AI 中文总结
本研究针对超导量子比特的两能级系统缺陷，开发两种互补自动化分析流程，经实验验证可实现缺陷的有效表征，为大规模超导量子处理器缺陷工程提供统计基线。
AI 中文摘要
微观两能级系统（TLS）缺陷仍是超导Transmon量子比特退相干与操作不稳定的主要机制，因此需要可扩展的自动化方法对其进行表征。本文提出并基准测试了两种互补的分析流程，用于直接从时间分辨SWAP光谱中提取TLS统计数据：一是一维衰减率拟合（1D-DRF），通过量子比特弛豫率的局域增强来检测缺陷；二是确定性非参数计算机视觉框架（2D-CV），利用相干布居抑制的时间持续性实现二维光谱定位。我们将两种方法应用于Rigetti处理器上52个磁通可调Transmon量子比特的SWAP光谱测量，这些量子比特经过或未经过中等程度（约10%）的交替偏置辅助退火（ABAA）后的制备后频率修整。结果显示，两种流程对缺陷环境的全局表征一致，且在不同耦合区间呈现互补敏感性；关键的是，两种方法均独立揭示了中等退火下的计数-损耗解耦：可检测的总TLS缺陷密度在统计上保持不变，但跨区域积分的介电损耗降低了约2倍，表明对最具耗散性的缺陷通道进行了选择性抑制。这些结果为高通量硬件诊断建立了自动化非参数分析框架，并为大规模超导量子处理器的制备后缺陷工程提供了统计基线。
英文摘要
Microscopic two-level system (TLS) defects remain a primary mechanism of decoherence and operational instability in superconducting transmon qubits, necessitating scalable and automated methods for their characterization. Here, we present and benchmark two complementary analysis pipelines for extracting TLS statistics directly from time-resolved SWAP spectroscopy: one-dimensional decay-rate fitting (1D-DRF), which detects defects via localized enhancements in the qubit relaxation rate, and a deterministic, non-parametric computer-vision framework (2D-CV) that achieves two-dimensional spectral localization by exploiting the temporal persistence of coherent population suppression. We deploy both methods on SWAP spectroscopy measurements from 52 flux-tunable transmon qubits on Rigetti processors with and without moderate ($\sim 10\%$) post-fabrication frequency trimming via Alternating-Bias Assisted Annealing (ABAA). We show that both pipelines converge on a consistent global characterization of the defect landscape while exhibiting complementary sensitivity across distinct coupling regimes. Crucially, both methods independently reveal a count--loss decoupling under moderate annealing: while the total detectable TLS defect density remains statistically unchanged, the span-integrated dielectric loss is reduced by approximately a factor of two, demonstrating selective suppression of the most strongly dissipative defect channels. These results establish an automated, non-parametric analysis framework for high-throughput hardware diagnostics and provide a statistical baseline for post-fabrication defect engineering in large-scale superconducting quantum processors.
Comments14 pages, 11 figures
