Article(id=1322960895785185387, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, articleNumber=null, orderNo=null, doi=10.11834/jig.250566, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1763913600000, receivedDateStr=2025-11-24, revisedDate=1765900800000, revisedDateStr=2025-12-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348056979, onlineDateStr=2026-10-30, pubDate=1786809600000, pubDateStr=2026-08-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348056979, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348056979, creator=13701087609, updateTime=1793348056979, updator=13701087609, issue=Issue{id=1322960856543273127, tenantId=1146029695717560320, journalId=1249024232475115590, year='2026', volume='31', issue='8', pageStart='2687', pageEnd='3074', issueExtLink='null', onlineDate='null', pubDate='1786809600000', pubDateStr='2026-08-16', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793348047624, creator='13701087609', updateTime=1793348605344, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322963195874378645, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322963195874378646, tenantId=1146029695717560320, journalId=1249024232475115590, issueId=1322960856543273127, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2814, endPage=2843, ext={EN=ArticleExt(id=1322960895965540460, articleId=1322960895785185387, tenantId=1146029695717560320, journalId=1249024232475115590, language=EN, title=Progress of research on quantum image information processing methods, columnId=1249044010699002654, journalTitle=Journal of Image and Graphics, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

In the era of big data and artificial intelligence，image processing has emerged as a cornerstone technology that drives advancements across diverse fields，including medical diagnostics，autonomous systems，and remote sensing. The exponential growth of high-resolution image data has created unprecedented computational demands that classical computing architectures increasingly struggle to meet. As the progress under Moore’s law slows，quantum computing has emerged as a transformative paradigm； it leverages fundamental quantum properties，including superposition，entanglement，and quantum parallelism，to overcome classical limitations. This convergence has given rise to the rapidly evolving field of quantum image processing （QIP），which promises to redefine the boundaries of computational imaging capabilities. This comprehensive survey provides a systematic analysis of QIP’s remarkable evolution over the past five years，documenting its transition from a theoretical concept to an emerging practical technology. We introduce an integrated classification framework that mirrors the complete image processing pipeline——from fundamental representation to advanced applications——thus creating a coherent perspective that effectively connects quantum mechanical principles with practical implementation requirements. The foundation of this framework lies in quantum image representation models，where innovative schemes，such as novel enhanced quantum repre-sentation，flexible representation of quantum images，and generalized quantum image representation，have advanced to support arbitrary image dimensions and complex color spaces while simultaneously optimizing critical qubit utilization efficiency. Building upon this foundation，QIP techniques demonstrate increasingly sophisticated capabilities. Advanced filtering algorithms achieve exponential speedups in noise reduction tasks，and quantum Fourier and wavelet transforms enable unprecedented efficiency in image compression. Quantum parallelism facilitates rapid morphological analysis and feature extraction，substantially accelerating fundamental image processing operations. Aside from basic processing，quantum-enhanced methods for edge detection，segmentation，and feature matching also demonstrate remarkable efficiency improvements in extracting meaningful information from complex visual data. The security domain benefits substantially from quantum approaches，with innovative encryption protocols based on quantum walks and chaos theory providing theoretically unbreakable protection for sensitive visual data. Quantum watermarking techniques that utilize entanglement phenomena offer fundamentally undetectable data-hiding capabilities，creating new paradigms for digital rights management and secure communication. In machine learning applications，quantum support vector machines enhance classification accuracy for high-dimensional image data，and quantum convolutional neural networks revolutionize feature extraction through inherent quantum parallelism. Quantum generative adversarial networks enable sophisticated synthetic data generation and have notable applications in data augmentation and privacy preservation. This review emphasizes an integrated perspective that connects theoretical advances with practical implementations across multiple application domains. Our structured analytical framework clearly reveals how abstract quantum principles translate into operational capabilities while simultaneously addressing the critical challenges currently hindering widespread adoption. The noisy intermediate-scale quantum era presents substantial hardware limitations，where qubit decoherence and elevated error rates remain persistent barriers to reliable operation. The considerable theory-practice implementation gap，where algorithm performance on actual quantum devices often falls short of theoretical predictions，requires careful consideration. Moreover，scalability issues in real-world applications，where the substantial overhead of quantum data encoding and measurement can diminish computational advantages，present additional hurdles that must be overcome. Resource management considerations examine how different representation models directly affect quantum circuit complexity and execution time，providing crucial insights for practical algorithm design and implementation. Looking forward，several promising pathways can be taken. Examples include hybrid quantum-classical architectures that leverage the complementary strengths of both computational paradigms，advanced error mitigation techniques that compensate for current hardware imperfections，and quantum-inspired classical algorithms that can deliver enhanced performance on conventional hardware while preserving certain quantum advantages. The discussion extends to current hardware platforms，including superconducting qubits，trapped ions，and photonic quantum systems，to provide practical guidance for experimental research and implementation strategies. Through systematic analysis and critical evaluation，this review demonstrates quantum computing’s massive potential to achieve exponential acceleration in computational tasks，enhance security beyond classical limitations，and enable unprecedented capabilities in image analysis and understanding. Emerging applications in medical imaging，remote sensing，and security systems highlight the transformative potential of these technologies. As quantum hardware continues its steady evolution，the strategic integration of quantum approaches into conventional image processing workflows promises to redefine computational boundaries across numerous disciplines，including healthcare，environmental monitoring，industrial inspection，and artificial intelligence. The field currently stands at the threshold of a new computational paradigm，requiring continued technical innovation and strategic vision to successfully navigate the coming transition. By synthesizing recent advances，clearly identifying current limitations，and outlining promising research directions，this survey aims to accelerate the development and adoption of QIP technologies. The ultimate integration of quantum approaches with classical image processing methodologies promises to create powerful hybrid frameworks that leverage the distinctive strengths of both computational paradigms，fundamentally transforming how we extract knowledge and value from visual data in the emerging quantum era and enabling capabilities beyond the reach of either approach independently. The mentioned algorithms are summarized at https://github.com/cococococo7/QIIP.

, authors=Pin Lyu, Yiquan Wu^*, authorsList=Pin Lyu, Yiquan Wu, authorCompany=null, correspAuthors=Yiquan Wu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322960904068935810, articleId=1322960895785185387, tenantId=1146029695717560320, journalId=1249024232475115590, language=CN, title=量子图像信息处理方法研究进展, columnId=1249044010992603937, journalTitle=中国图象图形学报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

量子计算凭借其叠加性、纠缠性和并行性等核心特性，为解决图像处理领域的新型复杂难题提供了革命性的思路和工具，由此催生了量子图像信息处理这一研究领域。近年来，量子图像信息处理引起了越来越多研究人员的关注。本文对近5年来的相关研究进行了调研。首先介绍了量子计算的特性，并回顾了量子图像信息处理的发展历程。其次总结了近5年来的量子图像表示方法。从量子图像处理方法、量子图像分析方法、量子图像信息安全技术和量子机器学习方法这4个方面详细阐明了量子图像信息处理方法的研究进展，并对比了所采用方法的核心创新、性能突破和关键局限。其中量子图像处理方法涵盖量子图像滤波、量子图像压缩和量子形态变换；量子图像分析方法涉及量子边缘检测、量子图像分割和量子图像匹配3个方面；量子图像信息安全技术包括量子图像加密和量子图像数据隐藏；而量子支持向量机、量子卷积神经网络及量子生成对抗网络，则归属于量子机器学习的关键实现路径。最后指出目前量子图像信息处理方法在硬件、图像表示模型、协同效率和量子图像安全机制上面临的挑战，并针对挑战对未来进一步的研究方向进行了展望，提及的算法已汇总至https://github.com/cococococo7/QIIP。

