发表机构
Department of Environmental Protection and Occupational Safety Technologies（环境保护与职业安全技术系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在IBM量子硬件上四量子比特ZZ量子内核态矢参考几何结构的存活情况，通过三种执行配置进行固定子集诊断，测量Gram矩阵及态矢几何结构保留程度，发现门旋转最忠实，虽有硬件提升但保真度与标签对齐相反，强调硬件量子机器学习研究应兼顾实现保真度和任务相关性。
AI 中文摘要
量子内核方法将数据集的几何结构编码在Gram矩阵中，因此关于硬件内核的学习断言假定预期的几何结构在执行后仍然存在。我们测量了一个冻结的四量子比特ZZ特征映射内核在N = 24个真实室内空气质量窗口上的这种存活情况，该内核在ibm_fez上重建（每个电路1024次测量），分别在基线、仅动态解耦和仅门旋转这三种配置下，每种配置都是单个非交错作业。每种配置都返回了一个完整、有限、正定的Gram矩阵，并在很大但不完整的描述程度上保留了中心态矢几何结构（全矩阵中心内核对齐，CKA，0.933 - 0.989）。门旋转在每个报告的几何轴上最忠实，相对于基线有唯一通过留一法解析的改进（持续的斯皮尔曼、平均绝对误差和全矩阵CKA诊断）；在冻结窗口尺度上，仅动态解耦与基线没有区别。残余硬件失真而非有限采样主导了差异。然而，保真度和标签对齐是相反的：最忠实的配置具有最低的中心内核 - 目标对齐，对于态矢和硬件而言，该对齐处于或低于标签置换参考。我们将硬件上的微小提升视为非仿射失真的归一化属性，而非捕获的信号。这些是关于一个后端上单个作业的数据结果，而非因果缓解效果估计；未提出量子优势、硬件分类器优越性或预测断言。实现保真度和任务相关性是不同的轴；硬件量子机器学习研究应同时报告两者。
英文摘要
Hardware noise and finite sampling perturb the fidelity estimates forming a quantum-kernel Gram matrix. We measured how far three hardware-reconstructed Gram matrices depart from an exact statevector reference for one frozen four-qubit ZZ feature map on N=24 indoor air-quality windows, executed on ibm_fez at 1024 shots per circuit in three single, non-interleaved jobs: baseline, dynamical decoupling alone, and gate twirling alone. All were complete, finite, and positive-semidefinite. Off-diagonal root-mean-squared error (RMSE) against the reference was 0.0878, 0.0864, and 0.0427; full-matrix centered kernel alignment (CKA) ranged 0.933-0.989 and the post hoc diagonal-excluded (U-centered) CKA 0.816-0.986. The gate-twirled job deviated least on every reported geometry axis; its baseline contrasts are deletion-stable for the Spearman, mean-absolute-error, RMSE, and full-matrix CKA diagnostics, while the Pearson and diagonal-excluded contrasts fall just below that convention. Dynamical decoupling was not separated from the baseline. The observed error exceeded both finite-shot reference scales, so, under those sampling-only models, sampling does not explain it. Centered kernel-target alignment did not track reconstruction fidelity and stayed at or below each label-permutation reference: implementation fidelity and task relevance are distinct diagnostic axes. All configuration-level statements describe three realized jobs on one backend; no mitigation-efficacy, classifier-superiority, forecasting, or quantum-advantage claim is made.
Comments14 pages, 2 figures. v2: retitled and restructured after journal peer review as a single-backend, fixed-kernel, three-job diagnostic case study; main text shortened by about 75%; claims restricted to the three observed jobs; ancillary files contain the Supplementary Methods and Supplementary Notes S1-S3 (four PDFs). Code: https://doi.org/10.5281/zenodo.21438523
Journal refQuantum Reports 8(3), 93 (2026)
