扩散模型中的量子电路：角度嵌入失败的公平比较研究与机理分析
Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures
- NextITS Co., Ltd.（NextITS有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究变分量子电路与扩散模型集成，通过多实验发现量子核心在FID上与经典控制相当但未证明参数效率优势，还识别出基于分数的NCSN的结构故障及角度嵌入失败问题，提供了公平比较协议和机理说明。
AI中文摘要：
我们通过挤压激励（SE）通道调制支架研究变分量子电路（VQC）与扩散模型的集成，以分离量子贡献。在MNIST和CIFAR - 10数据集上对DDPM和潜在扩散进行角色匹配的经典控制及多种子显著性测试，对MNIST进行基于分数的NCSN研究。发现量子核心在DDPM和潜在扩散中平均FID与经典控制相当，EfficientSU2的配对采样种子测试无显著差异。虽量子核心参数比角色匹配控制少4.5至9倍，但参数匹配经典控制FID相当，未证明量子参数效率优势。还发现基于分数的NCSN的结构故障：无界分数目标使角度嵌入输入超出旋转门2π周期，导致相位混叠和量子调制器崩溃。通过边界变换可改善量子核心。本研究提供量子增强生成模型的公平比较协议及角度嵌入失败的机理说明。
英文摘要：
We study the integration of variational quantum circuits (VQCs) into diffusion models through a squeeze-and-excitation (SE) channel-modulation scaffold that isolates the quantum contribution. Using a role-matched classical control and multi-seed significance testing across DDPM and latent diffusion on MNIST and CIFAR-10, with a score-based NCSN study on MNIST, we find that quantum cores achieve comparable mean FID to the classical control across DDPM and latent diffusion, while paired sampling-seed tests for EfficientSU2 detect no statistically significant difference. Although the quantum cores use $4.5$--$9\times$ fewer core parameters than the role-matched control, parameter-matched classical controls attain comparable mean FID, so the experiments do not establish a quantum parameter-efficiency advantage. We further identify a structural failure in score-based NCSN: the unbounded score target, proportional to $1/σ$, drives angle-embedding inputs far beyond the $2π$ period of rotation gates, causing phase aliasing and collapse of the quantum modulator. A bounding transformation, $θ\leftarrow π\tanh(\cdot)$, maps inputs to the non-aliasing domain and substantially improves both quantum cores. Since all circuits are classically simulated at a few-qubit scale, we do not claim quantum advantage. Instead, the study provides a fair-comparison protocol for quantum-enhanced generative models and a mechanistic account of when and why angle embeddings fail.