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吕品，男，博士研究生，主要研究方向为量子图像信息处理。E-mail： bx2504004@nuaa.edu.cn

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吕品，男，博士研究生，主要研究方向为量子图像信息处理。E-mail： bx2504004@nuaa.edu.cn

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吴一全，通信作者，男，教授，博士生导师，主要研究方向为量子图像信息处理、遥感图像处理与理解、红外目标检测与识别、视觉检测与图像测量、视频处理与智能分析。E-mail： nuaaimage@163.com

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吴一全，通信作者，男，教授，博士生导师，主要研究方向为量子图像信息处理、遥感图像处理与理解、红外目标检测与识别、视觉检测与图像测量、视频处理与智能分析。E-mail： nuaaimage@163.com

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（（a） original image； （b） filtered result image）

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（（a） reference image； （b） images to be registered；

（c） result image）

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方法	核心创新	性能突破	关键局限
张智等人（2016）	图像信息分层转换为量子叠加态	对比度↑至22.74（原22.65）	计算复杂度上升
	量子面内范数优化约束	信息熵↑至7.41（原6.98）	需并行算法优化实时性
李盼池和孙昊（2018）	量子Oracle划分多频段图像	较文献方法PSNR↑0.198 6 dB	依赖量子计算硬件
	陷波滤波器设计支持周期噪声消除	同时输出三频段图像	空域滤波未实现
赵娅等人（2021）	量子比较器迭代排序计算中值	PSNR↑16.22 dB	复杂度上升
	循环平移模块同步处理边缘像素	椒盐噪声PSNR↑18 dB	经典仿真受限硬件
Ji等人（2022）	高斯—小波混合滤波	PSNR↑ > 8 dB	小波变换限制实时性
	小波系数优化光子计数	光学平台实测结构相似性↑	30 ps重合门宽增硬件复杂度
Pachuau和Saha （2022）	Y轴旋转编码像素强度	量子成本仅3(q-1)	依赖概率重建
	角度加减实现泛化滤波	平滑滤波RMSE = 14.77	需多次量子测量提取结果
Atchade-Adelomou和 Alonso-Linaje（2022）	变分量子电路替代CNN卷积层	训练速度↑40%	需ml.c5.2xlarge级硬件
	量子点积实现特征提取	测试集准确率 > 40%（50样本）	梯度计算量子线路深度↑
Yuan等人（2022a）	位置比特复用架构	辅助量子比特↓至O(1)	512 × 512像素图像需 320万次测量
	8邻域图像共享量子态	时间复杂度O(q)（原O(q²)）	IBM Q平台延迟敏感
冉术坤等人（2023）	量子纠缠绑定邻域像素避免卷积	时间复杂↓至O(10n² + 216q²)	量子比较器复杂度高
	补码电路简化矢量距离计算		边缘像素处理需额外循环移位
Cui等人（2024）	固定点Grover搜索模拟频域滤波	信噪比 \lambda ≥ {\lambda }_0 时指数加速	非高效编码图像加速失效
	针对高效编码图像设计	SSIM↑至0.956（低通滤波）	量子噪声导致输出失真
Yuan等人（2025）	优化NCQI存储模型 （辅助量子比特↓）	电路复杂↓至O(q)	硬件限制
	量子除法器+噪声检测模块	PSNR 26.75 dB（高斯噪声）	盐噪密度 > 0.1时细节损失显著
Mu等人（2025）	NEQR模型实现经典引导滤波量子化	时间复杂指数↓至O(q²)	依赖高效量子态制备
	量子并行优化线性系数计算	PSNR↑6.45 dB (噪声图: 20.33 dB）	大规模图像需优化量子比特数

), ArticleFig(id=1322960909366341807, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表1, caption=

量子图像滤波方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
张智等人（2016）	图像信息分层转换为量子叠加态	对比度↑至22.74（原22.65）	计算复杂度上升
	量子面内范数优化约束	信息熵↑至7.41（原6.98）	需并行算法优化实时性
李盼池和孙昊（2018）	量子Oracle划分多频段图像	较文献方法PSNR↑0.198 6 dB	依赖量子计算硬件
	陷波滤波器设计支持周期噪声消除	同时输出三频段图像	空域滤波未实现
赵娅等人（2021）	量子比较器迭代排序计算中值	PSNR↑16.22 dB	复杂度上升
	循环平移模块同步处理边缘像素	椒盐噪声PSNR↑18 dB	经典仿真受限硬件
Ji等人（2022）	高斯—小波混合滤波	PSNR↑ > 8 dB	小波变换限制实时性
	小波系数优化光子计数	光学平台实测结构相似性↑	30 ps重合门宽增硬件复杂度
Pachuau和Saha （2022）	Y轴旋转编码像素强度	量子成本仅3(q-1)	依赖概率重建
	角度加减实现泛化滤波	平滑滤波RMSE = 14.77	需多次量子测量提取结果
Atchade-Adelomou和 Alonso-Linaje（2022）	变分量子电路替代CNN卷积层	训练速度↑40%	需ml.c5.2xlarge级硬件
	量子点积实现特征提取	测试集准确率 > 40%（50样本）	梯度计算量子线路深度↑
Yuan等人（2022a）	位置比特复用架构	辅助量子比特↓至O(1)	512 × 512像素图像需 320万次测量
	8邻域图像共享量子态	时间复杂度O(q)（原O(q²)）	IBM Q平台延迟敏感
冉术坤等人（2023）	量子纠缠绑定邻域像素避免卷积	时间复杂↓至O(10n² + 216q²)	量子比较器复杂度高
	补码电路简化矢量距离计算		边缘像素处理需额外循环移位
Cui等人（2024）	固定点Grover搜索模拟频域滤波	信噪比 \lambda ≥ {\lambda }_0 时指数加速	非高效编码图像加速失效
	针对高效编码图像设计	SSIM↑至0.956（低通滤波）	量子噪声导致输出失真
Yuan等人（2025）	优化NCQI存储模型 （辅助量子比特↓）	电路复杂↓至O(q)	硬件限制
	量子除法器+噪声检测模块	PSNR 26.75 dB（高斯噪声）	盐噪密度 > 0.1时细节损失显著
Mu等人（2025）	NEQR模型实现经典引导滤波量子化	时间复杂指数↓至O(q²)	依赖高效量子态制备
	量子并行优化线性系数计算	PSNR↑6.45 dB (噪声图: 20.33 dB）	大规模图像需优化量子比特数

