用量子启发方法优化卷积网络，降低延迟同时保持精度。
QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN Models
- 融合量子启发的剪枝、张量分解与退火矩阵分解重设计网络结构
- 在GoogLeNet、DenseNet和ResNet-18上实现推理时间显著降低
- 适合追求低延迟高精度部署的视觉模型开发者
卷积神经网络（CNN）在计算机视觉任务中取得显著进展，但其高推理时间和延迟常限制实际应用。尽管模型压缩技术日益流行，却常忽视低延迟与精度不妥协之间的平衡。本文通过引入量子启发的剪枝、张量分解与基于退火的矩阵分解——三种量子启发概念，提出QIANets：一种对传统GoogLeNet、DenseNet和ResNet-18架构进行重构的新方法，可在处理更多参数和计算量的同时维持低推理时间。尽管存在实验限制，该方法经过测试评估，展现出推理时间的降低及精度的有效保持。
原文摘要 · Abstract (English)
Convolutional neural networks (CNNs) have made significant advances in computer vision tasks, yet their high inference times and latency often limit real-world applicability. While model compression techniques have gained popularity as solutions, they often overlook the critical balance between low latency and uncompromised accuracy. By harnessing quantum-inspired pruning, tensor decomposition, and annealing-based matrix factorization - three quantum-inspired concepts - we introduce QIANets: a novel approach of redesigning the traditional GoogLeNet, DenseNet, and ResNet-18 model architectures to process more parameters and computations whilst maintaining low inference times. Despite experimental limitations, the method was tested and evaluated, demonstrating reductions in inference times, along with effective accuracy preservations.
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