Quantum walks have been shown to quadratically speed up search problems on graphs. We discuss their usage towards mixing and quantum sampling on graphs, problems that are in a sense dual to the search problem. Quantum sampling, or quantum state generation, roughly amounts to creating a superposition over the elements of a set, where this set may be implicitly defined. It is known that if this task can be solved efficiently, then the complexity class called “statistical zero knowledge” is in BQP. We discuss a folklore approach to this problem called “inverted search”, where a quantum search algorithm is essentially inverted to yield a quantum sampling algorithm. We also show how this approach solves the search problem when starting from a single element of the graph, rather than a superposition. The easier task of mixing on graphs asks for a classical sample of the set, rather than a superposition. It has long been conjectured that quantum walks can quadratically speed up this task when compared to simple random walks. We show how a quantum sampling approach confirms this conjecture for a limited set of graphs. For more general graphs however, it is conceivable that the quantum sampling problem cannot be solved efficiently, such that a different approach towards mixing is needed. We discuss ideas in this direction.