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Deep Learning-Based Min-Entropy-Accelerated Evaluation for High-Speed Quantum Random Number Generation.

Xiaomin Guo1,2, Wenhe Zhou1,2, Yue Luo1,2

  • 1Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China.

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Summary
This summary is machine-generated.

This study enhances quantum random number generation (QRNG) using polarization-controlled heterodyne detection. It achieves high-speed, secure random bit generation and rapid entropy evaluation, improving practical QRNG applications.

Keywords:
deep convolutional neural networkdual-quadrature heterodyne detectionquantum conditional min-entropyquantum cryptographyquantum random number generation

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Area of Science:

  • Quantum Information Science
  • Secure Communication Technologies
  • Applied Physics

Background:

  • Secure communication relies on high-speed, high-security quantum random number generation (QRNG).
  • Practical QRNG systems face non-idealities affecting efficiency and security.
  • Accurate entropy evaluation is crucial for quantifying randomness.

Purpose of the Study:

  • To enhance QRNG efficiency and security using a novel responsive approach.
  • To investigate the impact of system non-idealities on quantum randomness.
  • To develop a fast and accurate method for entropy evaluation in QRNG.

Main Methods:

  • Utilized polarization-controlled heterodyne detection to measure vacuum shot noise fluctuations.
  • Analyzed imbalanced detection, amplitude-phase overlap, and security parameters on quantum conditional min-entropy.
  • Developed a deep convolutional neural network (CNN) for rapid entropy assessment.

Main Results:

  • Achieved a true random bit extraction ratio of 83.16% at 37.25 Gbps for high-security parameters.
  • Demonstrated mitigation of randomness overestimation and enhanced security against eavesdropping.
  • CNN processed extensive quadrature data rapidly with high accuracy (MAPE of 0.004).
  • Dual-quadrature heterodyne detection exceeded 85 Gbps generation rate.

Conclusions:

  • The proposed method significantly enhances QRNG performance and security.
  • Rapid entropy evaluation using CNN accelerates practical QRNG deployment.
  • This work advances the development of high-speed, secure random number generation.