发表机构
Purdue University; Columbia University; Data Science Institute, Columbia University（普渡大学; 哥伦比亚大学; 哥伦比亚大学数据科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于Willow处理器验证量子纠错解码器的仿真到真实排名假设，评估六款解码器及NVIDIA的Ising预解码器，发布完整评估流程与单次测量结果供后续研究使用。
AI 中文摘要
量子纠错解码器通常在合成的电路级噪声下进行基准测试，假设该噪声下解码器的排名会迁移到硬件上，且随着噪声模型变得更贴合实际，排名会更准确。首个工作在表面码阈值以下的Willow处理器让我们得以验证这一假设。我们使用包含四个层级、保真度递增的噪声模型对六款解码器进行排名，评估覆盖三个码距、两种基矢及十五个循环次数的真实数据。当噪声模型为每种操作类型分配独立错误率时，其与硬件的排名一致性才会显现。将模型校准到设备可提升绝对错误率，但无法提升排名一致性。我们还首次在硬件上对NVIDIA的Ising预解码器进行独立评估，评估场景为码距低于其训练感受野，并通过映射到其训练所用晶格实现。在这些条件下，该解码器无准确率-延迟优势：在280次评估中，有278次另一款面板解码器在每周期错误率和解码延迟上与其相当或更优。我们发布了完整流程及每次评估的单次测量结果，以便未来可对解码器和设备进行比较。
英文摘要
Quantum error-correction decoders are typically benchmarked against synthetic circuit-level noise, under the assumption that a decoder's ranking under such noise transfers to hardware and improves as the noise model becomes more realistic. The Willow processor, the first to operate below the surface-code threshold, allows us to test this assumption. We rank a panel of six decoders using a four-rung ladder of noise models with increasing fidelity, evaluated against real data across three code distances, two bases, and fifteen round counts. Rank agreement with hardware appears once the noise model gives each operation type its own error rate. Calibrating the model to the device improves absolute error rates but not rank agreement. We additionally provide the first independent evaluation of NVIDIA's Ising pre-decoder on hardware, at code distances below its training receptive field and via a mapping onto the lattice on which it was trained. Under these conditions, it holds no accuracy-latency advantage: another panel decoder matches or improves on it in both per-cycle error rate and decode latency in 278 of the 280 evaluations. We release the full pipeline and the per-shot outcome of every evaluation, so future decoders and devices can be compared.
Comments11 pages, 8 figures
