Practical and fast quantum random number generation based on photon arrival time relative to external reference

Nie, You-Qi; Zhang, Hongfei; Zhang, Zhen; Wang, Jian; Ma, Xiongfeng; Zhang, Jun; Pan, Jian-Wei

doi:10.1063/1.4863224

Cited by 112 publications

(81 citation statements)

References 12 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although the reported sky brightness at Xinglong is in the range of [16,22] magnitude per arcsec 2 [11], which is similar to what was observed in other observatories. Being located at a lower altitude and close to major cities (Beijing, Chengde, and a few others), the experiment at Xinglong observatory may be affected by scattered light due to human activities.…”

Section: Estimating Signal-to-noise Ratio For Rng With Photons From Qsupporting

confidence: 65%

“…The main results are summarized in Table I. First, we notice that the photon counting signal rates exceed 10 6 s −1 (which is within the linear operation mode of the SPAD in use) for CRSS with lower magnitude, demonstrating that this method is as efficient as laser-based RNGs in generating random numbers [19][20][21][22][23][24][25]. Second, despite the dramatic fluctuation of signal rates (shown by signal ranges in Table I ), the true signal rate (with background subtracted) scales with magnitude as expected, as indicated in Fig.…”

mentioning

confidence: 91%

See 1 more Smart Citation

Random Number Generation with Cosmic Photons

Bai

Liu

et al. 2017

Phys. Rev. Lett.

Self Cite

View full text Add to dashboard Cite

Random numbers are indispensable for a variety of applications ranging from testing physics foundation to information encryption. In particular, nonlocality tests provide a strong evidence to our current understanding of nature -quantum mechanics. All the random number generators (RNG) used for the existing tests are constructed locally, making the test results vulnerable to the freedom-of-choice loophole. We report an experimental realization of RNGs based on the arrival time of cosmic photons. The measurement outcomes (raw data) pass the standard NIST statistical test suite. We present a realistic design to employ these RNGs in a Bell test experiment, which addresses the freedom-of-choice loophole.

show abstract

Section: Estimating Signal-to-noise Ratio For Rng With Photons From Qsupporting

confidence: 65%

mentioning

confidence: 91%

Random Number Generation with Cosmic Photons

Bai

Liu

et al. 2017

Phys. Rev. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…They constitute a strong extractor which implies that the seed can be reused without sacrificing too much randomness. In recent development of QRNGs [20,22,26,27,43], they have been used to construct hashing functions such as the Toeplitz-hashing matrix. These constructions require a long (but reusable) seed [44].…”

Section: Randomness Extractionmentioning

confidence: 99%

Maximization of Extractable Randomness in a Quantum Random-Number Generator

et al. 2015

View full text Add to dashboard Cite

The generation of random numbers via quantum processes is an efficient and reliable method to obtain true indeterministic random numbers that are of vital importance to cryptographic communication and large-scale computer modeling. However, in realistic scenarios, the raw output of a quantum random-number generator is inevitably tainted by classical technical noise. The integrity of the device can be compromised if this noise is tampered with, or even controlled by some malicious party. To safeguard against this, we propose and experimentally demonstrate an approach that produces side-information independent randomness that is quantified by min-entropy conditioned on this classical noise. We present a method for maximizing the conditional min-entropy of the number sequence generated from a given quantum-to-classical-noise ratio. The detected photocurrent in our experiment is shown to have a real-time random-number generation rate of 14 (Mbit/s)/MHz. The spectral response of the detection system shows the potential to deliver more than 70 Gbit/s of random numbers in our experimental setup.

show abstract

“…Since it is impossible to generate true random numbers by computer algorithms, most true random number generators are based on unpredictable physical process. Recently a variety of quantum random number generation (QRNG)schemes based on the intrinsic randomness of quantum theory have been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. All of these schemes work essentially according to the same principle, exploiting the randomness of quantum measurements.…”

Section: Introductionmentioning

confidence: 99%

More randomness from a prepare-and-measure scenario with independent devices

Han

Yin

et al. 2016

Phys. Rev. A

View full text Add to dashboard Cite

How to generate genuine quantum randomness from untrusted devices is an important problem in quantum information processing. Inspired by the previous work on self-testing quantum random number generator [Phys. Rev. Lett. 114, 150501], we present a new method to generate quantum randomness from a prepare-and-measure scenario with independent devices. In existing protocols, the quantum randomness only depends on a witness value (e.g., CHSH value ), which is calculated with the observed probabilities. Differently, here all the observed probabilities are directly used to calculate the min-entropy in our method. Through numerical simulation, we find that the minentropy of our proposed scheme is higher than the previous work, when a typical untrusted BB84 setup is used. Consequently, thanks to the proposed method, more genuine quantum random numbers may be obtained than before.

show abstract

Practical and fast quantum random number generation based on photon arrival time relative to external reference

Cited by 112 publications

References 12 publications

Random Number Generation with Cosmic Photons

Random Number Generation with Cosmic Photons

Maximization of Extractable Randomness in a Quantum Random-Number Generator

More randomness from a prepare-and-measure scenario with independent devices

Contact Info

Product

Resources

About