  Graph partitioning has many applications in powersystems from decentralized
state estimation to parallel simulation. Focusing on parallel simulation,
optimal grid partitioning minimizes the idle time caused by different
simulation times for the sub-networks and their components and reduces the
overhead required to simulate the cuts. Partitioning a graph into two parts
such that, for example, the cut is minimal and the subgraphs have equal size is
an NP-hard problem. In this paper we show how optimal partitioning of a graph
can be obtained using quantum annealing (QA). We show how to map the
requirements for optimal splitting to a quadratic unconstrained binary
optimization (QUBO) formulation and test the proposed formulation using a
current D-Wave QPU. We show that the necessity to find an embedding of the QUBO
on current D-Wave QPUs limits the problem size to under 200 buses and notably
affects the time-to-solution. We finally discuss the implications on near-term
implementation of QA in combination to traditional CPU or GPU based simulation.
