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A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise.

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Summary
This summary is machine-generated.

This study presents a quantum random number generator that directly produces Gaussian distributed random sequences. This method is crucial for applications like quantum key distribution requiring specific randomness properties.

Keywords:
Gaussian distributiongoodness of fit testquantum random number generatorvacuum fluctuation

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Area of Science:

  • Quantum physics
  • Information security

Background:

  • Quantum random number generators (QRNGs) offer genuine randomness but often produce uniform distributions.
  • Existing QRNGs may not meet the specific distribution requirements for advanced applications like continuous-variable quantum key distribution (CV-QKD).

Purpose of the Study:

  • To demonstrate a practical quantum random number generation scheme producing Gaussian distributed random sequences.
  • To analyze the influence of sampling devices on the generated randomness.
  • To develop a post-processing method for enhancing the precision and maintaining the quality of random sequences.

Main Methods:

  • Utilizing vacuum shot noise measurement for direct Gaussian random number generation.
  • Analyzing the impact of sampling devices within the practical QRNG system.
  • Implementing a post-processing technique to refine distribution and autocorrelation properties.

Main Results:

  • A practical quantum random number generator directly producing Gaussian distributed sequences was demonstrated.
  • The impact of sampling devices on the system's performance was analyzed.
  • A post-processing method successfully extended the precision of generated numbers to over 20 bits.
  • Generated sequences passed normality and randomness tests, confirming Gaussian distribution and high-quality randomness.

Conclusions:

  • The developed quantum random number generation scheme effectively produces high-precision Gaussian distributed random sequences.
  • The method addresses the limitations of uniform distribution in standard QRNGs for specific applications.
  • The findings support the use of these generated sequences in demanding systems like CV-QKD.