用量子模型实现高精度梵文数字手写识别，突破传统方法瓶颈。
Devanagari Digit Recognition using Quantum Machine Learning
- 结合CNN与10量子比特变分量子电路，实现空间特征提取与量子分类。
- 在DHCD数据集上达99.80%准确率，测试损失仅0.2893，性能超越经典模型。
- 参数更少、抗干扰更强，适合低资源语言场景的量子机器学习应用。
区域性文字如梵文的手写数字识别对多语言文档数字化、教育工具及文化遗产保护至关重要。该文提出首个用于梵文手写数字识别的混合量子-经典架构，融合卷积神经网络（CNN）进行空间特征提取与10量子比特变分量子电路（VQC）实现量子增强分类。在梵文手写字符数据集（DHCD）上训练与评估，所提模型实现99.80%的测试准确率、0.2893的测试损失，且平均每类F1分数达0.9980，为量子实现中的最新成果。相比等效经典CNN，该模型以更少参数获得更高精度并具备更强鲁棒性。通过利用量子叠加与纠缠原理，本工作为区域文字识别建立新基准，凸显量子机器学习（QML）在真实世界低资源语言场景中的潜力。
原文摘要 · Abstract (English)
Handwritten digit recognition in regional scripts, such as Devanagari, is crucial for multilingual document digitization, educational tools, and the preservation of cultural heritage. The script's complex structure and limited annotated datasets pose significant challenges to conventional models. This paper introduces the first hybrid quantum-classical architecture for Devanagari handwritten digit recognition, combining a convolutional neural network (CNN) for spatial feature extraction with a 10-qubit variational quantum circuit (VQC) for quantum-enhanced classification. Trained and evaluated on the Devanagari Handwritten Character Dataset (DHCD), the proposed model achieves a state-of-the-art test accuracy for quantum implementation of 99.80% and a test loss of 0.2893, with an average per-class F1-score of 0.9980. Compared to equivalent classical CNNs, our model demonstrates superior accuracy with significantly fewer parameters and enhanced robustness. By leveraging quantum principles such as superposition and entanglement, this work establishes a novel benchmark for regional script recognition, highlighting the promise of quantum machine learning (QML) in real-world, low-resource language settings.
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