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

The device-independent approach to physics is one where conclusions are drawndirectly from the observed correlations between measurement outcomes. Inquantum information, this approach allows one to make strong statements aboutthe properties of the underlying systems or devices solely via the observationof Bell-inequality-violating correlations. However, since one can only performa {\em finite number} of experimental trials, statistical fluctuationsnecessarily accompany any estimation of these correlations. Consequently, animportant gap remains between the many theoretical tools developed for theasymptotic scenario and the experimentally obtained raw data. In particular, aphysical and concurrently practical way to estimate the underlying quantumdistribution has so far remained elusive. Here, we show that the naturalanalogs of the maximum-likelihood estimation technique and theleast-square-error estimation technique in the device-independent contextresult in point estimates of the true distribution that are physical, unique,computationally tractable and consistent. They thus serve as sound algorithmictools allowing one to bridge the aforementioned gap. As an application, wedemonstrate how such estimates of the underlying quantum distribution can beused to provide, in certain cases, trustworthy estimates of the amount ofentanglement present in the measured system. In stark contrast to existingapproaches to device-independent parameter estimations, our estimation does notrequire the prior knowledge of {\em any} Bell inequality tailored for thespecific property and the specific distribution of interest.

Device-independent point estimation from finite data and its application to device-independent property estimation