无需测量的盲重置可将量子纠错循环延迟降低38倍，适合超导与离子阱平台。
Measurement-Free Ancilla Recycling via Blind Reset: A Cross-Platform Study on Superconducting and Trapped-Ion Processors
- 采用无测量的盲重置策略，通过重复缩放序列实现辅助量子比特复用。
- 在保持量子态清洁度不低于0.86的前提下，循环延迟最多缩短38倍。
- 适用于超导与离子阱系统，提供基于硬件特性的部署决策支持。
在重复校验子提取中，辅助量子比特复用将重置质量与逻辑循环延迟耦合。我们评估了盲重置——仅通过缩放序列重播实现的无测量复用——在IQM Garnet、Rigetti Ankaa-3和IonQ上的表现，使用匹配种子、序列长度和采样预算。利用辅助量子比特清洁度F_clean=P(|0>)、每循环延迟以及距离3的重复码逻辑错误代理，平台校准模拟识别出候选区域：在类NVQLink反馈开销下，循环延迟最多降低38倍，且在L ≤ 6时维持F_clean ≥ 0.86。IQM Garnet的硬件实验验证了在L=8（1024次采样，种子42）时清洁度≥0.84；Rigetti Ankaa-3的平台校准模拟预测性能相当。不同架构的交叉长度分别为L* ~ 12（IQM）、~11（Rigetti）、~1（IonQ）及~78（GPU链接外部反馈）。两项附加分析收紧了部署边界：T1/T2敏感性图识别出相干比区间，误差边界验证确认实测清洁度与预测诊断包络一致。部署决策矩阵将结果转化为后端特定策略选择。
原文摘要 · Abstract (English)
Ancilla reuse in repeated syndrome extraction couples reset quality to logical-cycle latency. We evaluate blind reset -- unitary-only recycling via scaled sequence replay -- on IQM Garnet, Rigetti Ankaa-3, and IonQ under matched seeds, sequence lengths, and shot budgets. Using ancilla cleanliness F_clean=P(|0>), per-cycle latency, and a distance-3 repetition-code logical-error proxy, platform-calibrated simulation identifies candidate regions where blind reset cuts cycle latency by up to 38x under NVQLink-class feedback overhead while maintaining F_clean >= 0.86 for L <= 6. Hardware experiments on IQM Garnet confirm blind-reset cleanliness >= 0.84 at L=8 (1024 shots, seed 42); platform-calibrated simulation for Rigetti Ankaa-3 predicts comparable performance. Architecture-dependent crossover lengths are L* ~ 12 (IQM), ~ 11 (Rigetti), ~ 1 (IonQ), and ~ 78 with GPU-linked external feedback. Two added analyses tighten deployment boundaries: a T1/T2 sensitivity map identifies coherence-ratio regimes, and error-bound validation confirms measured cleanliness remains consistent with the predicted diagnostic envelope. A deployment decision matrix translates these results into backend-specific policy selection.
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