  In this paper, we propose a novel technique to accelerate Ising machines
hyperparameter tuning. Firstly, we define Ising machine performance and explain
the goal of hyperparameter tuning in regard to this performance definition.
Secondly, we compare well-known hyperparameter tuning techniques, namely random
sampling and Tree-structured Parzen Estimator (TPE) on different combinatorial
optimization problems. Thirdly, we propose a new convergence acceleration
method for TPE which we call "FastConvergence".It aims at limiting the number
of required TPE trials to reach best performing hyperparameter values
combination. We compare FastConvergence to previously mentioned well-known
hyperparameter tuning techniques to show its effectiveness. For experiments,
well-known Travel Salesman Problem (TSP) and Quadratic Assignment Problem (QAP)
instances are used as input. The Ising machine used is Fujitsu's third
generation Digital Annealer (DA). Results show, in most cases, FastConvergence
can reach similar results to TPE alone within less than half the number of
trials.
