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Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information.

ABSTRACT: Quantum random number generators promise perfectly unpredictable random numbers. A popular approach to quantum random number generation is homodyne measurements of the vacuum state, the ground state of the electro-magnetic field. Here we experimentally implement such a quantum random number generator, and derive a security proof that considers quantum side-information instead of classical side-information only. Based on the assumptions of Gaussianity and stationarity of noise processes, our security analysis furthermore includes correlations between consecutive measurement outcomes due to finite detection bandwidth, as well as analog-to-digital converter imperfections. We characterize our experimental realization by bounding measured parameters of the stochastic model determining the min-entropy of the system's measurement outcomes, and we demonstrate a real-time generation rate of 2.9 Gbit/s. Our generator follows a trusted, device-dependent, approach. By treating side-information quantum mechanically an important restriction on adversaries is removed, which usually was reserved to semi-device-independent and device-independent schemes.

SUBMITTER: Gehring T

PROVIDER: S-EPMC7840728 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information.

Gehring Tobias T Lupo Cosmo C Kordts Arne A Solar Nikolic Dino D Jain Nitin N Rydberg Tobias T Pedersen Thomas B TB Pirandola Stefano S Andersen Ulrik L UL

Nature communications 20210127 1

Quantum random number generators promise perfectly unpredictable random numbers. A popular approach to quantum random number generation is homodyne measurements of the vacuum state, the ground state of the electro-magnetic field. Here we experimentally implement such a quantum random number generator, and derive a security proof that considers quantum side-information instead of classical side-information only. Based on the assumptions of Gaussianity and stationarity of noise processes, our secu ...[more]

PMID: 33504789

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Project description:The volume of securely encrypted data transmission increases continuously in modern society with all things connected. Towards this end, true random numbers generated from physical sources are highly required for guaranteeing security of encryption and decryption schemes for exchanging sensitive information. However, majority of true random number generators (TRNGs) are mechanically rigid, and thus cannot be compatibly integrated with some specific flexible platforms. Herein, we present a flexible and stretchable bionic TRNG inspired by the uniqueness and randomness of biological architectures. The flexible TRNG film is molded from the surface microstructures of natural plants (e.g., ginkgo leaf) via a simple, low-cost, and environmentally friendly manufacturing process. In our proof-of-principle experiment, the TRNG exhibits a fast generation speed of up to 1.04 Gbit/s, in which random numbers are fully extracted from laser speckle patterns with a high extraction rate of 72%. Significantly, the resulting random bit streams successfully pass all randomness test suites including NIST, TestU01, and DIEHARDER. Even after 10,000 times cyclic stretching or bending tests, or during temperature shock (-25-80 °C), the bionic TRNG still reveals robust mechanical reliability and thermal stability. Such a flexible TRNG shows a promising potential in information security of emerging flexible networked electronics.Electronic supplementary materialSupplementary material (light path diagram of transmitted laser speckle, pseudo random pattern, statistical distribution of bionic microstructures, haze of the bionic TRNG film, multi-layer circular laser intensity pattern, percentage of bit 0/1 for different hashed images, Pearson correlation coefficient between 100 different speckle images, the whole process of randomness extraction, SEM images of the flexible TRNG film after 10,000 times stretching and bending, continuous work stability of the TRNG at low or high temperature, light path diagram of reflective laser speckle, and detailed randomness test results of NIST, TestU01, and DIEHARDER) is available in the online version of this article at 10.1007/s12274-022-4109-9.

Project description:Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.

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Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information.

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Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information.

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