  Currently there are three major paradigms of quantum computation, the gate
model, annealing, and walks on graphs. The gate model and quantum walks on
graphs are universal computation models, while annealing plays within a
specific subset of scientific and numerical computations. Quantum walks on
graphs have, however, not received such widespread attention and thus the door
is wide open for new applications and algorithms to emerge. In this paper we
explore teaching a coined discrete time quantum walk on a regular graph a
probability distribution. We go through this exercise in two ways. First we
adjust the angles in the maximal torus $\mathbb{T}^d$ where $d$ is the
regularity of the graph. Second, we adjust the parameters of the basis of the
Lie algebra $\mathfrak{su}(d)$. We also discuss some hardware and software
concerns as well as immediate applications and the several connections to
machine learning.
