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

Pei Sheng Lin, Denis Rosset, Yanbao Zhang, Jean Daniel Bancal, Yeong Cherng Liang

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Article number032309
JournalPhysical Review A
Issue number3
Publication statusPublished - 2018 Mar 12

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

Fingerprint Dive into the research topics of 'Device-independent point estimation from finite data and its application to device-independent property estimation'. Together they form a unique fingerprint.

Cite this