Temporal Quantum Correlations and Hidden Variable Models

Author(s):  
Costantino Budroni
2012 ◽  
Vol 86 (3) ◽  
Author(s):  
Wiesław Laskowski ◽  
Marcin Markiewicz ◽  
Tomasz Paterek ◽  
Marcin Wieśniak

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 185 ◽  
Author(s):  
Yeong-Cherng Liang ◽  
Yanbao Zhang

The device-independent approach to physics is one where conclusions about physical systems (and hence of Nature) are drawn directly and solely from the observed correlations between measurement outcomes. This operational approach to physics arose as a byproduct of Bell’s seminal work to distinguish, via a Bell test, quantum correlations from the set of correlations allowed by local-hidden-variable theories. In practice, since one can only perform a finite number of experimental trials, deciding whether an empirical observation is compatible with some class of physical theories will have to be carried out via the task of hypothesis testing. In this paper, we show that the prediction-based-ratio method—initially developed for performing a hypothesis test of local-hidden-variable theories—can equally well be applied to test many other classes of physical theories, such as those constrained only by the nonsignaling principle, and those that are constrained to produce any of the outer approximation to the quantum set of correlations due to Navascués-Pironio-Acín. We numerically simulate Bell tests using hypothetical nonlocal sources of correlations to illustrate the applicability of the method in both the independent and identically distributed (i.i.d.) scenario and the non-i.i.d. scenario. As a further application, we demonstrate how this method allows us to unveil an apparent violation of the nonsignaling conditions in certain experimental data collected in a Bell test. This, in turn, highlights the importance of the randomization of measurement settings, as well as a consistency check of the nonsignaling conditions in a Bell test.


2020 ◽  
Author(s):  
Jake M. Ferguson ◽  
Andrea González-González ◽  
Johnathan A. Kaiser ◽  
Sara M. Winzer ◽  
Justin M. Anast ◽  
...  

AbstractThe impacts of disease on host vital rates can be clearly demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual effects of disease, caused by infection, from population-level measurements of survival and fecundity when longitudinal studies are not possible. Our approach seeks to explain temporal changes in population-level vital rates by coupling observed changes in the infection status of individuals to an epidemiological model. We tested the approach using both single and coinfection viral challenge experiments on populations of fruit flies (Drosophila melanogaster). Specifically, we determined whether our approach yielded reliable estimates of disease prevalence and of the effects of disease on survival and fecundity rates for treatments of single infections and coinfection. We found two conditions are necessary for reliable estimation. First, diseases must drive detectable changes in vital rates, and second, there must be substantial variation in the degree of prevalence over time. This approach could prove useful for detecting epidemics from public health data in regions where standard surveillance techniques are not available, and in the study of epidemics in wildlife populations, where longitudinal studies can be especially difficult to implement.


2016 ◽  
Vol 117 (19) ◽  
Author(s):  
Flavien Hirsch ◽  
Marco Túlio Quintino ◽  
Tamás Vértesi ◽  
Matthew F. Pusey ◽  
Nicolas Brunner

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