用量子纠缠测相似性，提升偏振材料识别精度
Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification
- 将偏振材料转为点匹配问题，用量子态编码特征向量
- 在23类材料上达94.7%准确率，支持未知类别检测
- 适合做边缘端的轻量化材料识别，尤其适合量子硬件部署
我们提出一种量子-经典混合框架用于偏振材料分类，将问题转化为点匹配任务。使用包含偏振光反射信息的体素立方体训练编码器，生成32维嵌入表示。推理时舍弃编码器头，将嵌入作为量子态的概率幅输入SWAP测试电路，计算查询立方体与一组锚点立方体之间的保真度。聚合保真度作为材料相似性得分，取最高分对应类作为预测类别。在由穆勒矩阵构建的23类材料数据集（每类约800样本）上评估，结果表明该方法在分类准确率上具有竞争力，并具备开集识别能力，为基于近中期量子设备的材料识别提供了可行路径。
原文摘要 · Abstract (English)
We present a quantum--classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem. Voxel cubes, containing polarized light reflections, are used to train an encoder to produce 32-dimensional embeddings for the voxels of the cubes. At inference, the encoder head is discarded and the embeddings are encoded as probability amplitudes of quantum states. Next, a SWAP-test circuit estimates the fidelity between each of the 32D embeddings from the query cube and a dataset of anchor cubes. The aggregated fidelity serves as materials similarity scores, and the class of the anchor with highest aggregated fidelity is deemed as the class of the queried material. We evaluate our approach on a dataset of 23 materials ($\approx$800 samples each) derived from their Mueller matrices. The point-matching approaches from the proposed quantum SWAP-test and a classical classifier using Optimal Transport are compared. Our results demonstrate the competitive classification accuracy alongside open-set discrimination potential, establishing it as a viable path toward NISQ-based material recognition.
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