), ArticleFig(id=1322960909571862704, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 2, caption=Summary of quantum image compression methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
苏枫等人（2015）	OMP算法 + DCT稀疏化降采样 高斯随机矩阵优化测量	sast图像采样550次（↓36倍） 航天图标采样8 000次 （↓125倍）	图像增大时重构时间线性增长 大尺寸优化有限
Dai等人（2021）	QDCT多图融合压缩 4D混沌序列像素扩散	密文熵7.998 3 相关系数 ≈ 0.006	5D混沌电路复杂度过高 压缩率固定不灵活
臧一鸣等人（2022）	热金属码量子化实现伪彩色 R/G/B变换器量子电路设计 （2 947门）	PSNR 24.3～36.4 dB 空间复杂度↓指数级	量子比特初始化依赖经典模拟 分段函数适应性差
Ma和Zhou（2023）	Haar小波多图融合压缩 5D混沌加密 + Fibonacci置乱	相关系数 ≈ 0.009 抗剪切PSNR > 15 dB	高压缩率（75%）PSNR↓至 18 dB 置乱迭代50次实时性差
Majji等人（2023）	比较量子编码技术 提出4种振幅嵌入量子压缩技术	75%压缩率（卫星图像） SSIM > 0.978（光学图像）	量子硬件资源有限 仅支持4量子位电路
Wang等人（2024b）	参数化量子电路实现量子自编码器 混合量子—经典梯度优化压缩图像	保真度0.957±0.023 压缩率4∶1（量子位↓）	资源密集（训练需多次迭代） 依赖经典模拟硬件
Hai等人（2025）	泰勒展开优化量子编译参数估计 邻域块参数传递减少冗余计算	迭代次数↓86% 损失 < 0.38×10⁻³（高分辨率图像）	图像质量依赖阈值调整 “转移”率仅88%
Ko等人（2025）	傅里叶近似量子概率编码 结合UCR压缩和图像分区策略	门计数↓96%（1 024 × 1 024） 最大门复杂度↓80%	性能依赖图像可压缩性 预处理时间仍较高（313.58 s）

), ArticleFig(id=1322960909659943089, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表2, caption=

量子图像压缩方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
苏枫等人（2015）	OMP算法 + DCT稀疏化降采样 高斯随机矩阵优化测量	sast图像采样550次（↓36倍） 航天图标采样8 000次 （↓125倍）	图像增大时重构时间线性增长 大尺寸优化有限
Dai等人（2021）	QDCT多图融合压缩 4D混沌序列像素扩散	密文熵7.998 3 相关系数 ≈ 0.006	5D混沌电路复杂度过高 压缩率固定不灵活
臧一鸣等人（2022）	热金属码量子化实现伪彩色 R/G/B变换器量子电路设计 （2 947门）	PSNR 24.3～36.4 dB 空间复杂度↓指数级	量子比特初始化依赖经典模拟 分段函数适应性差
Ma和Zhou（2023）	Haar小波多图融合压缩 5D混沌加密 + Fibonacci置乱	相关系数 ≈ 0.009 抗剪切PSNR > 15 dB	高压缩率（75%）PSNR↓至 18 dB 置乱迭代50次实时性差
Majji等人（2023）	比较量子编码技术 提出4种振幅嵌入量子压缩技术	75%压缩率（卫星图像） SSIM > 0.978（光学图像）	量子硬件资源有限 仅支持4量子位电路
Wang等人（2024b）	参数化量子电路实现量子自编码器 混合量子—经典梯度优化压缩图像	保真度0.957±0.023 压缩率4∶1（量子位↓）	资源密集（训练需多次迭代） 依赖经典模拟硬件
Hai等人（2025）	泰勒展开优化量子编译参数估计 邻域块参数传递减少冗余计算	迭代次数↓86% 损失 < 0.38×10⁻³（高分辨率图像）	图像质量依赖阈值调整 “转移”率仅88%
Ko等人（2025）	傅里叶近似量子概率编码 结合UCR压缩和图像分区策略	门计数↓96%（1 024 × 1 024） 最大门复杂度↓80%	性能依赖图像可压缩性 预处理时间仍较高（313.58 s）

), ArticleFig(id=1322960909735440562, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 3, caption=Summary of quantum morphological transformation methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Wang等人（2024d）	膨胀/腐蚀/低帽/高帽专用量子电路 全像素并行处理	复杂度 O(n² + q) 支持灰度图像并行处理	精度损失未量化（无临床对比） 骨架提取未实现
Li等人（2024）	五输入新型多数门 QCA单元直接交互机制	0.05 μm²面积 能耗2.02 eV	容错设计缺失 仅实现基础操作（不支持开闭运算）
Lin等人（2024）	双五输入容错多数门 控制线动态切换	面积0.07 μm² 容错率90%	灰度处理能力缺失 多操作串联延迟未测试
Abdullah-Al-Shafi（2025）	双多数门动态控制 医学影像实时处理	0.75时钟周期延迟 抗错率95%	仅支持二值图像 未实现开闭运算
Tang等人（2025）	容错QCA电路优化 缺陷容忍提升	容错率80%～90% 能耗3.54～3 eV	工艺敏感性未分析 辐射环境未验证
张亚奇等人（2025）	位置—像素双基态编码 任意尺寸适配	任意尺寸适配 去噪加速3.7倍	PSNR未量化 量子纠缠寿命未提及

), ArticleFig(id=1322960909819326643, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表3, caption=

量子形态变换方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Wang等人（2024d）	膨胀/腐蚀/低帽/高帽专用量子电路 全像素并行处理	复杂度 O(n² + q) 支持灰度图像并行处理	精度损失未量化（无临床对比） 骨架提取未实现
Li等人（2024）	五输入新型多数门 QCA单元直接交互机制	0.05 μm²面积 能耗2.02 eV	容错设计缺失 仅实现基础操作（不支持开闭运算）
Lin等人（2024）	双五输入容错多数门 控制线动态切换	面积0.07 μm² 容错率90%	灰度处理能力缺失 多操作串联延迟未测试
Abdullah-Al-Shafi（2025）	双多数门动态控制 医学影像实时处理	0.75时钟周期延迟 抗错率95%	仅支持二值图像 未实现开闭运算
Tang等人（2025）	容错QCA电路优化 缺陷容忍提升	容错率80%～90% 能耗3.54～3 eV	工艺敏感性未分析 辐射环境未验证
张亚奇等人（2025）	位置—像素双基态编码 任意尺寸适配	任意尺寸适配 去噪加速3.7倍	PSNR未量化 量子纠缠寿命未提及

