Quantum Neural Network Image Three-Classification Model Based on the Iris Dataset
Minglin Zhang, Xiao Chen, Zhihao Liu · 2024
Quantum neural networks, which combine quantum computing and machine learning, have become a topic of great interest due to the advancement of quantum computing. This paper introduces a pure quantum neural network model based on parameterized quantum circuits, applied to image classification using the Iris dataset. Experimental validation demonstrates superior classification accuracy in three-class tasks. Specifically, this paper innovatively uses IQP encoding to encode classical data into quantum states, and independently designs the Ansatz in the quantum neural network. Experimental results show that compared with the other three classical classification algorithms and four quantum neural network models, the pure quantum neural network model proposed in the study has the highest accuracy in image classification tasks. In addition, this article analyzes and evaluates the model’s expression characteristics and entanglement capabilities, providing a reference for further optimizing and expanding the quantum neural network model. Overall, this research provides new ideas and methods for the integration of quantum computing and machine learning, which has certain practical value and research significance.