The failure of machine learning models to accurately capture causal relationships is becoming increasingly well understood, leading to an explosion of casually motivated machine learning techniques. In this talk we consider the quantum case: can we learn causal structure in a quantum world? We introduce a quantum causal machine learning framework and also consider the possibility of a quantum enhanced agent that learns via explicitly quantum interventions. The latter model of a quantum agent uses optical probes to learn about the external world in a manner that improves upon the classical analogue.
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