用4量子比特电路提升鸢尾花分类准确率至100%。
Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification
- 量子电路通过角度编码与纠缠门处理特征关系。
- 训练20轮后测试集准确率达100%，16轮时已达成。
- 适合对量子机器学习感兴趣的开发者和研究者。
本文提出一种混合量子-经典机器学习模型，将4量子比特量子电路与经典神经网络结合用于分类任务。量子电路采用角度嵌入与纠缠门对鸢尾花数据集特征进行编码，以捕捉经典模型难以建模的复杂特征关系。该模型被称为量子卷积神经网络（QCNN），在20个训练周期内实现测试集100%准确率，且在16个周期时已达成此表现。结果表明，利用量子资源可有效增强监督学习任务的性能。文中详细阐述了量子电路设计、参数化门选择及量子层与经典组件的集成方式。本工作推动了混合量子-经典模型在真实数据集上的应用研究。
原文摘要 · Abstract (English)
This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterized gate selection, and the integration of the quantum layer with classical neural network components. This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.
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