这篇论文用仅18个参数的室温光量子模型在口腔癌检测上做到100%准确率，参数比经典模型少67%，特别适合边缘设备。
该研究针对资源受限环境中口腔癌早期检测需求，提出混合经典-连续变量（CV）量子分类器。管道结合MobileNetV1特征提取器、PCA降维至16维及含位移、干涉仪和Kerr门的CV-QNN。简化Φ∘D∘U1架构比标准Killoran层减少40-45%可训练参数，并通过降维与编码策略将损失梯度方差提升约58个数量级。四qumode简化CV-QNN仅用18个参数，验证AUC最高，以67%更少参数超越55参数经典基线，达到100%校准测试准确率。结论支持CV光子量子机器学习用于室温、参数高效的医学图像分类，推动边缘量子AI发展。
Parameter-Efficient Continuous-Variable Photonic Quantum Neural Networks for Edge Quantum AI: Demonstration in Oral Cancer Detection
Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings. Smartphone-based screening is a scalable alternative but needs lightweight models that run within edge-hardware constraints. Hybrid classical-quantum architectures are emerging candidates for parameter-efficient learning, yet most rely on qubit hardware that needs cryogenic operation, unsuitable for edge deployment. Continuous-variable (CV) photonic quantum computing, which operates at room temperature, offers a complementary route. We investigate a hybrid classical-CV quantum classifier for oral cancer detection from smartphone images. The pipeline combines a MobileNetV1 feature extractor, principal component analysis to 16 dimensions, and a parameterized CV-QNN of displacement, interferometric, and Kerr gates on a photonic backend. We propose a simplified $Φ\circ D \circ U_1$ CV-QNN architecture that cuts trainable parameters 40-45% relative to the standard CV-QNN layer of Killoran et al. (2019a), and identify dimensionality-reduction and encoding-restriction strategies that mitigate barren plateaus, raising loss-gradient variance by roughly 58 orders of magnitude. Whether the simplified layer beats the full layer is width-dependent: the full layer holds a small but significant edge at two qumodes, whereas the simplified layer is significantly better at four qumodes using 44% fewer parameters. The strongest model, a four-qumode simplified CV-QNN with only 18 parameters, attains the highest validation AUC of all models, exceeds a 55-parameter classical baseline using 67% fewer parameters, and reaches 100% calibrated test accuracy across all seeds. These results support CV photonic quantum machine learning for parameter-efficient, room-temperature medical image classification and motivate progress toward edge quantum AI.