Performance Optimization Method for Laser-Phase-Noise based Quantum Random Number Generation

TL;DR

This paper introduces a comprehensive physical model for laser-phase-noise-based quantum random number generators, enabling performance optimization by predicting raw data spectra and entropy, validated through simulation and experiments.

Contribution

A new detailed physical model for laser-phase-noise QRNGs that predicts raw data characteristics and guides optimal system configuration.

Findings

Model accurately predicts power spectrum and probability distribution.

Simulation and experimental results show significant agreement.

System configuration can be optimized for maximum entropy and rate.

Abstract

The quantum random number generation based on laser phase noise, which is featured with high generation rate and ease for photonic integration, has been extensively investigated and demonstrated. Despite these advancements, a theoretical model to achieve optimal performance in terms of maximizing the generation rate or entropy is still incomplete. In this work, a comprehensive physical model for this scheme is introduced to accurately predict the power spectrum and probability distribution of raw data, based on which the entropy source bandwidth and quantum min-entropy can be accordingly calculated and thus the system performance can be quantitatively evaluated. The model is sufficiently validated through both simulation and experiment with significant agreement under various setups. Furthermore, our proposal enables the priori configuration of experimental setups to achieve designed power spectrum and probability distribution of the raw data, thereby maximizing the generation rate or the min-entropy for system performance optimization.