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Training and Hyperparameter Optimization (HPO) of deep learning-based AImodels are often compute resource intensive and calls for the use oflarge-scale distributed resources as well as scalable and resource efficienthyperparameter search algorithms. This work studies the potential of usingmodel performance prediction to aid the HPO process carried out on HighPerformance Computing systems. In addition, a quantum annealer is used to trainthe performance predictor and a method is proposed to overcome some of theproblems derived from the current limitations in quantum systems as well as toincrease the stability of solutions. This allows for achieving results on aquantum machine comparable to those obtained on a classical machine, showinghow quantum computers could be integrated within classical machine learningtuning pipelines.  Furthermore, results are presented from the development of a containerizedbenchmark based on an AI-model for collision event reconstruction that allowsus to compare and assess the suitability of different hardware accelerators fortraining deep neural networks.

Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC