Kansikuva

Tallennettuna:
Bibliografiset tiedot
Päätekijä: Golter, Steve
Aineistotyyppi: Recurso digital
Kieli:englanti
Julkaistu: Zenodo 2025
Aiheet:
Quantum random number generation Device-independent randomness Quantum cryptography Randomness certification Entropy extraction Proof-carrying randomness Bell test Homodyne detection Quantum information science Post-quantum cryptography
Linkit:https://doi.org/10.5281/zenodo.17080273
Tagit: Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!
Sisällysluettelo:
  • <p><strong>Q-STAR: Proof Carrying Quantum Classical Randomness with Device Independent Certification”</strong><br>Submitted to <em>Physical Review A / PRX Quantum</em> (APS, 2025).</p> <p>The Q-STAR system integrates three heterogeneous entropy sources—device-independent Bell-test certified randomness, a high-rate homodyne quantum random number generator, and a classical thermal noise source—into a unified framework that achieves both provable entropy guarantees and Gb/s throughput.</p> <p>This Zenodo package includes:</p> <ul> <li> <p><strong>Raw data (representative subsets)</strong></p> <ul> <li> <p>Homodyne quadrature ADC samples</p> </li> <li> <p>Bell-test outcomes (Alice/Bob settings and results)</p> </li> <li> <p>Thermal Johnson-noise time series</p> </li> </ul> </li> <li> <p><strong>Processed data</strong></p> <ul> <li> <p>Entropy rate estimates (Fig. 2)</p> </li> <li> <p>Homodyne power spectral densities (Fig. 3)</p> </li> <li> <p>Predictability and robustness tests (Fig. 4)</p> </li> <li> <p>Extractor validation p-values (Fig. 5)</p> </li> <li> <p>Proof-carrying metrics (Fig. 6)</p> </li> <li> <p>30-day stability traces (Fig. 7)</p> </li> </ul> </li> <li> <p><strong>Code and notebooks</strong></p> <ul> <li> <p>Python scripts for entropy estimation, cross-predictability analysis, extractor validation, and STARK proof metrics</p> </li> <li> <p>A Jupyter notebook to reproduce key figures</p> </li> </ul> </li> <li> <p><strong>Documentation</strong></p> <ul> <li> <p>README with instructions</p> </li> <li> <p>Requirements file for Python dependencies</p> </li> <li> <p>Dual licensing (CC-BY 4.0 for data, MIT for code)</p> </li> </ul> </li> </ul> <p>The datasets are representative subsets, provided to ensure reproducibility of all reported figures and tables while remaining lightweight for sharing. Full raw data and FPGA bitstreams are available upon reasonable request.</p>

Samankaltaisia teoksia