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Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be acompute resource intensive process as it usually requires to train the targetmodel with many different hyperparameter configurations. We show thatintegrating model performance prediction with early stopping methods holdsgreat potential to speed up the HPO process of deep learning models. Moreover,we propose a novel algorithm called Swift-Hyperband that can use eitherclassical or quantum support vector regression for performance prediction andbenefit from distributed High Performance Computing environments. Thisalgorithm is tested not only for the Machine-Learned Particle Flow model usedin High Energy Physics, but also for a wider range of target models fromdomains such as computer vision and natural language processing.Swift-Hyperband is shown to find comparable (or better) hyperparameters as wellas using less computational resources in all test cases.

Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing