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.

翻译：机器学习模型的不断增强能力与其训练所需的海量数据和计算资源密不可分。因此，训练通常外包给高性能计算设施，而根据摩尔定律的预测，我们在传统HPC硬件的扩展中已开始遇到瓶颈。尽管进行了大量的并行化和优化努力，当前最先进的机器学习模型仍需要数周时间进行训练，这伴随着巨大的二氧化碳排放。量子计算，特别是量子机器学习，能够提供理论上的显著加速和增强的表达能力。然而，训练QML模型需要调整各种超参数，这是一项非平凡的任务，且次优的选择会严重影响模型的可训练性和性能。在本研究中，我们识别出最具影响力的超参数，并收集关于QML模型性能的数据。我们比较了不同的配置，为研究人员提供性能数据以及超参数选择的具体建议。