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How drug trials are simpler if your subjects are quantum (and other applications of quantum causal models)


A fundamental question in trying to understand the world -- be it classical or quantum -- is why things happen. We seek a causal account of events, and merely noting correlations between them does not provide a satisfactory answer. In classical statistics, a better alternative exists: the framework of causal models has proven useful for studying causal relations in a range of disciplines. We try to adapt this formalism to allow for quantum variables, and in the process discover a new perspective on how causality is different in the quantum world. One of the peculiarities that arise in this context can be harnessed to solve a task of causal inference -- inferring the causal relation between variables based on observed statistics -- that is impossible for classical variables. I will report on a recent experimental realization of this scheme.

Time permitting, I will also discuss a more realistic approach to the problem of characterizing quantum processes in the presence of initial correlations with an environment, viz non-Markovian dynamics. Another application of quantum causal inference arises in the context of quantum field theory: if one couples two detectors to a quantum field at different points throughout space-time, this may allow one of them to causally influence the other, via the field. We explore how different variables of the model, such as the acceleration of the detectors and the ultraviolet cutoff of the field theory, are reflected in the strength and quality of the causal influence.