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UID:2459@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180914T140000
DTEND;TZID=Europe/Paris:20180914T150000
DTSTAMP:20180830T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/causal-learning-in-a-quantum-wo
 rld/
SUMMARY: (...): Causal learning in a quantum world
DESCRIPTION:: Causal reasoning is essential to how we understand and model 
 the world. Quite recently\, a general theory of causal modelling has been 
 developed\, with the ambitious goal to provide a unified framework for cau
 sal discovery across all disciplines. The practical applications of this f
 ramework are still unclear\, as building algorithms that learn causal stru
 ctures\, as opposed to sheer correlations\, remains challenging. However\,
  where the framework fails most dramatically is quantum physics\, where ca
 usal models are unable to reproduce simple scenarios. What is worse\, the 
 ordinary quantum formalism does not support causal inference: one cannot m
 eaningfully ask\, given two events\, which is the cause and which the effe
 ct. This is particularly troubling\, as future quantum technologies might 
 require systematic methods to discover cause-effect relations in large net
 works. I will present a novel formalism that makes quantum inference a wel
 l-defined task\, with possible applications to the analysis of complex pro
 cesses. I will discuss how machine-learning techniques can help solving th
 is task in the absence of complete information.http://smp.uq.edu.au/profil
 e/167/fabio-costa
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DTSTART:20180325T030000
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