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A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise
Min Huang1, Ziyang Chen1, Yichen Zhang2
1Department of Electronics, and Center for Quantum Information Technology, State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, Beijing 100871, China.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
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.
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.
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