Causal Inference and Quantum Networks: How Hypothesis Testing from Statistical Data is relevant to Quantum Engineering
Causal inference is a (classical) technique used to figure out what sorts of causal relationships between variables constitute a viable explanations for some observed statistics. Equivalently, causal inference allows us to quantify the sorts of correlations between variables which are possible given a particular causal structure. The famous inability to explain quantum correlations in terms of local hidden variable models can be understood as a fact about causal inference, namely: whereas in classical causal structures all unobserved variables have the status of shared randomness, in quantum causal structures an observed system can represent quantum entanglement. Accordingly, Bell's theorem can be recast in causal terminology: The set of correlations compatible with a (particular) quantum causal structure strictly contains the set of correlations compatible with its analogous classical causal structure. I'll set out a series of research targets related to generalizing this quantum-advantage to network-like causal structures; focusing mostly on the simplest networks, consisting of three parties sharing pairwise entanglement. Based mostly on arXiv:1609.00672.
Tags: Quantum Foundations, Quantum Internet, Causal Inference, Causal Structure, Triangle Scenario, Inflation Technique
תאריך עדכון אחרון : 21/08/2018