解码器模型兼容性在漂移和相关量子噪声下提供超出逻辑间隙的信息
Decoder Model Compatibility Provides Information beyond the Logical Gap under Drifting and Correlated Quantum Noise
- University of Washington（华盛顿大学）
- Pacific Northwest National Laboratory（太平洋西北国家实验室）
- Fordham University（福特汉姆大学）
- Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
- NASA Ames Research Center（美国宇航局艾姆斯研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
利用解码器模型与硬件噪声不匹配产生的综合征惊讶度进行似然感知后选择，在IBM和Google量子硬件上显著降低保留逻辑错误概率并提升魔法态产率。
AI中文摘要：
量子纠错解码器为单个测量记录的逻辑错误风险提供修正和置信度估计。这种置信度可以指导后选择，但它依赖于解码器噪声模型的准确性。当物理设备偏离该模型时，置信度度量（如逻辑间隙）可能变得校准不良。我们表明，这种不匹配本身可以提供有用的信息。在IBM量子硬件上，综合征统计在连续采集窗口之间漂移，由此产生的探测器历史与用于解码的静态探测器错误模型预测的结果显著不同。我们在独立的Google表面码存储器数据中观察到类似的硬件-模型不匹配。我们通过为在假设模型下不太可能的探测器历史分配额外的拒绝权重来利用这一效应。对于最小权重完美匹配（MWPM），最小权重$W$提供了综合征惊讶度的廉价代理。在留出的IBM重复码存储器上，似然感知后选择在$d=7$时保留的逻辑错误概率（LEP）降低了$10.6\%$，在$d=9$时降低了$28.8\%$，两者均在$15\%$拒绝率下。在Google十轮$d=5$ XZZX表面码存储器数据上，它在$90\%$拒绝率下将保留的LEP降低了$75.4\%$。应用于Google的Tesseract解码器魔法态培养实验时，它在$30\%$额外嫁接阶段拒绝率下将层析保真度误差降低了$34.7\%$，或在目标保真度$97.0\%$下将接受产率提高了$30.4\%$。突发噪声模拟表明，随着码距离和综合征提取体积的增加，该优势持续存在。该方法重用逻辑间隙解码期间已产生的量，仅需要额外的拟合步骤。
英文摘要：
Quantum error-correcting decoders provide both corrections and confidence estimates for the logical-error risk of individual measurement records. Such confidence can guide postselection, but it depends on the accuracy of the decoder noise model. When the physical device drifts away from that model, confidence measures such as the logical gap can become miscalibrated. We show that this mismatch can itself provide useful information. On IBM quantum hardware, syndrome statistics drift across consecutive acquisition windows, and the resulting detector histories differ substantially from those predicted by the stationary detector error model used for decoding. We observe a similar hardware-model mismatch in independent Google surface-code memory data. We exploit this effect by assigning additional rejection weight to detector histories that are unlikely under the assumed model. For minimum-weight perfect matching (MWPM), the minimum weight $W$ provides an inexpensive proxy for syndrome surprisal. On held-out IBM repetition-code memories, likelihood-aware postselection reduces the retained logical-error probability (LEP) by $10.6\%$ at $d=7$ and $28.8\%$ at $d=9$, both at $15\%$ rejection. On Google ten-round $d=5$ XZZX surface-code memory data, it reduces retained LEP by $75.4\%$ at $90\%$ rejection. Applied to Google's magic-state cultivation experiment with the Tesseract decoder, it reduces tomography infidelity by $34.7\%$ with $30\%$ additional grafting-stage rejection, or increases accepted yield by $30.4\%$ at a target fidelity of $97.0\%$. Burst-noise simulations show that the advantage persists as code distance and syndrome-extraction volume increase. The method reuses quantities already produced during logical-gap decoding and requires only an additional fitting step.
