使用量子迁移学习的物体检测
Object Detection Using Quantum Transfer Learning
- Shahrood University of Technology（沙赫鲁德理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种物理信息启发的混合经典-量子物体检测架构，利用预训练MobileNetV2和八量子比特变分电路，通过拓扑控制信息流和限制纠缠实现高效检测，无需最大纠缠。
AI中文摘要：
物体检测需要联合学习语义身份和连续空间几何。现有的量子迁移学习方法主要集中在分类任务上，不支持回归。在此，我们引入一种物理信息启发的混合经典-量子架构用于物体检测，其中预训练的MobileNetV2将视觉特征映射到八量子比特的变分量子希尔伯特空间。该希尔伯特空间被划分为两个任务特定的子空间，分别用于语义分类和几何定位。局部量子期望值用于预测连续的边界框坐标，而电路拓扑则调节两个任务之间的量子信息流。我们比较了线性和环形CNOT拓扑，并表明周期性边界条件抑制了二分纠缠而不降低检测性能。环形架构达到0.1688比特的二分冯·诺依曼熵和0.0370的Meyer-Wallach纠缠度量，同时实现了0.9448的mAP@0.5和0.6558的mAP@0.5:0.95。热力学公式进一步将总学习损失解释为亥姆霍兹自由能的变化。它将几何定位解释为有效的内能贡献，将语义分类解释为熵贡献。这些结果表明，所提出的物体检测模型的高性能并不需要最大纠缠。相反，高效学习可以通过拓扑控制的信息流、针对性的纠缠限制以及希尔伯特空间内的信息重整化来实现。
英文摘要：
Object detection requires the joint learning of semantic identity and continuous spatial geometry. Existing quantum transfer learning approaches have focused mainly on classification tasks and do not support regression. Here, we introduce a physics informed hybrid classical-quantum architecture for object detection in which a pre-trained MobileNetV2 maps visual features into an eight qubits variationally quantum Hilbert space. The Hilbert space is partitioned into two task specific subspaces for semantic classification and geometric localization. Local quantum expectation values are used to predict continuous bounding box coordinates, while circuit topology regulates the flow of quantum information between the two tasks. We compare linear and circular CNOT topologies and show that periodic boundary conditions suppress bipartite entanglement without degrading detection performance. The circular architecture reaches a bipartite von Neumann entropy of 0.1688 bits and a Meyer-Wallach entanglement measure of 0.0370, while achieving an mAP@0.5 of 0.9448 and an mAP@0.5:0.95 of 0.6558. A thermodynamic formulation further interprets the total learning loss as a variation in Helmholtz free energy. It interprets geometric localization as an effective internal energy contribution and semantic classification as an entropic contribution. These results indicate that high performance in the proposed object detection model does not require maximal entanglement. Instead, efficient learning can emerge through topology controlled information flow, targeted restriction of entanglement, and information renormalization within the Hilbert space.
