用三元组损失优化量子编码，提升图像分类的标签可分性。
Triplet Loss Based Quantum Encoding for Class Separability
- 基于三元组损失训练量子编码电路，使同类样本在希尔伯特空间中聚类更紧密。
- 在MNIST和MedMNIST上比幅值编码准确率显著提升，且电路深度更低。
- 适合需要低资源量子分类器的研究者，尤其适用于高维图像数据。
提出一种高效且数据驱动的量子编码方案，以增强变分量子分类器性能。该编码专为复杂数据集（如图像）设计，旨在通过生成根据标签形成良好分离簇的输入态来辅助分类任务。编码电路采用受经典人脸识别算法启发的三元组损失函数进行训练，类别可分性通过编码密度矩阵间的平均迹距离衡量。在多个二分类任务上对MNIST和MedMNIST数据集的基准测试表明，该方法相较于相同变分量子电路结构的幅值编码有显著性能提升，同时所需电路深度大幅降低。
原文摘要 · Abstract (English)
An efficient and data-driven encoding scheme is proposed to enhance the performance of variational quantum classifiers. This encoding is specially designed for complex datasets like images and seeks to help the classification task by producing input states that form well-separated clusters in the Hilbert space according to their classification labels. The encoding circuit is trained using a triplet loss function inspired by classical facial recognition algorithms, and class separability is measured via average trace distances between the encoded density matrices. Benchmark tests performed on various binary classification tasks on MNIST and MedMNIST datasets demonstrate considerable improvement over amplitude encoding with the same VQC structure while requiring a much lower circuit depth.
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