Sub-Tb/s Physical Random Bit Generators Based on Direct Detection of Amplified Spontaneous Emission Signals

Argyris, Apostolos; Pikasis, Evangelos; Deligiannidis, Stavros; Syvridis, Dimitris

doi:10.1109/jlt.2012.2188377

Cited by 70 publications

(36 citation statements)

References 26 publications

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“…We increase the resolution, i.e., increase the number of elements in the covering while decreasing their volumes, to reach our best estimate of H is calculated using the worst point in each region, as in Eq. (27), while the constraints are calculated using the region average, as in Eq. (31), the average min-entropy bound increases with increasing resolution, making the procedure conservative at finite resolution.…”

Section: Our Goal Is Now To Minimize H [Wcp (X)] ∞mentioning

confidence: 99%

“…Nearly all experimental claims of QRNG to date implicitly or explicitly assume nonadversarial devices, with varying degrees of trust in their sources [2,3,[5][6][7][9][10][11][12][22][23][24][25][26][27][28]. To take the best-known example, splitting a single photon on an ideal 50:50 beam splitter gives a random direction to the photon, and this direction can be measured to give one perfectly random bit.…”

Section: Introductionmentioning

confidence: 99%

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Strong experimental guarantees in ultrafast quantum random number generation

2015

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We describe a methodology and standard of proof for experimental claims of quantum random-number generation (QRNG), analogous to well-established methods from precision measurement. For appropriately constructed physical implementations, lower bounds on the quantum contribution to the average min-entropy can be derived from measurements on the QRNG output. Given these bounds, randomness extractors allow generation of nearly perfect " -random" bit streams. An analysis of experimental uncertainties then gives experimentally derived confidence levels on the randomness of these sequences. We demonstrate the methodology by application to phase-diffusion QRNG, driven by spontaneous emission as a trusted randomness source. All other factors, including classical phase noise, amplitude fluctuations, digitization errors, and correlations due to finite detection bandwidth, are treated with paranoid caution, i.e., assuming the worst possible behaviors consistent with observations. A data-constrained numerical optimization of the distribution of untrusted parameters is used to lower bound the average min-entropy. Under this paranoid analysis, the QRNG remains efficient, generating at least 2.3 quantum random bits per symbol with 8-bit digitization and at least 0.83 quantum random bits per symbol with binary digitization at a confidence level of 0.999 93. The result demonstrates ultrafast QRNG with strong experimental guarantees.

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Section: Our Goal Is Now To Minimize H [Wcp (X)] ∞mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strong experimental guarantees in ultrafast quantum random number generation

2015

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“…In [12] a QRNG rate up to 1.1 Gbps was demonstrated and in [13], 6 Gbps QRNG rate. The main difference is that Jofre et al strongly modulate the laser diode, taking it below and above threshold, while Xu et al directly detect phase fluctuations in an above-threshold continuous wave regime.…”

Section: Introductionmentioning

confidence: 96%

“…Physical RNGs take as input data from a physical process believed to be random, for example electronic and thermal noise [4], chaotic semiconductor lasers [5] and amplified spontaneous emission signals [6]. A subset of physical RNGs, known as quantum RNGs (QRNGs) use a physical process with randomness derived from quantum processes.…”

Section: Introductionmentioning

confidence: 99%

“…Processes used include radioactive decay [7], two-path splitting of single photons [8], photon number path entanglement [9], amplified spontaneous emission [10], measurement of the phase noise of a laser [11][12][13], photon arrival time [14], and vacuum-seeded bistable processes [15]. These physical RNGs and QRNGs show a trade-off between speed of generation and surety of the random bits generated: chaotic and ASE sources [5,6,16,17] reach hundreds of Gbps using signals that include contributions from both random and in-principle predictable sources, e.g. detector noise.…”

Section: Introductionmentioning

confidence: 99%

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Ultra-fast quantum randomness generation by accelerated phase diffusion in a pulsed laser diode

