Quantum search in stochastic planning

There has been recent interest in implementing automated planning by optimizing a planning domain modeled as a stochastic system. Planning is viewed as a process where sequential decision problems are solved in order to reach the goal, and thus, can be considered as instances of a Markov Decision Process (MDP). However, standard MDP techniques cannot solve a typical planning problem in polynomial time. Hence, the motivation for investigating the use of quantum search techniques based on the Grover Search Algorithm, to identify policies with high utility.