TY  - JOUR
AU  - Wulff, Eric
AU  - Garcia Amboage, Juan Pablo
AU  - Aach, Marcel
AU  - Gislason, Thorsteinn Eli
AU  - Ingolfsson, Thorsteinn Kristinn
AU  - Ingolfsson, Tomas Kristinn
AU  - Pasetto, Edoardo
AU  - Delilbasic, Amer
AU  - Riedel, Morris
AU  - Sarma, Rakesh
AU  - Girone, Maria
AU  - Lintermann, Andreas
TI  - Distributed hybrid quantum-classical performance prediction for hyperparameter optimization
JO  - Quantum machine intelligence
VL  - 6
IS  - 2
SN  - 2524-4906
CY  - [Cham]
PB  - Springer Nature Switzerland AG
M1  - FZJ-2024-05716
SP  - 59
PY  - 2024
AB  - 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). The workflow is shown to run on up to 50 GPUs and a QA at the same time, completely automating the communication between the classical and the quantum systems. The approach is evaluated extensively on several benchmarking datasets from the computer vision (CV), high-energy physics (HEP), and natural language processing (NLP) domains. Empirical results show that resource costs for performing HPO can be reduced by up to 9% when using the hybrid workflow with performance prediction, compared to using a plain HPO algorithm without performance prediction. Additionally, the workflow obtains similar and in some cases even better accuracy of the final hyperparameter configuration, when combining multiple heuristically obtained predictions from the QA, compared to using just a single classically obtained prediction. The results highlight the potential of hybrid quantum-classical machine learning algorithms. The workflow code is made available open-source to foster adoption in the community.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001312144200001
DO  - DOI:10.1007/s42484-024-00198-5
UR  - https://juser.fz-juelich.de/record/1031521
ER  -