Abellán¹,

Amaya²,

Jofre³

et al. 2014

Opt. Express

139

137

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Abstract:We demonstrate a high bit-rate quantum random number generator by interferometric detection of phase diffusion in a gain-switched DFB laser diode. Gain switching at few-GHz frequencies produces a train of bright pulses with nearly equal amplitudes and random phases. An unbalanced Mach-Zehnder interferometer is used to interfere subsequent pulses and thereby generate strong random-amplitude pulses, which are detected and digitized to produce a high-rate random bit string. Using established models of semiconductor laser field dynamics, we predict a regime of high visibility interference and nearly complete vacuum-fluctuation-induced phase diffusion between pulses. These are confirmed by measurement of pulse power statistics at the output of the interferometer. Using a 5.825 GHz excitation rate and 14-bit digitization, we observe 43 Gbps quantum randomness generation.

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Gaussian random number generator: Implemented in FPGA for quantum key distribution

Chen

et al. 2019

Int J Numerical Modelling

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Quantum key distribution is the process of using quantum communication to establish a shared key between two parties. It has been demonstrated the unconditional security and effective communication of quantum communication system can be guaranteed by an excellent Gaussian random number (GRN) generator with high speed and an extended random period. In this paper, we propose to construct the Gaussian random number generator by using field-programmable gate array (FPGA), which is able to process large data in high speed. We also compare three algorithms of GRN generation:Box-Muller algorithm, polarization decision algorithm, and central limit algorithm. We demonstrate that the polarization decision algorithm implemented in FPGA requires less computing resources and also produces a high-quality GRN through the null hypothesis test. KEYWORDSGaussian random numbers, quantum key distribution, field-programmable gate array, numerical modeling However, it is possible to obtain a pseudorandom quantum signal by a set of high-quality Gaussian random numbers (GRNs). 20 This requires the period of GRNs is long enough for guaranteeing the absolute security of QCK. On the other side, the speed of generating is also critical for effective communication between two parties. 21 Hence, an adequate Gaussian random source is economical and vital for continuous-variable quantum cryptography communication. 22 Whereas, the study of the Gaussian random number generator (GRNG) in continuous-variable QCK system is much complicated. Some excellent surveys of the GRNGs from the algorithmic perspective exist in the published literature of Thomas et al. 23 Thomas et al 23 compared their computational requirements and examined the quality. In this work, we choose three conventional algorithms for generating GRN: Box-Muller algorithm, 23,24 polarization decision algorithm, 23,25 and central limit algorithm. 23,26 The Box-Muller transform is one of the earliest exact transformation methods. 23 It produces a pair of GRNs from a couple of uniform numbers. The polar method is an exact method related to the Box-Muller transform and has a closely related 2-D graphical interpretation but uses a different approach to get the 2-D Gaussian distribution. 23,25 The probability density function describing the sum of multiple uniform random numbers (URNs) is obtained by convolving the constituent probability density function. Thus, by the central limit theorem, the probability density function of the sum of K URNs over the range (0, 1) will approximate a Gaussian distribution. Furthermore, Malik and Hemani 27 provide a potential capsulization of hardware GRNG architectures. In this work, we analyzed and compare these three algorithms of generating GRN for a continuous-variable quantum cryptography communication system. More importantly, we choose field-programmable gate array (FPGA) as hardware to construct GRNG architectures using the Box-Muller algorithm, polarization decision algorithm, and central limit algorithm. Generally, CPU executes instructions with...

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Sub-Tb/s Physical Random Bit Generators Based on Direct Detection of Amplified Spontaneous Emission Signals

Cited by 70 publications

References 26 publications

Strong experimental guarantees in ultrafast quantum random number generation

Strong experimental guarantees in ultrafast quantum random number generation

Ultra-fast quantum randomness generation by accelerated phase diffusion in a pulsed laser diode

Gaussian random number generator: Implemented in FPGA for quantum key distribution

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