), ArticleFig(id=1322960909882241204, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 4, caption=Summary of quantum edge detection methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Tariq等人（2021）	QGA优化Tsallis熵双阈值分割 SVM三新型训练向量设计	PSNR↑至7.97 dB（超QGA 3.2%） SSIM↑至0.302（超Sobel 278%）	训练仅用直线边缘模拟图像 指标与视觉结果偏差
Chetia和Sahu（2024）	扩展Sobel算子至4方向 概率幅置换算符U优化电路	边缘像素数量显著增加 电路复杂度降至O(n²+ q³)	仅支持2ⁿ × 2ⁿ尺寸图像 未在真实量子硬件验证
吴琼和马雷（2022）	LoG算子量子化实现 退相干鲁棒性设计	抗噪性↑ 比特数↓18	仅MATLAB仿真 图像尺寸需2ⁿ
Liu和Wang（2022）	8方向梯度同步计算 集成边缘跟踪技术	边缘信息量↑ 复杂度↓至O(n² + q²)	算法动态适应性不足​ 未考虑噪声影响
Geng等人（2022）	量子人工神经元架构 6种电路变体优化	256 × 256像素图像处理 单像素处理时间↓至0.57 s	仅支持直线方向滤波 依赖IBM后端校准波动
董跃华和张先光（2023）	NEQR模型量子形态学电路 分步量子流程: 去噪处理 + 边缘提取	量子位↓至2n + 10 实现IBM Q平台真实验证	仅支持二值图像 大尺寸图像处理慢
Fan和Xiao（2023）	高斯差分量子滤波 4方向零交叉检测量子实现	量子位仅需2n + 10 抗噪性强（高斯噪声下边缘完整）	未扩展至彩色图像 大尺寸图像处理慢
Yuan等人（2024）	NEQR-MI多图像存储模型 量子绝对值减法器	电路复杂度↓至O(q) 512 × 512像素图像边缘检测	量子资源消耗激增 边缘像素依赖镜像填充
Wang等人（2024a）	量子小波正/逆变换 频域边缘重构	EPI指数↑12% 多尺度细节保留率↑17%	仅灰度图像有效 复杂度O( N 2)偏高
Chetia和Sahu（2024）	双量子空间滤波（高斯 + 中值核） 希尔伯特空间自适应阈值	PSNR↑41.53 dB 盐椒噪声抑制率↑36%	高密度噪声抑制不足 仅MATLAB仿真
Chaduvula等人（2024）	Hill熵量子边缘检测 神经网络混合滤波	准确率↑97.5% 量子噪声抑制率↑42.59%	NEQR物理实现缺陷 参数α需手动调整
Shubha等人（2024）	FRQI编码融合QHED 局部测量后量子态重置	轮廓识别精度↑31% 空间复杂度维持O(log N )	预处理增加时间成本 仅验证2 × 2小图像
Llorens等人（2025）	Schur-Weyl对偶性边界检测 平方根测量协议	成功率↑65% (d = 2) 长字符串检测效率↑40%	仅1D场景验证 未处理混合态噪声

), ArticleFig(id=1322960909961932981, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表4, caption=

量子边缘检测方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Tariq等人（2021）	QGA优化Tsallis熵双阈值分割 SVM三新型训练向量设计	PSNR↑至7.97 dB（超QGA 3.2%） SSIM↑至0.302（超Sobel 278%）	训练仅用直线边缘模拟图像 指标与视觉结果偏差
Chetia和Sahu（2024）	扩展Sobel算子至4方向 概率幅置换算符U优化电路	边缘像素数量显著增加 电路复杂度降至O(n²+ q³)	仅支持2ⁿ × 2ⁿ尺寸图像 未在真实量子硬件验证
吴琼和马雷（2022）	LoG算子量子化实现 退相干鲁棒性设计	抗噪性↑ 比特数↓18	仅MATLAB仿真 图像尺寸需2ⁿ
Liu和Wang（2022）	8方向梯度同步计算 集成边缘跟踪技术	边缘信息量↑ 复杂度↓至O(n² + q²)	算法动态适应性不足​ 未考虑噪声影响
Geng等人（2022）	量子人工神经元架构 6种电路变体优化	256 × 256像素图像处理 单像素处理时间↓至0.57 s	仅支持直线方向滤波 依赖IBM后端校准波动
董跃华和张先光（2023）	NEQR模型量子形态学电路 分步量子流程: 去噪处理 + 边缘提取	量子位↓至2n + 10 实现IBM Q平台真实验证	仅支持二值图像 大尺寸图像处理慢
Fan和Xiao（2023）	高斯差分量子滤波 4方向零交叉检测量子实现	量子位仅需2n + 10 抗噪性强（高斯噪声下边缘完整）	未扩展至彩色图像 大尺寸图像处理慢
Yuan等人（2024）	NEQR-MI多图像存储模型 量子绝对值减法器	电路复杂度↓至O(q) 512 × 512像素图像边缘检测	量子资源消耗激增 边缘像素依赖镜像填充
Wang等人（2024a）	量子小波正/逆变换 频域边缘重构	EPI指数↑12% 多尺度细节保留率↑17%	仅灰度图像有效 复杂度O( N 2)偏高
Chetia和Sahu（2024）	双量子空间滤波（高斯 + 中值核） 希尔伯特空间自适应阈值	PSNR↑41.53 dB 盐椒噪声抑制率↑36%	高密度噪声抑制不足 仅MATLAB仿真
Chaduvula等人（2024）	Hill熵量子边缘检测 神经网络混合滤波	准确率↑97.5% 量子噪声抑制率↑42.59%	NEQR物理实现缺陷 参数α需手动调整
Shubha等人（2024）	FRQI编码融合QHED 局部测量后量子态重置	轮廓识别精度↑31% 空间复杂度维持O(log N )	预处理增加时间成本 仅验证2 × 2小图像
Llorens等人（2025）	Schur-Weyl对偶性边界检测 平方根测量协议	成功率↑65% (d = 2) 长字符串检测效率↑40%	仅1D场景验证 未处理混合态噪声

), ArticleFig(id=1322960910037430454, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 5, caption=Summary of quantum image segmentation methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Tariq Jamal等人（2023）	量子Rényi熵替代经典Kapur熵 量子遗传算法优化阈值搜索	PSNR↑29.21 dB（较经典高15%） 7阈值时收敛速度提升40%	仅支持灰度图像 量子电路深度随阈值数指数增长
Liu等人（2022）	量子形态学底帽/顶帽变换电路 量子循环移位架构	512 × 512像素图像处理快66% 辅助量子位仅需3q + 7	结构元素固定为3 × 3 光照不均场景鲁棒性不足
Konar等人（2022）	Qutrit三态量子神经元 自适应多类量子Sigmoid激活	Dice系数↑0.927（超经典8%） 迭代次数↓至12轮	参数敏感性强 超像素预处理增加时耗
Yuan等人（2022b）	量子减法器/除法器模块 多图像量子叠加编码	维度↓99% IBM Q实验验证可行性	T-count高达97q-63 仅支持2n尺寸图像
Shi等人（2023）	超像素量子游走模型 HOG纹理量子化融合	mIoU↑0.927 处理速度↑66%	超像素边界模糊 弱纹理区域误分割率↑15%
Wang等人（2022a）	动态阈值调整机制 量子比特重用策略	精度 ↑5.27%（超MATLAB） 辅助量子位需求 ↓50%	仅验证4 × 4小图像 多目标分割性能下降
López等人（2023）	Clifford+T门容错设计 量子比较器T-count优化	T-count ↓57% 成功率 ↑90%	电路深度增加30% 需量子纠错支持
Presles等人（2024）	量子退火映射MRF能量函数 量子模糊激活函数	速度↑8倍（较模拟退火） 超参数需求↓70%	D-Wave噪声敏感 需200 μs退火时间
Wang等人（2024c）	QUBO模型构建 量子最大流最小割算法	成功率↑2倍（较QAOA） 退火时间↓75%	仅支持二分类 船舶特定场景泛化弱
Konar等人（2024）	3D量子张量神经元 体素量子Sigmoid	速度↑300%（较3D-UNet） 内存占用↓60%	脑瘤边缘分割模糊 需GPU集群支持

), ArticleFig(id=1322960910104539319, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表5, caption=

