Variational Quanvolutional Neural Networks with enhanced image encoding
This work addresses the optimization of quantum image encoding for hybrid quantum-classical neural networks, but it is incremental as it builds on existing quanvolutional neural networks without introducing a new paradigm.
The paper studied the impact of three quantum image encoding approaches and variational circuits on the performance of a hybrid quantum-classical image classification algorithm, finding that the best encoding depends on application-specific constraints rather than a universal solution.
Image classification is an important task in various machine learning applications. In recent years, a number of classification methods based on quantum machine learning and different quantum image encoding techniques have been proposed. In this paper, we study the effect of three different quantum image encoding approaches on the performance of a convolution-inspired hybrid quantum-classical image classification algorithm called quanvolutional neural network (QNN). We furthermore examine the effect of variational - i.e. trainable - quantum circuits on the classification results. Our experiments indicate that some image encodings are better suited for variational circuits. However, our experiments show as well that there is not one best image encoding, but that the choice of the encoding depends on the specific constraints of the application.