English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We develop a framework for certifying randomness from Bell-test trials basedon directly estimating the probability of the measurement outcomes withadaptive test supermartingales. The number of trials need not be predetermined,and one can stop performing trials early, as soon as the desired amount ofrandomness is extractable. It can be used with arbitrary, partially known andtime-dependent probabilities for the random settings choices. Furthermore, itis suitable for application to experimental configurations with low Bellviolation per trial, such as current optical loophole-free Bell tests. It ispossible to adapt to time-varying experimental parameters. We formulate theframework for the general situation where the trial probability distributionsare constrained to a known set. Randomness expansion with logarithmic settingsentropy is possible for many relevant configurations. We implement probabilityestimation numerically and apply it to a representative settings-conditionalprobability distribution of the outcomes from an atomic loophole-free Bell test[Rosenfeld et al., Phys. Rev. Lett. 119:010402 (2017), arXiv:1611.04604 (2016)]to illustrate trade-offs between the amount of randomness, error, settingsentropy, unknown settings biases, and number of trials. We then show thatprobability estimation yields more randomness from the loophole-free Bell-testdata analyzed in [Bierhorst et al., arXiv:1702.05178 (2017)] and toleratesadversarial settings probability biases.

Quantum Randomness Generation by Probability Estimation with Classical Side Information