量子图像分割方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Tariq Jamal等人（2023）	量子Rényi熵替代经典Kapur熵 量子遗传算法优化阈值搜索	PSNR↑29.21 dB（较经典高15%） 7阈值时收敛速度提升40%	仅支持灰度图像 量子电路深度随阈值数指数增长
Liu等人（2022）	量子形态学底帽/顶帽变换电路 量子循环移位架构	512 × 512像素图像处理快66% 辅助量子位仅需3q + 7	结构元素固定为3 × 3 光照不均场景鲁棒性不足
Konar等人（2022）	Qutrit三态量子神经元 自适应多类量子Sigmoid激活	Dice系数↑0.927（超经典8%） 迭代次数↓至12轮	参数敏感性强 超像素预处理增加时耗
Yuan等人（2022b）	量子减法器/除法器模块 多图像量子叠加编码	维度↓99% IBM Q实验验证可行性	T-count高达97q-63 仅支持2n尺寸图像
Shi等人（2023）	超像素量子游走模型 HOG纹理量子化融合	mIoU↑0.927 处理速度↑66%	超像素边界模糊 弱纹理区域误分割率↑15%
Wang等人（2022a）	动态阈值调整机制 量子比特重用策略	精度 ↑5.27%（超MATLAB） 辅助量子位需求 ↓50%	仅验证4 × 4小图像 多目标分割性能下降
López等人（2023）	Clifford+T门容错设计 量子比较器T-count优化	T-count ↓57% 成功率 ↑90%	电路深度增加30% 需量子纠错支持
Presles等人（2024）	量子退火映射MRF能量函数 量子模糊激活函数	速度↑8倍（较模拟退火） 超参数需求↓70%	D-Wave噪声敏感 需200 μs退火时间
Wang等人（2024c）	QUBO模型构建 量子最大流最小割算法	成功率↑2倍（较QAOA） 退火时间↓75%	仅支持二分类 船舶特定场景泛化弱
Konar等人（2024）	3D量子张量神经元 体素量子Sigmoid	速度↑300%（较3D-UNet） 内存占用↓60%	脑瘤边缘分割模糊 需GPU集群支持

), ArticleFig(id=1322960910175842488, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 6, caption=Summary of quantum image matching methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Jiang等人（2016）	Grover算法修改目标像素概率 仅关注单一像素减少测量次数	复杂度↓至O(2n) 测量概率↑至96.13%	仅支持精确匹配 多匹配点或空匹配时输出随机
Dang等人（2017）	改进算法利用整个匹配区域 解决多匹配点随机错误问题	复杂度↓至O(2(2m+1) + 2n) 准确率↑至近100%	多匹配点时可能随机选择 网络复杂度高
Luo等人（2018）	基于灰度差异的模糊匹配方案 NEQR表示结合量子操作	量子并行实现指数加速 实验中阈值T₀ = 32时匹配成功	固定阈值需手动调整
唐泽恬等人（2020）	SIFT特征结合量子全局描述子 量子点密度动态调整对比度阈值	误匹配率↓至10.84%～20% 特征点数量控制更合理	依赖经典特征提取 量子编码复杂度高
Benkner等人（2021）	量子循环α扩展避免显式约束 量子黄金分割搜索加速一维优化	医学图像配准精度ε < 0.001 计算资源消耗对数级↓	电路深度随图像尺寸增长 需经典后处理
Yetis和Karaköse（2022）	量子窗口并行处理框架 量子全局描述子增强特征区分度	二值模板匹配误差 < 3% 并行计算加速显著↑	仅支持二值图像 抗噪能力弱
Shu等人（2022）	SIFT特征量子化 量子振幅估计加速相似度计算	NWPU数据集误匹配率20% Mnist数据集F1值0.77	需大量量子门操作 特征维度受限
Dang等人（2017）	改进算法利用整个匹配区域 解决多匹配点随机错误问题	复杂度↓至O(2(2m+1) + 2n) 准确率↑至近100%	多匹配点时可能随机选择 网络复杂度高
Luo等人（2018）	基于灰度差异的模糊匹配方案 NEQR表示结合量子操作	量子并行实现指数加速 实验中阈值T₀ = 32时匹配成功	固定阈值需手动调整
Chen等人（2023b）	量子Powell方法实现参数优化 量子黄金分割搜索替代一维遍历	亚像素级配准精度 (ε < 0.001)	量子态制备复杂 噪声敏感度高
Heidari等人（2024）	量子退火优化立体匹配能量函数 动态阈值控制特征点数量	QAPLIB实例优化率9/10 (n = 16)	需多次量子测量 大规模问题受限于量子比特数

), ArticleFig(id=1322960910247145657, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表6, caption=

量子图像匹配方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Jiang等人（2016）	Grover算法修改目标像素概率 仅关注单一像素减少测量次数	复杂度↓至O(2n) 测量概率↑至96.13%	仅支持精确匹配 多匹配点或空匹配时输出随机
Dang等人（2017）	改进算法利用整个匹配区域 解决多匹配点随机错误问题	复杂度↓至O(2(2m+1) + 2n) 准确率↑至近100%	多匹配点时可能随机选择 网络复杂度高
Luo等人（2018）	基于灰度差异的模糊匹配方案 NEQR表示结合量子操作	量子并行实现指数加速 实验中阈值T₀ = 32时匹配成功	固定阈值需手动调整
唐泽恬等人（2020）	SIFT特征结合量子全局描述子 量子点密度动态调整对比度阈值	误匹配率↓至10.84%～20% 特征点数量控制更合理	依赖经典特征提取 量子编码复杂度高
Benkner等人（2021）	量子循环α扩展避免显式约束 量子黄金分割搜索加速一维优化	医学图像配准精度ε < 0.001 计算资源消耗对数级↓	电路深度随图像尺寸增长 需经典后处理
Yetis和Karaköse（2022）	量子窗口并行处理框架 量子全局描述子增强特征区分度	二值模板匹配误差 < 3% 并行计算加速显著↑	仅支持二值图像 抗噪能力弱
Shu等人（2022）	SIFT特征量子化 量子振幅估计加速相似度计算	NWPU数据集误匹配率20% Mnist数据集F1值0.77	需大量量子门操作 特征维度受限
Dang等人（2017）	改进算法利用整个匹配区域 解决多匹配点随机错误问题	复杂度↓至O(2(2m+1) + 2n) 准确率↑至近100%	多匹配点时可能随机选择 网络复杂度高
Luo等人（2018）	基于灰度差异的模糊匹配方案 NEQR表示结合量子操作	量子并行实现指数加速 实验中阈值T₀ = 32时匹配成功	固定阈值需手动调整
Chen等人（2023b）	量子Powell方法实现参数优化 量子黄金分割搜索替代一维遍历	亚像素级配准精度 (ε < 0.001)	量子态制备复杂 噪声敏感度高
Heidari等人（2024）	量子退火优化立体匹配能量函数 动态阈值控制特征点数量	QAPLIB实例优化率9/10 (n = 16)	需多次量子测量 大规模问题受限于量子比特数

