Hybrid quantum ResNet for car classification and its hyperparameter optimization
This work addresses computational efficiency in image recognition for applications like car classification, but it is incremental as it builds on existing quantum and classical methods.
The paper tackled the problem of high computational overhead in image recognition by proposing a hybrid quantum-classical ResNet model and a quantum-inspired hyperparameter optimization technique, achieving a classification accuracy of 0.97 in 18 iterations compared to 0.92 in 75 iterations for a classical model.
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.