Optics and Precision Engineering, Volume. 27, Issue 7, 1492(2019)

Quantum random number Gaussian noise signal generator

YU Heng-wei1,2、*, SUN Xiao-juan2,3, WANG Xing-chen1,2, JIANG Ke2,3, WU-You2,3, CHENG Dong-bi1,2, SHI Zhi-ming2,3, JIA Yu-ping2,3, and LI Da-bing2,3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    The existing method used to generate Gaussian noise signals is to generate random numbers through mathematical calculation. However, this method cannot achieve a true random signal nor can it match the actual noise signal. In this study, a Gaussian noise signal generator based on a quantum random number is proposed. A single-photon detector detects the photon signal of the selected path as the source of the random number and realizes the Gaussian noise signal generator based on a true random number. The random number is processed by a weighted Girvan-Newman Gaussian algorithm to obtain the Gaussian noise signal, which is implemented using the Verilog language in a field programmable gate array. The results show that the amplitude of the generated noise signal varies from 0 to 255. A statistical analysis of the amplitude spectrum obeys the Gaussian distribution. The power spectrum of the noise signal fluctuates uniformly at approximately 20 dB and follows a uniform distribution, thus satisfying the characteristics of the Gaussian white noise. Compared with existing methods based on quantum random numbers, the source of the random number is distinct in the present case, and the proposed generator can achieve real randomness. A simple scheme for realizing a true random number for a Gaussian noise signal generator is therefore presented.

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    YU Heng-wei, SUN Xiao-juan, WANG Xing-chen, JIANG Ke, WU-You, CHENG Dong-bi, SHI Zhi-ming, JIA Yu-ping, LI Da-bing. Quantum random number Gaussian noise signal generator[J]. Optics and Precision Engineering, 2019, 27(7): 1492

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    Paper Information

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    Received: Dec. 11, 2018

    Accepted: --

    Published Online: Sep. 2, 2019

    The Author Email: Heng-wei YU (yhw520@mail.ustc.edu.cn)

    DOI:10.3788/ope.20192707.1492

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