), ArticleFig(id=1322960910314254522, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 7, caption=Summary of quantum image encryption methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
李盼池和卢爱平（2016）	双随机相位加密量子化实现 完整量子线路设计	密钥空间扩大 解密错误密钥恢复PSNR < 8 dB	密钥敏感度过高 电路深度随像素数指数增长
李盼池和曹梓崎（2017a）	像素位置置乱加密算法 量子比特旋转加密算法	加密图像相关系数绝对值平均0.007 0 直方图均匀分布，安全性高	测量需多次制备态 量子电路深度
郭海儒等人（2019）	改进的GQIR模型突破尺寸限制 动态密钥生成	加密图像三基色相关系数均值 ≈ 0.005	24维旋转门难分解 随机序列占用大量存储空间
Zhang和Wang（2023）	随机起点锯齿扫描置乱 8阶魔方位级旋转	信息熵7.999 4 抗49%遮挡攻击	时间复杂度高 魔方映射存储开销大
Agrawal等人（2024）	四子图DCT稀疏化预处理 量子逻辑映射生成加密矩阵	抗高斯噪声PSNR↑至33 dB 嵌入容量↑至4 bpp（双水印）	缩放因子k影响鲁棒性 子图划分↑63%时耗
Aly（2024）	NQBID量子表示 块球面参数加密	PSNR = Inf (↑100%) Lab PSNR = 69.80 dB (↑45.6%)	固定尺寸图像 未测量子实时性
Priyadarshini等人（2024）	DExConNet+SCCIE框架 密文ROI提取	PSNR达标率 = 92% (↑4.5%) 加密速度88% (↑3.5%)	无密钥更新 未抗量子攻击
Wang（2024）	量子混沌掩码 同态层级解密	PSNR达标率 = 90% (↑5.9%) NPCR达标率 = 99.63% (↑0.031%)	无自适应采样 静态密钥
Qi等人（2025）	格密码+同态加密 可更新密钥	NPCR达标率 = 99.627 7% (↑0.029%) PSNR = 38.52 dB（CR = 0.75） (↑7.6%)	未公开速度 仅交通图像验证

), ArticleFig(id=1322960910381363387, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表7, caption=

量子图像加密方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
李盼池和卢爱平（2016）	双随机相位加密量子化实现 完整量子线路设计	密钥空间扩大 解密错误密钥恢复PSNR < 8 dB	密钥敏感度过高 电路深度随像素数指数增长
李盼池和曹梓崎（2017a）	像素位置置乱加密算法 量子比特旋转加密算法	加密图像相关系数绝对值平均0.007 0 直方图均匀分布，安全性高	测量需多次制备态 量子电路深度
郭海儒等人（2019）	改进的GQIR模型突破尺寸限制 动态密钥生成	加密图像三基色相关系数均值 ≈ 0.005	24维旋转门难分解 随机序列占用大量存储空间
Zhang和Wang（2023）	随机起点锯齿扫描置乱 8阶魔方位级旋转	信息熵7.999 4 抗49%遮挡攻击	时间复杂度高 魔方映射存储开销大
Agrawal等人（2024）	四子图DCT稀疏化预处理 量子逻辑映射生成加密矩阵	抗高斯噪声PSNR↑至33 dB 嵌入容量↑至4 bpp（双水印）	缩放因子k影响鲁棒性 子图划分↑63%时耗
Aly（2024）	NQBID量子表示 块球面参数加密	PSNR = Inf (↑100%) Lab PSNR = 69.80 dB (↑45.6%)	固定尺寸图像 未测量子实时性
Priyadarshini等人（2024）	DExConNet+SCCIE框架 密文ROI提取	PSNR达标率 = 92% (↑4.5%) 加密速度88% (↑3.5%)	无密钥更新 未抗量子攻击
Wang（2024）	量子混沌掩码 同态层级解密	PSNR达标率 = 90% (↑5.9%) NPCR达标率 = 99.63% (↑0.031%)	无自适应采样 静态密钥
Qi等人（2025）	格密码+同态加密 可更新密钥	NPCR达标率 = 99.627 7% (↑0.029%) PSNR = 38.52 dB（CR = 0.75） (↑7.6%)	未公开速度 仅交通图像验证

), ArticleFig(id=1322960910456860860, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 8, caption=Summary of quantum image data hiding methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
李盼池和赵娅（2017）	格雷码置乱实现像素位置—颜色同步加密 小规模量子线路降低复杂度	PSNR > 51 dB（视觉质量↑） 比特重合度↑6.7%	置乱周期固定 需多次量子测量提取
李盼池和曹梓崎 （2017b）	单量子比特相位描述彩色图像 Bloch球面绕轴旋转加密	电路复杂度O(n)	相位提取需重复制备态 经典模拟依赖硬件
郭海儒等人（2018）	分块空间域LSB抗剪切 量子比特比较器定位嵌入点	PSNR ≈ 64 dB（隐蔽性↑）	预分块降低抗剪切灵活性 密钥管理复杂度高
罗佳等人（2021）	QGAN生成抗统计检测含密图像 量子—经典映射规避修改痕迹	KL散度→0（分布拟合↑）	模型训练资源密集 收敛速度慢
Gao等人（2022）	量子小波文本分块嵌入 比特翻转置乱算法	嵌入容量O(n)（效率↑） 总像素差异 < 3.65 M（视觉保真↑）	容易受到噪声影响 实时性不足
Yu等人（2023）	Haar小波自适应LSB嵌入 对角细节子带优化	PSNR > 48 dB（隐蔽性↑） SSIM > 0.997（结构相似性↑）	非盲水印需原载体提取 容量受限1/4 bpp
Mu等人（2023）	QHWT + Sobel边缘检测选平坦区 量子比较器动态选嵌入位	BER < 0.018（误码率↓） PSNR ≈ 58 dB（质量↑）	容量仅0.25 bpp 计算复杂度高
Randhawa等人（2023）	Hadamard门直编像素强度 量子随机嵌入规避检测	支持12字符/RGB层（容量↑） 叠加态加密抗量子攻击	需QRNG增强随机性 量子硬件依赖

), ArticleFig(id=1322960910523969725, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表8, caption=

量子图像数据隐藏方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
李盼池和赵娅（2017）	格雷码置乱实现像素位置—颜色同步加密 小规模量子线路降低复杂度	PSNR > 51 dB（视觉质量↑） 比特重合度↑6.7%	置乱周期固定 需多次量子测量提取
李盼池和曹梓崎 （2017b）	单量子比特相位描述彩色图像 Bloch球面绕轴旋转加密	电路复杂度O(n)	相位提取需重复制备态 经典模拟依赖硬件
郭海儒等人（2018）	分块空间域LSB抗剪切 量子比特比较器定位嵌入点	PSNR ≈ 64 dB（隐蔽性↑）	预分块降低抗剪切灵活性 密钥管理复杂度高
罗佳等人（2021）	QGAN生成抗统计检测含密图像 量子—经典映射规避修改痕迹	KL散度→0（分布拟合↑）	模型训练资源密集 收敛速度慢
Gao等人（2022）	量子小波文本分块嵌入 比特翻转置乱算法	嵌入容量O(n)（效率↑） 总像素差异 < 3.65 M（视觉保真↑）	容易受到噪声影响 实时性不足
Yu等人（2023）	Haar小波自适应LSB嵌入 对角细节子带优化	PSNR > 48 dB（隐蔽性↑） SSIM > 0.997（结构相似性↑）	非盲水印需原载体提取 容量受限1/4 bpp
Mu等人（2023）	QHWT + Sobel边缘检测选平坦区 量子比较器动态选嵌入位	BER < 0.018（误码率↓） PSNR ≈ 58 dB（质量↑）	容量仅0.25 bpp 计算复杂度高
Randhawa等人（2023）	Hadamard门直编像素强度 量子随机嵌入规避检测	支持12字符/RGB层（容量↑） 叠加态加密抗量子攻击	需QRNG增强随机性 量子硬件依赖

