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 our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.

翻译：图像识别是机器学习算法的主要应用之一。然而，现代图像识别系统中使用的机器学习模型包含数百万个参数，通常需要大量计算时间进行调整。此外，模型超参数的调整会带来额外开销。因此，亟需开发新型机器学习模型及超参数优化技术。本文提出了一种受量子启发的超参数优化方法及用于监督学习的混合量子-经典机器学习模型。我们在标准黑箱目标函数上对超参数优化方法进行基准测试，观察到随着搜索空间规模增长，该方法在降低预期运行时间和适应度方面表现出性能提升。我们将上述方法应用于车辆图像分类任务，并展示了基于张量训练超参数优化的混合量子ResNet模型的全规模实现。实验表明，与使用深度神经网络ResNet34的标准经典表格网格搜索方法相比，本方法具有定性和定量优势。混合模型经过18次迭代后达到0.97的分类准确率，而经典模型在75次迭代后仅达到0.92的准确率。