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Information-theoretic measures such as relative entropy and correlation areextremely useful when modeling or analyzing the interaction of probabilisticsystems. We survey the quantum generalization of 5 such measures and point outsome of their commonalities and interpretations. In particular we find theapplication of information theory to distributional semantics useful. Bymodeling the distributional meaning of words as density operators rather thanvectors, more of their semantic structure may be exploited. Furthermore,properties of and interactions between words such as ambiguity, similarity andentailment can be simulated more richly and intuitively when using methods fromquantum information theory.

On Quantum Generalizations of Information-Theoretic Measures and their Contribution to Distributional Semantics