Hence, our result shows that ML has potentials inīenchmarking the quality of RNG devices. We further demonstrate the robustness of our ML approachīy applying it to uniformly distributed random numbers from the QRNG and aĬongruential RNG. After appropriate filtering and randomnessĮxtraction processes are introduced, our QRNG system, in turn, demonstrates its ML model successfully detects inherent correlations when the deterministic Machine learning (ML) analysis to investigate the impact of deterministicĬlassical noise in different stages of an optical continuous variable QRNG. Properties, the presence of classical noise in the measurement processĬompromises the integrity of a QRNG. Generators (QRNGs) are based on the intrinsic indeterministic nature of quantum Supposed to be truly random and unpredictable. ![]() These attacksĮxploit environmental information to predict generated random numbers that are Random number generators (RNGs) that are crucial for cryptographicĪpplications have been the subject of adversarial attacks.
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