  With the advent of exascale computing, effective load balancing in massively
parallel software applications is critically important for leveraging the full
potential of high performance computing systems. Load balancing is the
distribution of computational work between available processors. Here, we
investigate the application of quantum annealing to load balance two
paradigmatic algorithms in high performance computing. Namely, adaptive mesh
refinement and smoothed particle hydrodynamics are chosen as representative
grid and off-grid target applications. While the methodology for obtaining real
simulation data to partition is application specific, the proposed balancing
protocol itself remains completely general. In a grid based context, quantum
annealing is found to outperform classical methods such as the round robin
protocol but lacks a decisive advantage over more advanced methods such as
steepest descent or simulated annealing despite remaining competitive. The
primary obstacle to scalability is found to be limited coupling on current
quantum annealing hardware. However, for the more complex particle formulation,
approached as a multi-objective optimization, quantum annealing solutions are
demonstrably Pareto dominant to state of the art classical methods across both
objectives. This signals a noteworthy advancement in solution quality which can
have a large impact on effective CPU usage.