), ArticleFig(id=1322960910746267838, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 9, caption=Summary of QSVM methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Shaik和Periasamy（2022）	首用QSVM分类PRISMA高光谱影像 量子—经典处理对比	50样本下QSVM精度↑5% 12量子位时准确率100%	训练时间284 s（50样本） 预测400像素需5 243 s
Grossi等人（2022）	量子特征选择优化QSVM参数 混合量子—经典决策集成	混合模型准确率81% 特征选择提升分类效率	噪声下准确率波动±10% 仅支持7量子位特征
Kumar等人（2024）	ZZFeatureMap量子编码 PCA降维适配量子硬件	模拟器速度↑188倍 准确率95%（比经典高1.60%）	真实处理器仅5量子位 电路深度限制扩展性
Jose等人（2024）	ENSGA多目标优化QSVM参数 ZZ特征映射量子态空间	优化后准确率100% 执行时间0.14 s（QASM模拟器）	NISQ设备噪声敏感 电路深度受限训练复杂度
Suzuki等人（2024）	浅层量子电路减少噪声影响 缓解设备噪声	MNIST分类准确率100% 无噪声模拟回归R² = 0.932	噪声敏感，回归任务R²↓10.7% 需500次量子测量/核条目
Slabbert和Petruccione （2025）	ResNet10自动编码器降维 混合经典—量子架构处理图像输入	MNIST分类/重建准确率100% CIFAR-10精度 ≈ 75%（F1分数）	复杂图像性能低（精度↓25%） 不平衡数据影响异常检测效果
Zahid和Tahir（2025）	Tabu搜索联合优化量子门和特征选择 量子线性/RBF核模拟经典方法	Ionosphere准确率88.21% Moons数据集优于经典	简单数据集精度↓ 优化资源密集

), ArticleFig(id=1322960910821765311, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表9, caption=

QSVM方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
Shaik和Periasamy（2022）	首用QSVM分类PRISMA高光谱影像 量子—经典处理对比	50样本下QSVM精度↑5% 12量子位时准确率100%	训练时间284 s（50样本） 预测400像素需5 243 s
Grossi等人（2022）	量子特征选择优化QSVM参数 混合量子—经典决策集成	混合模型准确率81% 特征选择提升分类效率	噪声下准确率波动±10% 仅支持7量子位特征
Kumar等人（2024）	ZZFeatureMap量子编码 PCA降维适配量子硬件	模拟器速度↑188倍 准确率95%（比经典高1.60%）	真实处理器仅5量子位 电路深度限制扩展性
Jose等人（2024）	ENSGA多目标优化QSVM参数 ZZ特征映射量子态空间	优化后准确率100% 执行时间0.14 s（QASM模拟器）	NISQ设备噪声敏感 电路深度受限训练复杂度
Suzuki等人（2024）	浅层量子电路减少噪声影响 缓解设备噪声	MNIST分类准确率100% 无噪声模拟回归R² = 0.932	噪声敏感，回归任务R²↓10.7% 需500次量子测量/核条目
Slabbert和Petruccione （2025）	ResNet10自动编码器降维 混合经典—量子架构处理图像输入	MNIST分类/重建准确率100% CIFAR-10精度 ≈ 75%（F1分数）	复杂图像性能低（精度↓25%） 不平衡数据影响异常检测效果
Zahid和Tahir（2025）	Tabu搜索联合优化量子门和特征选择 量子线性/RBF核模拟经典方法	Ionosphere准确率88.21% Moons数据集优于经典	简单数据集精度↓ 优化资源密集

), ArticleFig(id=1322960910897262784, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 10, caption=Summary of QCNN methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
王兆滨和徐敏哲（2023）	量子逻辑门构建全加器/乘法器/卷积模块 双通道量子脉冲耦合降低复杂度	复杂度O(n² + d²) 图像融合互信息 > 97%	依赖QRAM制备量子态 灰度编码误差随位数增加
Amin等人（2023）	J-QCNN模型（4量子比特 + 5层） 量子—经典混合结构优化异常检测	异常检测准确率99% AUC 0.98	需预训练经典CNN 实时处理依赖硬件算力
Kim等人（2023）	经典到量子迁移学习（C2Q-CNN） 预训练CNN特征输入QCNN	MNIST分类95% + 准确率 参数量 < 100	仅支持小规模输入（8量子比特） 编码依赖振幅嵌入
Reejisha和Mohan（2023）	QNN与QCNN对比架构 4 × 4像素量子编码简化电路	QNN准确率90% 训练速度↑30%	图像需降采样 当前硬件无法支持大规模数据
Chen等人（2023a）	MERA特征提取方法 图像位平面切片与Ising相变相似性发现	抗攻击准确率87.50%～95.31%	位平面通用性依赖 需多次量子测量
Baek等人（2023）	sQCNN-3D架构 RF-Train多样化特征训练	ShapeNet准确率92.59% 扩展提升6.5%	训练资源密集 Barren plateaus风险
Song等人（2024）	振幅编码策略 QAOA电路构建量子卷积层	Fashion-MNIST准确率92.59% MNIST上TPR 89%（FPR 2%时）	非稀疏数据效率低 梯度消失风险
Roh等人（2024）	HKT整合预训练CNN CNN教师模型指导QCNN训练	KITTI AP_{50}接近CNN	依赖预训练模型 量子模拟开销大
Gong等人（2024）	混合树状结构振幅编码平衡电路深度/宽度 纯变分量子电路QCNN架构	MNIST分类准确率98.4% 超越经典CNN 2.36%	依赖PCA/自编码器预降维 8分类任务精度↓至49.4%
Das等人（2024）	置换基振幅编码调控对称群表示 反射等变QCNN结构设计	反射对称分类准确率↑7% 噪声下鲁棒性提升	特定嵌入组合性能↓ 多对称约束削弱表达力
Asaoka和Kudo （2025）	QAE编码标签信息 量子电路端到端分类机制	四分类准确率85% 参数量↓98%（vs FCNN）	8分类任务梯度误差放大 量子态幅度衰减限制通道扩展
Das和Caruso（2025）	随机追踪量子比特实现置换对称 概率化QCNN架构	图连通分类准确率100% 训练充分时超越等变QNN	通道选择需辅助量子比特 电路深度随类别数指数增长

), ArticleFig(id=1322960912549818561, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表10, caption=

