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Distributed hybrid quantum-classical performance prediction for hyperparameter optimization / Wulff, Eric (CERN) ; Garcia Amboage, Juan Pablo (CERN) ; Aach, Marcel (Julich, Forschungszentrum ; Iceland U.) ; Gislason, Thorsteinn Eli (Iceland U.) ; Ingolfsson, Thorsteinn Kristinn (Iceland U.) ; Ingolfsson, Tomas Kristinn (Iceland U.) ; Pasetto, Edoardo (Julich, Forschungszentrum ; RWTH Aachen U.) ; Delilbasic, Amer (Julich, Forschungszentrum ; Iceland U.) ; Riedel, Morris (Julich, Forschungszentrum ; Iceland U.) ; Sarma, Rakesh (Julich, Forschungszentrum) et al.
Hyperparameter optimization (HPO) of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple graphics processing units (GPUs) on a classical supercomputer, while predicting the configurations’ performance with quantum-trained support vector regression (QT-SVR) on a quantum annealer (QA). [...]
2024 - 14 p. - Published in : Quantum Machine Intelligence 6 (2024) 59 Fulltext: PDF;

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