使用混合量子机器学习的人脸分类
Facial classification Using Hybrid Quantum Machine Learning
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中文总结 AI 辅助
本文提出一种混合量子-经典人脸识别流水线，在标准CPU硬件上实现优于FaceNet基线的准确率和训练效率，并验证了实际部署的可行性。
中文摘要 AI 辅助
混合量子方法在资源受限的人脸生物识别中研究较少。我们提出了一种混合量子-经典人脸识别流水线，旨在标准计算硬件上运行。图像经过伽马校正、对比度增强和主成分分析后，其特征被编码到一个八量子比特变分量子分类器中。随后，经典图像匹配执行识别。在包含50,000张图像的实验中，其中包括来自CelebA的25,000张人脸图像和来自CIFAR-10的25,000张非人脸图像，该方法在准确性和训练效率上均优于所报告的CPU训练的FaceNet基线。该流水线还在GPU和量子硬件上进行了评估。在与Lloyds技术中心合作的海德拉巴马欣德拉大学的考勤监控部署中，CPU推理每人耗时0.2至0.5秒，并且系统对佩戴眼镜的情况保持鲁棒性。这些发现支持在现有CPU硬件上部署混合量子方法进行人脸识别的可行性。
英文摘要
Hybrid quantum methods have received limited study for resource-constrained facial biometrics. We present a hybrid quantum-classical facial recognition pipeline designed to run on standard computing hardware. Images undergo gamma correction, contrast enhancement, and principal component analysis before their features are encoded into an eight-qubit variational quantum classifier. Classical image matching then performs recognition. In experiments with 50,000 images, comprising 25,000 faces from CelebA and 25,000 non-face images from CIFAR-10, the method outperformed the reported CPU-trained FaceNet baseline in accuracy and training efficiency. The pipeline was also evaluated on GPU and quantum hardware. In an attendance monitoring deployment at Mahindra University in collaboration with Lloyds Technology Centre, CPU inference took 0.2 to 0.5 seconds per person, and the system remained robust to the use of spectacles. These findings support the feasibility of deploying hybrid quantum methods for facial recognition on existing CPU hardware.
