The quantum random number generator (QRNG) is theoretically capable of generating unpredictable random numbers based on the inherent uncertainty of quantum mechanics, which is of paramount importance for information security. However, the security of practical QRNG is susceptible to the influence of unknown classical noise introduced by imperfect measurement devices, leading to potential security threats. In this paper, we propose a deep learning-based prediction model for analyzing the bidirectional security of QRNG where the quantum source is contaminated by classical noise. Firstly, we train a deep learning model capable of evaluating the randomness of mixed entropy source composed of quantum source and classical source, which exhibits excellent performance and effectively avoids the limitations of Statistical tests in evaluating the randomness of the mixture of quantum and classical source results. Secondly, systematically analyzes the impact of non-ideal measurement devices on the practical security of the continuous variable QRNG, which provides an explicit basis for compensating the discrepancy between theory and experiment. Finally, we perform correlation detection on QRNG output sequences with a deep learning model and focus on both the forward and backward security of random numbers. Through bidirectional security detection, random number sequences that may be biased or manipulated can be more accurately and comprehensively evaluated, further preventing potential correlations from opening security holes for eavesdroppers.
The quantum random number generator (QRNG) is theoretically capable of generating unpredictable random numbers based on the inherent uncertainty of quantum mechanics, which is of paramount importance for information security. However, the security of practical QRNG is susceptible to the influence of unknown classical noise introduced by imperfect measurement devices, leading to potential security threats. In this paper, we propose a deep learning-based prediction model for analyzing the bidirectional security of QRNG where the quantum source is contaminated by classical noise. Firstly, we train a deep learning model capable of evaluating the randomness of mixed entropy source composed of quantum source and classical source, which exhibits excellent performance and effectively avoids the limitations of Statistical tests in evaluating the randomness of the mixture of quantum and classical source results. Secondly, systematically analyzes the impact of non-ideal measurement devices on the practical security of the continuous variable QRNG, which provides an explicit basis for compensating the discrepancy between theory and experiment. Finally, we perform correlation detection on QRNG output sequences with a deep learning model and focus on both the forward and backward security of random numbers. Through bidirectional security detection, random number sequences that may be biased or manipulated can be more accurately and comprehensively evaluated, further preventing potential correlations from opening security holes for eavesdroppers.
Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.
A quantum random-number generator (QRNG) can theoretically generate unpredictable random numbers with perfect devices and is an ideal and secure source of random numbers for cryptography. However, the practical implementations always contain imperfections, which will greatly influence the randomness of the final output and even open loopholes to eavesdroppers. Recently, Thewes et al. experimentally demonstrated a continuous-variable eavesdropping attack, based on heterodyne detection, on a trusted continuous-variable QRNG in Phys. Rev. A 100, 052318 (2019), yet like in many other practical continuous-variable QRNG studies, they always supposed the local oscillator was stable and ignored its fluctuation, which might lead to security threats such as wavelength attack. In this work, based on the theory of the conditional min-entropy, imperfections of the practical security of continuous-variable QRNGs are systematically analyzed, especially the local oscillator fluctuation under imbalanced homodyne detection. Experiments of a practical QRNG based on vacuum fluctuation are demonstrated to show the influence of local oscillator fluctuation on the total measurement noise variances and the practical conditional min-entropy with beam splitters of different transmittances. Moreover, a local oscillator monitoring method is proposed for the practical continuous-variable QRNG, which can be used to calibrate the practical conditional min-entropy.
Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electrical noise). In addition, our model detects a decrease in the randomness of the QRNG random numbers when the electrical noise intensity increases to a certain correlation. The results show that machine learning (ML) can be used as a measure of the quality of QRNG.
Abstract As a fundamental phenomenon in nature, randomness has a wide range of applications in the fields of science and engineering. Among different types of random number generators (RNG), quantum random number generator (QRNG) is a kind of promising RNG as it can provide provable true random numbers based on the inherent randomness of fundamental quantum processes. Nevertheless, the randomness from a QRNG can be diminished (or even destroyed) if the devices (especially the entropy source devices) are not perfect or ill-characterized. To eliminate the practical security loopholes from the source, source-independent QRNGs, which allow the source to have arbitrary and unknown dimensions, have been introduced and become one of the most important semi-device-independent (DI) QRNGs. Herein a method that enables ultra-fast unpredictable quantum random number generation from quadrature fluctuations of quantum optical field without any assumptions on the input states is proposed. Particularly, to estimate a lower bound on the extractable randomness that is independent from side information held by an eavesdropper, a new security analysis framework is established based on the extremality of Gaussian states, which can be easily extended to design and analyze new semi-DI continuous variable QRNG protocols. Moreover, the practical imperfections of the QRNG including the effects of excess noise, finite sampling range, finite resolution and asymmetric conjugate quadratures are taken into account and quantitatively analyzed. Finally, the proposed method is experimentally demonstrated to obtain high secure random number generation rates of 15.07 Gbits s −1 in off-line configuration and can potentially achieve 6 Gbits s −1 by real-time post-processing.
Abstract A quantum random number generator (QRNG) as a genuine source of randomness is essential in many applications, such as number simulation and cryptography. Recently, a source-independent quantum random number generator (SI-QRNG), which can generate secure random numbers with untrusted sources, has been realized. However, the measurement loopholes of the trusted but imperfect devices used in SI-QRNGs have not yet been fully explored, which will cause security problems, especially in high-speed systems. Here, we point out and evaluate the security loopholes of practical imperfect measurement devices in SI-QRNGs. We also provide corresponding countermeasures to prevent these information leakages by recalculating the conditional minimum entropy and adding a monitor. Furthermore, by taking into account the finite-size effect, we show that the influence of the afterpulse can exceed that of the finite-size effect with the large number of sampled rounds. Our protocol is simple and effective, and it promotes the security of SI-QRNG in practice as well as the compatibility with high-speed measurement devices, thus paving the way for constructing ultrafast and security-certified commercial SI-QRNG systems.
Recently, a source-independent quantum random number generator (SI-QRNG), which can generate secure random numbers with untrusted sources, has been realized. However, the measurement loopholes of the trusted but imperfect devices used in SI-QRNGs have not yet been fully explored. Here, we point out and evaluate the security loopholes of practical imperfect measurement devices in SI-QRNGs. We also provide corresponding countermeasures to prevent these information leakages by recalculating the conditional minimum entropy and adding a monitor. Furthermore, by taking into account the finite-size effect, we show that the influence of the afterpulse can exceed that of the finite-size effect with the large number of sampled rounds. Our protocol is simple and effective, and it promotes the security of SI-QRNG in practice as well as the compatibility with high-speed measurement evices, thus paving the way for constructing ultrafast and security-certified commercial SI-QRNG systems.