Abstract
Disclosed are techniques for calibrating a quantum random number generator (QRNG) system using machine learning to detect noise, drift, and entropy degradation across devices and the QRNG pipeline. One or more models evaluate device data, entropy quality, and system conditions to determine whether recalibration is needed, which devices should be recalibrated, and when recalibration should occur. The techniques may also support predictive recalibration and calibration planning to improve entropy quality and overall QRNG reliability.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kundu, Ashish; Kompella, Ramana; Zhao, Peng; and Kaur, Eneet, "CALIBRATION OF A QUANTUM RANDOM NUMBER GENERATOR SYSTEM USING MACHINE LEARNING", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10437