Casual models for a quantum world
Quantum theory is a theory of information, imposing new -- and often counter-intuitive -- rules on how it can be acquired, processed and shared. To understand these rules, one can draw on the framework of causal Bayesian networks, which successfully addresses questions concerning knowledge, causation and inference in the context of classical statistics. The process of adapting classical causal models to accommodate quantum theory provides a new perspective on the fundamental differences between the two.
A central task in causal modeling is to characterize causal relations based solely on observed correlations. In the case of just two systems, we find that quantum coherence (eg entanglement) provides a distinct advantage for this problem, as it is known to do for other tasks such as cryptography and information processing. A linear optics experiment demonstrates this advantage in practice.
[Nat Phys 11, 414 (2015)]
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