Lean classical-quantum hybrid neural network model for image classification
This work addresses computational efficiency for researchers and practitioners in quantum machine learning, though it is incremental as it builds on existing hybrid architectures.
The paper tackled the high computational cost of quantum machine learning for image classification by introducing a Lean Classical-Quantum Hybrid Neural Network (LCQHNN) that uses only four variational circuit layers, achieving accuracies of 100%, 99.02%, and 85.55% on MNIST, FashionMNIST, and CIFAR-10 datasets.
The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classification predominantly relies on traditional architectures such as variational quantum circuits. The performance of these models is closely tied to the scale of their parameters, with the substantial demand for parameters potentially leading to limitations in computational resources and a significant increase in computation time. In this paper, we introduce a Lean Classical-Quantum Hybrid Neural Network (LCQHNN), which achieves efficient classification performance with only four layers of variational circuits, thereby substantially reducing computational costs. Our experiments demonstrate that LCQHNN achieves 100\%, 99.02\%, and 85.55\% classification accuracy on MNIST, FashionMNIST, and CIFAR-10 datasets. Under the same parameter conditions, the convergence speed of this method is also faster than that of traditional models. Furthermore, through visualization studies, it is found that the model effectively captures key data features during training and establishes a clear association between these features and their corresponding categories. This study confirms that the employment of quantum algorithms enhances the model's ability to handle complex classification problems.