QCNN方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
王兆滨和徐敏哲（2023）	量子逻辑门构建全加器/乘法器/卷积模块 双通道量子脉冲耦合降低复杂度	复杂度O(n² + d²) 图像融合互信息 > 97%	依赖QRAM制备量子态 灰度编码误差随位数增加
Amin等人（2023）	J-QCNN模型（4量子比特 + 5层） 量子—经典混合结构优化异常检测	异常检测准确率99% AUC 0.98	需预训练经典CNN 实时处理依赖硬件算力
Kim等人（2023）	经典到量子迁移学习（C2Q-CNN） 预训练CNN特征输入QCNN	MNIST分类95% + 准确率 参数量 < 100	仅支持小规模输入（8量子比特） 编码依赖振幅嵌入
Reejisha和Mohan（2023）	QNN与QCNN对比架构 4 × 4像素量子编码简化电路	QNN准确率90% 训练速度↑30%	图像需降采样 当前硬件无法支持大规模数据
Chen等人（2023a）	MERA特征提取方法 图像位平面切片与Ising相变相似性发现	抗攻击准确率87.50%～95.31%	位平面通用性依赖 需多次量子测量
Baek等人（2023）	sQCNN-3D架构 RF-Train多样化特征训练	ShapeNet准确率92.59% 扩展提升6.5%	训练资源密集 Barren plateaus风险
Song等人（2024）	振幅编码策略 QAOA电路构建量子卷积层	Fashion-MNIST准确率92.59% MNIST上TPR 89%（FPR 2%时）	非稀疏数据效率低 梯度消失风险
Roh等人（2024）	HKT整合预训练CNN CNN教师模型指导QCNN训练	KITTI AP_{50}接近CNN	依赖预训练模型 量子模拟开销大
Gong等人（2024）	混合树状结构振幅编码平衡电路深度/宽度 纯变分量子电路QCNN架构	MNIST分类准确率98.4% 超越经典CNN 2.36%	依赖PCA/自编码器预降维 8分类任务精度↓至49.4%
Das等人（2024）	置换基振幅编码调控对称群表示 反射等变QCNN结构设计	反射对称分类准确率↑7% 噪声下鲁棒性提升	特定嵌入组合性能↓ 多对称约束削弱表达力
Asaoka和Kudo （2025）	QAE编码标签信息 量子电路端到端分类机制	四分类准确率85% 参数量↓98%（vs FCNN）	8分类任务梯度误差放大 量子态幅度衰减限制通道扩展
Das和Caruso（2025）	随机追踪量子比特实现置换对称 概率化QCNN架构	图连通分类准确率100% 训练充分时超越等变QNN	通道选择需辅助量子比特 电路深度随类别数指数增长

), ArticleFig(id=1322960912629510338, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=EN, label=Table 11, caption=Summary of QGAN methods, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
刘文杰等人（2022）	one-hot形式多粒子W态编码 条件信息输入生成器/判别器稳定模型	时间复杂度O(N) (原O(N²)) BAS数据集准确率96.83%	贫瘠高原现象（电路深度↑） 参数优化依赖离线模拟
江奕达和王明明 （2024）	量子—经典混合生成器 振幅/角度编码方案优化	混合模型保真度 > 0.95 (MNIST) 重构PSNR > 20 dB	训练资源密集（参数 > 4 000） 收敛速度慢
Chang等人（2024）	自编码器潜在空间映射 量子生成器多测量策略	FID < 20 (20 epochs收敛) MNIST数据集IS > 8.0	对shot noise敏感 训练不稳定
Steck和Behrmann （2024）	量子“风格”参数化生成器 Levenberg-Marquardt量子优化	模拟分类准确率 > 90% 产品状态检测误差 < 5%	硬件训练困难 电路深度限制
Thomas等人（2025）	MERA特征提取方法 图像位平面切片与Ising相变相似性发现	JSD↓30.1% (vs PQWGAN) MNIST数据集FID改善至22.6	计算资源密集 训练时间↑50%
瞿治国等人（2025）	QBiGRU和QCNN生成器设计 MPS/TTN变分量子电路	平均分类准确率82.6% NISQ设备兼容	量子资源限制 训练过程复杂
吉家欣等人（2025）	双GAN耦合结构 权重共享约束 量子补丁生成器	MNIST图像质量↑ 领域适应分类精度75.6%	计算开销大 泛化性受限
Joon等人（2024）	量子补丁生成器架构 能量守恒物理约束	Discriminator Loss 0.50 Generator Loss 0.98	量子噪声敏感 数据集规模限制
Guntupalli等人 （2024）	量子预处理（振幅编码） 量子GAN去噪架构	PSNR值↑	计算效率↓ 高噪声敏感
Ma等人（2025）	QINR集成量子隐式表示 参数减少技术（近10倍）	量子参数↓10倍（vs PQWGAN） 生成78 × 64像素高分辨率图像	训练资源密集 （2 000 epochs） 收敛速度慢
Yang等人（2026）	可逆量子生成器参数共享 辅助CNN实现单向循环一致性约束	23个子数据集有效翻译参数规模↓	生成图像亮度低 （BPR↓~15%） 量子资源限制
Tian等人（2025）	QGAN生成平凡环路纠错路径 Transformer自注意力捕获全局依赖	解码精度99.875% 错误阈值7.5%	量子电路忽略内部噪声 训练数据需求高

), ArticleFig(id=1322960912709202115, tenantId=1146029695717560320, journalId=1249024232475115590, articleId=1322960895785185387, language=CN, label=表11, caption=

QGAN方法汇总

, figureFileSmall=null, figureFileBig=null, tableContent=

方法	核心创新	性能突破	关键局限
刘文杰等人（2022）	one-hot形式多粒子W态编码 条件信息输入生成器/判别器稳定模型	时间复杂度O(N) (原O(N²)) BAS数据集准确率96.83%	贫瘠高原现象（电路深度↑） 参数优化依赖离线模拟
江奕达和王明明 （2024）	量子—经典混合生成器 振幅/角度编码方案优化	混合模型保真度 > 0.95 (MNIST) 重构PSNR > 20 dB	训练资源密集（参数 > 4 000） 收敛速度慢
Chang等人（2024）	自编码器潜在空间映射 量子生成器多测量策略	FID < 20 (20 epochs收敛) MNIST数据集IS > 8.0	对shot noise敏感 训练不稳定
Steck和Behrmann （2024）	量子“风格”参数化生成器 Levenberg-Marquardt量子优化	模拟分类准确率 > 90% 产品状态检测误差 < 5%	硬件训练困难 电路深度限制
Thomas等人（2025）	MERA特征提取方法 图像位平面切片与Ising相变相似性发现	JSD↓30.1% (vs PQWGAN) MNIST数据集FID改善至22.6	计算资源密集 训练时间↑50%
瞿治国等人（2025）	QBiGRU和QCNN生成器设计 MPS/TTN变分量子电路	平均分类准确率82.6% NISQ设备兼容	量子资源限制 训练过程复杂
吉家欣等人（2025）	双GAN耦合结构 权重共享约束 量子补丁生成器	MNIST图像质量↑ 领域适应分类精度75.6%	计算开销大 泛化性受限
Joon等人（2024）	量子补丁生成器架构 能量守恒物理约束	Discriminator Loss 0.50 Generator Loss 0.98	量子噪声敏感 数据集规模限制
Guntupalli等人 （2024）	量子预处理（振幅编码） 量子GAN去噪架构	PSNR值↑	计算效率↓ 高噪声敏感
Ma等人（2025）	QINR集成量子隐式表示 参数减少技术（近10倍）	量子参数↓10倍（vs PQWGAN） 生成78 × 64像素高分辨率图像	训练资源密集 （2 000 epochs） 收敛速度慢
Yang等人（2026）	可逆量子生成器参数共享 辅助CNN实现单向循环一致性约束	23个子数据集有效翻译参数规模↓	生成图像亮度低 （BPR↓~15%） 量子资源限制
Tian等人（2025）	QGAN生成平凡环路纠错路径 Transformer自注意力捕获全局依赖	解码精度99.875% 错误阈值7.5%	量子电路忽略内部噪声 训练数据需求高

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