4.6 Article

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

Journal

PHYSICAL REVIEW A
Volume 97, Issue 3, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.97.032309

Keywords

-

Funding

  1. Ministry of Science and Technology, Taiwan [104-2112-M-006-021-MY3]
  2. National Center for Theoretical Sciences of Taiwan
  3. Swiss National Science Foundations through NCCR QSIT
  4. Ontario Research Fund
  5. Natural Sciences and Engineering Research Council of Canada
  6. [PP00P2-150579]
  7. [P2GEP2_162060]

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The device-independent approach to physics is one where conclusions are drawn directly from the observed correlations between measurement outcomes. In quantum information, this approach allows one to make strong statements about the properties of the underlying systems or devices solely via the observation of Bell-inequality-violating correlations. However, since one can only perform a finite number of experimental trials, statistical fluctuations necessarily accompany any estimation of these correlations. Consequently, an important gap remains between the many theoretical tools developed for the asymptotic scenario and the experimentally obtained raw data. In particular, a physical and concurrently practical way to estimate the underlying quantum distribution has so far remained elusive. Here, we show that the natural analogs of the maximum-likelihood estimation technique and the least-square-error estimation technique in the device-independent context result in point estimates of the true distribution that are physical, unique, computationally tractable, and consistent. They thus serve as sound algorithmic tools allowing one to bridge the aforementioned gap. As an application, we demonstrate how such estimates of the underlying quantum distribution can be used to provide, in certain cases, trustworthy estimates of the amount of entanglement present in the measured system. In stark contrast to existing approaches to device-independent parameter estimations, our estimation does not require the prior knowledge of any Bell inequality tailored for the specific property and the specific distribution of interest.

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