AI 中文总结
该研究针对有界云故障模型，定义可接受逻辑扰动并推导其哈尔期望，评估种子化Clifford编码器，发现重新种子化可降低逻辑扰动，量化了其完整性增益。
AI 中文摘要
云量子处理器会编译提交的量子纠错电路，并可能将其与不可信工作负载共置。固定的公开编码器为故障注入对手提供了可重复的攻击目标，而每次运行的重新种子化会改变物理到逻辑的故障映射。精确的哈尔随机编码器具有指数级电路成本，高效的随机集合提供了平均矩保证，但未刻画最坏情况下的可接受损坏。我们定义了可接受逻辑扰动，这是一种在可接受结果中对有害逻辑作用的接受加权度量，并推导了其精确的哈尔期望。我们使用稠密线性代数和门级稳定器仿真，针对在编码器已知前或后选择的故障，评估了多项式成本的种子化Clifford编码器族。重新种子化将平均可接受逻辑扰动从学习每个编码器后选择故障时的0.150，降至在编码器已知前选择一个故障时的0.020，该86.7%的减少源于拒绝。固定距离为3的[[5,1,3]]码可纠正所有测试的重量1泡利算子，而所选集合中18.5%的采样编码器满足精确量子纠错。测量到的减少量化了重新种子化带来的完整性增益，并在显式故障和攻击者知识模型下区分了后选检测与精确纠错。
英文摘要
Cloud quantum processors compile submitted quantum error correction circuits and may colocate them with untrusted workloads. A fixed public encoder gives a fault-injection adversary a reusable target. Per-run reseeding changes the physical-to-logical fault map. Exact Haar-random encoders have exponential circuit cost. Efficient random ensembles provide average-moment guarantees and leave worst-case accepted corruption uncharacterized. We define accepted logical disturbance, an acceptance-weighted measure of harmful logical action in accepted results, and derive its exact Haar expectation. We evaluate a polynomial-cost seeded Clifford encoder family using dense linear algebra and gate-level stabilizer simulation against faults chosen before or after the encoder is known. Reseeding reduces mean accepted logical disturbance from 0.150 for faults chosen after learning each encoder to 0.020 for one fault chosen before it is known. The 86.7% reduction results from rejection. The fixed distance-three \([[5,1,3]]\) code corrects every tested weight-one Pauli, while 18.5% of sampled encoders in the selected ensemble satisfy exact quantum error correction. The measured reduction quantifies the integrity gain from reseeding and separates postselected detection from exact correction under explicit fault and attacker-knowledge models.
