Hyperparameter optimization of hybrid quantum neural networks for car classification. (arXiv:2205.04878v1 [quant-ph])

Image recognition is one of the primary applications of machine learning
algorithms. Nevertheless, machine learning models used in modern image
recognition systems consist of millions of parameters that usually require
significant computational time to be adjusted. Moreover, adjustment of model
hyperparameters leads to additional overhead. Because of this, new developments
in machine learning models and hyperparameter optimization techniques are
required. This paper presents a quantum-inspired hyperparameter optimization
technique and a hybrid quantum-classical machine learning model for supervised
learning. We benchmark …

arxiv classification hybrid networks neural networks optimization quantum quantum neural networks