Optimizing annealing parameters using genetic algorithms

Quantum annealing is an emerging technology that has the potential to be useful for solving combinatorial optimization problems such as maximum satisfiability. Prior work showed that annealing times [1] and inclusion of pauses [2] can significantly impact the probability of obtaining exact solutions. Less is known about leveraging other parameters such as flux biases implemented on the most recent D-Wave 2000Q annealer. We seek to bridge this gap by using genetic algorithms to select parameters. We explore various statistical measures, such as stochastic for ranking settings. Evaluations are performed using the Ames Research Center annealer.

[1] E. Crosson, arXiv:1401.7320
[2] J. Marshall, PhysRevApplied.11.044083