Long-Term Test of a Fast and Compact Quantum Random Number Generator

Abstract

Random numbers are an essential resource to many applications, including cryptography and Monte Carlo simulations. Quantum random number generators (QRNGs) represent the ultimate source of randomness, as the numbers are obtained by sampling a physical quantum process that is intrinsically probabilistic. However, they are yet to be widely employed to replace deterministic pseudo random number generators (PRNG) for practical applications. QRNGs are regarded as interesting devices. However they are slower than PRNGs for simulations and are typically seen as clumsy laboratory prototypes, prone to failures and unreliable for cryptographic applications.Here we overcome these limitations and demonstrate a compact and self-contained QRNG capable of generating random numbers at a pace of 8 Gbit/s uninterruptedly for 71 days. During this period, the physical parameters of the quantum process were monitored in real time by self-checking functions implemented in the generator itself. At the same time, the output random numbers were analyzed with the most stringent suites of statistical tests. The analysis shows that the QRNG under test sustained the continuous operation without physical instabilities or hardware failures. At the same time, the output random numbers were analyzed with the most stringent suites of statistical tests, which were passed during the whole operation time. This extensive trial demonstrates the reliability of a robustly designed QRNG and paves the way to its use in practical applications based on randomness. * davide.marangon@crl.toshiba.co.uk; Accepted in IEEE/OSA Journal of Lightwave Technology

Atmospheric pressure air microplasma current time series for true random bit generation

“…However, for high-speed real-time encryption applications, RBGs are mostly dependent on off-chip sources of entropy such as chaotic semiconductor lasers [25][26][27][28]44 , optical and non-optical quantum fluctuations 33,45,46 , and others 47 . Photonic devices with high bandwidth are the most popular options, and are able to reach ultra-fast bit rates of tens and hundreds of Gbit/s 27,[48][49][50] but at the cost of further post-processing routines that actually increase artificially the overall throughput (for example by means of higher-order derivatives 50 ).…”

Real-time interferometric quantum random number generation on chip

“…It is worth stressing that this operation is performed in real time by the FPGA. Although the FIR filter does not provide the same security level as a randomness extractor, it is an effective debiasing technique that does not affect the generation rate and makes it possible to pass the statistical tests of randomness [33]. Other filters used for de-biasing of the raw data have been proposed in the literature [34,35], these are based on converting the raw data into a time series consisting of the derivative of the ADC signal amplitude.…”

Robust Chaos of Cubic Polynomial Discrete Maps with Application to Pseudorandom Number Generators

“…It guarantees that the zero solution of the error equation is asymptotically stable. From the chaos generalized synchronization (GS) theorem [28], systems (8) and (13) as well as (9) and (14) are GS with respect to the transformation = for any initial value ( (0), (0)) ∈ R 3 × R 3 and ( (0), (0)) ∈ R 3 × R 3 . Since is invertible, systems (13) and (14) are also chaotic.…”

“…where = 1, 2, 3, and are defined by (8) and (13), and and are defined by (9) and (14). First, introduce transformations 11 , 12 , 13 , and 14 : R → {0, 1, .…”

“…Pseudorandom numbers have wide range of applications in many fields, such as physical systems simulation [3][4][5][6], information encryption [7][8][9][10][11][12][13], entertainment [14,15], and computer simulation [16][17][18][19][20]. In the practical applications, pseudorandom algorithm has almost replaced the stochastic indicator and random number generator based on the hardware.…”