preprint quant-ph/0001106

  • Jamil, M. & Yang, X.-S., 2013. A literature survey of benchmark functions for global optimisation problems.. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), pp. 150-194.

  • oender, C., AR, K., GT, T. & L., S., 1982. A stochastic method for global optimization. Mathematical programming., 22(1), pp. 125-140.

  • Djurdje, Jacek, C. & Klinowski, 1995. Taboo search: an approach to the multiple minima problem. Science, 267(5198), pp. 664-666.

  • Edward, F., Goldstone, J. & Gutmann, S., 2002. Quantum adiabatic evolution algorithms versus simulated annealing..[ arXiv: preprint quant-ph/0201031.

  • Farhi, E., J, G., S, G. & D., N., 2008. How to make the quantum adiabatic algorithm fail.. International Journal of Quantum Information., 06(03), pp. 503-516.

  • Farhi, E. et al., 2001. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem.. Science, 292(5516), pp. 472-475.

  • Reichardt Ben, W., 2004. The quantum adiabatic optimization algorithm and local minima.. ACM, s.n.

  • Aaronson, S., 2015. Quantum machine learning: read the fine print. Nature Physics, 11(4), pp. 291-293.

  • Biamonte, J. et al., 2016. Quantum machine learning. arXiv: arXiv preprint.

  • Biamonte, J. et al., 2016. Quantum machine learning. arXiv: arXiv preprint.

  • Cai, X. et al., 2015. Entanglement-based machine learning on a quantum computer.. Physical review letters., 114(11), p. 110504.

  • Cohen, T. H., Leiserson, C., RL, R. & C, S., 1990. Introduction to algorithms. Cambridge MA: The MIT Press.

  • Kak, S., 2007. Quantum Mechanics and Artificial Intelligence. London, DOI.

  • Liu, S., Ying, L.and Shakkottai, S., Influence maximization in social networks: An Ising-model-based approach, In Proc.48th Annual Allerton Conference on Communication, Control, and Computing (2010)

  • Jianging, Han, F. & Liu, H., 2004. Challenges of big data analysis. National Science Review, 1(2), pp. 293-314.

  • Kay, R. B. & Waldman, G. S., 1965. Complete Solutions to the Rate Equations Describing Q‐Spoiled and PTM Laser Operation.. Journal of Applied Physics, 36(4), pp. 1319-1323.

  • Quantum Random Number Generators, arXiv:1604.03304v2

  • On the Hermatian optical phase operator Journal of Modern Optics Vol 36 page 1, 7-19

  • The physics of Quantum Mechanics by James Biney

  • Evolution and Prospects for single photo diode and quenching circuits by Scova vol 51 issue 9-10 pages 1164-1288

  • Avalanche Multiplication Noise characteristics David IEEE vol 45 Issue 10

  • Semiconductor devices by Malvino

  • Network Distributed Quantum Random Number Generation US patent US 2012/0221615 A1, Aug 30 2012

  • Debabrata Goswami, Adiabatic Quantum Computing with Phase Modulated Laser Pulses , arXiv:quant-ph/0507268


Abstract Views: 230

PDF Views: 0




  • Implementation of Random nature of Qubits for Random Number Generation via Simulation

Abstract Views: 230  |  PDF Views: 0

Authors

Sahil Imtiyaz
Department of Computer Science International Islamic University, Islamabad 44000, Pakistan
Jamal A. Nasir
Department of Computer Science International Islamic University, Islamabad 44000, Pakistan
Junaid Ul Haq
Center of Theoretical Physics, Jamia Millia Islamia., New Delhi, India
Qin Zhao
Department of Physics, National University of Singapore 2 Science Drive 3, 117551, Singapore
Wail Mardini
Jordan University of Science and Technology, Jordan

Abstract


Random Number Generation implemented through Quantum-Classical integration. The system includes a plural source of light with coherent states such that each state has an indeterminate number of photons. This varying Photon number produces varying current when input to an avalanche photodiode and the characteristics of this hardware element (Avalanche Photodiode) is changed by varying the temperature, pressure and electric field of the electronic system. The varying characteristics introduce Classical noise of Quantum origin that forms the basic idea for Random Number Generation. The varying electric field on the other hand increases the reverse voltage and hence acts as a gain for the photon incident on its hardware Every state has different photon number and corresponding photodiode characteristic that is being fetch to analog to digital converter that eventually generates an absolute random number. The gain value of photon is multiplexed with the actual message and acts as a modulation technique. We utilize dye laser simulation and rhodium molecule as a site for this implementation via test particle. As the parameters are shifted and varied, there is an increase in the quantum nature of particle and then starts becoming classical due to decoherence and noise factor and then eventually randomize. This property can be best utilized for random number generation owing to this randomization.

Keywords


Topology, Qubit, Dye Laser, Simulation.

References