On Optimizing Hyperparameters for Quantum Neural Networks
Sabrina Herbst Vincenzo De Maio Ivona Brandic
The increasing capabilities of Machine Learning (ML) models go hand in hand
with an immense amount of data and computational power required for training.
Therefore, training is usually outsourced into HPC facilities, where we have
started to experience limits in scaling conventional HPC hardware, as theorized
by Moore's law. Despite heavy parallelization and optimization efforts, current
state-of-the-art ML models require weeks for training, which is associated with
an enormous $CO_2$ footprint. Quantum Computing, and specifically Quantum
Machine Learning (QML), can offer significant theoretical speed-ups and
enhanced expressive power. However, training QML models requires tuning various
hyperparameters, which is a nontrivial task and suboptimal choices can highly
affect the trainability and performance of the models. In this study, we
identify the most impactful hyperparameters and collect data about the
performance of QML models. We compare different configurations and provide
researchers with performance data and concrete suggestions for hyperparameter
selection.