Perimeter Institute for Theoretical Physics
Talks by Elie Wolfe
Robert Spekkens Perimeter Institute for Theoretical Physics
What do data science and the foundations of quantum theory have to do with one another?
A great deal, it turns out. The particular branch of data science known as causal inference focuses on a problem which is central to disciplines ranging from epidemiology to economics: that of disentangling correlation and causation in statistical data.
An Unconventional Classification of Multipartiteness + Inflation Techniques for Causal Inference for Quantum Networks
What does it mean for quantum state to be genuinely fully multipartite? Some would say, whenever the state cannot be decomposed as a mixture of states each of which has no entanglement across some partition. I'll argue that this partition-centric thinking is ill-suited for the task of assessing the connectivity of the network required to realize the state.
The ideas of no-signalling, nonlocality, Bell inequalities, and quantum correlations can all be understood as implications of a presumed causal structure. In particular, the causal structure of the Bell scenario implies the Bell inequalities whenever the shared resource is presumed to act like a classical hidden random variable. If the shared resource in the scenario is a quantum system, however, then the quantum causal structure can give rise to a larger set of correlations, including probability distributions which violate Bell inequalities up to Tsirelson's bound.