  Information-theoretic measures such as relative entropy and correlation are
extremely useful when modeling or analyzing the interaction of probabilistic
systems. We survey the quantum generalization of 5 such measures and point out
some of their commonalities and interpretations. In particular we find the
application of information theory to distributional semantics useful. By
modeling the distributional meaning of words as density operators rather than
vectors, more of their semantic structure may be exploited. Furthermore,
properties of and interactions between words such as ambiguity, similarity and
entailment can be simulated more richly and intuitively when using methods from
quantum information theory.
