用量子几何方法量化神经算子相似性，实现跨模态跨硬件的高效剪枝。
Quantum-Inspired Spectral Geometry for Neural Operator Equivalence and Structured Pruning
- 将算子映射到布洛赫超球上的奇异值谱，构建量子启发的几何表征。
- 证明谱距离与函数距离严格等价，首次建立跨架构算子替换的理论基础。
- 提出一阶段结构化剪枝方法，实测优于传统方法，适合国产异构硬件部署。
资源受限且异构的国内硬件上多模态智能的快速发展暴露出关键瓶颈：多模态特征异质性、动态场景下的实时需求以及硬件特定的算子冗余。本文提出一种量子启发的神经算子几何框架，将每个算子表示为在布洛赫超球上的归一化奇异值谱。我们证明了一个紧致的谱-函数等价定理，表明弗比尼-施图迪/沃瑟斯坦-2距离趋近于零时，函数逼近性可被严格保证，首次建立了跨模态与跨架构算子可替换性的理论基础。基于此度量，我们提出量子度量驱动的功能冗余图（QM-FRG）和一阶段结构化剪枝方法。受控仿真验证了该度量在性能上优于幅度和随机基线。大规模多模态变换器及国产异构硬件（华为昇腾、寒武纪MLU、昆仑芯）上的广泛实验验证，将在正在撰写中的扩展期刊版本中呈现。
原文摘要 · Abstract (English)
The rapid growth of multimodal intelligence on resource-constrained and heterogeneous domestic hardware exposes critical bottlenecks: multimodal feature heterogeneity, real-time requirements in dynamic scenarios, and hardware-specific operator redundancy. This work introduces a quantum-inspired geometric framework for neural operators that represents each operator by its normalized singular value spectrum on the Bloch hypersphere. We prove a tight spectral-to-functional equivalence theorem showing that vanishing Fubini--Study/Wasserstein-2 distance implies provable functional closeness, establishing the first rigorous foundation for cross-modal and cross-architecture operator substitutability. Based on this metric, we propose Quantum Metric-Driven Functional Redundancy Graphs (QM-FRG) and one-shot structured pruning. Controlled simulation validates the superiority of the proposed metric over magnitude and random baselines. An extensive experimental validation on large-scale multimodal transformers and domestic heterogeneous hardware (Huawei Ascend, Cambricon MLU, Kunlunxin) hardware is deferred to an extended journal version currently in preparation.
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