2015
DOI: 10.1103/physreva.91.012314
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Strong experimental guarantees in ultrafast quantum random number generation

Abstract: 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 ex… Show more

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Cited by 47 publications
(42 citation statements)
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References 41 publications
(105 reference statements)
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“…Here we develop statistical metrology for a short-delay RNG, in analogy to earlier work with high-throughput RNGs [19,21,28]. The excess predictability is exponentially reduced by randomness extraction (RE) [29]: in real time, we compute the parity of several raw bits to produce one very unpredictable extracted bit for the setting choice.…”
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confidence: 99%
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“…Here we develop statistical metrology for a short-delay RNG, in analogy to earlier work with high-throughput RNGs [19,21,28]. The excess predictability is exponentially reduced by randomness extraction (RE) [29]: in real time, we compute the parity of several raw bits to produce one very unpredictable extracted bit for the setting choice.…”
mentioning
confidence: 99%
“…Here we combine ultrafast RNG by accelerated laser phase diffusion [18][19][20] with real-time randomness extraction and metrological randomness assurances [21] to produce a RNGs suitable for loophole-free Bell tests. Because the laser phase diffusion is driven by effects, including spontaneous emission, that are unpredictable both in quantum theory and in an important class of stochastic hidden variable theories, the source can be used to address the "freedom-of-choice" loophole [22].…”
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confidence: 99%
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“…To preserve the statistical conclusions, the used values of A , B , and p 11 should be conservative overestimates, while p 12 , p 21 , p 22 should be conservative underestimates. Estimates of this kind, including p-values for A , B have recently been reported for phase-diffusion random number generators [52,58]. The p-value for A , B can be taken into account by including the failure probability q f into the process counting procedure, explained in the previous paragraph.…”
Section: Statistical Significance and Run Timementioning
confidence: 99%
“…For random number generators with small bias [52,58], it might be more efficient to quantify with A , B the excess predictability beyond probability 1 2 , despite the presence of the bias. Expression (16) then becomes 1 2 (1 − A ) ≤ p(a|µ) ≤ 1 2 (1 + A ), and similar for Bob.…”
Section: Statistical Significance and Run Timementioning
confidence: 99%