  In this paper we develop a new technique to model joint distributions of
signals. Our technique is based on quantum mechanical conjugate variables. We
show that the transition probability of quantum states leads to a distance
function on the signals. This distance function obeys the triangle inequality
on all quantum states and becomes a metric on pure quantum states. Treating
signals as conjugate variables allows us to create a new approach to segment
them.
  Keywords: Quantum information, transition probability, Euclidean distance,
Fubini-study metric, Bhattacharyya coefficients, conjugate variable,
signal/sensor fusion, signal and image segmentation.
