通过最优症候测量时序实现量子存储器中指数级的逻辑错误降低
Exponential logical-error reduction in quantum memories via optimal syndrome-measurement timing
浏览论文内容
中文总结 AI 辅助
该研究针对量子存储器症候测量的最优时序问题，提出相关模型与自适应策略，可使逻辑错误率指数级降低，经模拟与实验参数验证，单位时间逻辑错误率最高降40%。
中文摘要 AI 辅助
症候测量的时序通常被视为量子纠错码的固定时钟周期。然而对于量子存储器而言，测量间隔本身是可优化的控制参数：测量过于稀疏会导致空闲错误累积，而测量过于频繁则会引入测量诱导的错误。我们针对该权衡提出了现象学逻辑噪声模型，并解析证明最优症候测量间隔与码距成反比，这会使逻辑错误率相对于固定间隔调度产生与码距相关的指数级降低。此外，针对随时间变化的空闲噪声，我们开发了基于测得症候活动的自适应时序策略，其性能优于所有固定间隔协议，在短而强的噪声突发场景下增益最大。对带匹配解码的旋转表面码存储器的模拟验证了该现象学模型、与码距相关的最优值以及自适应策略的改进效果。而且，利用Google在《自然》638卷（2025年）报道的实验噪声参数，我们的模型预测单位时间内逻辑错误率可降低多达40%。
英文摘要
Syndrome-measurement timing is usually treated as a fixed clock cycle of a quantum error-correcting code. For quantum memories, however, the inter-round interval is itself an optimizable control parameter: measuring too rarely allows idling errors to accumulate, whereas measuring too often introduces measurement-induced faults. We propose a phenomenological logical-noise model for this trade-off and analytically show that the optimal syndrome-measurement interval is inversely proportional to the code distance and that this produces an exponential reduction of logical-error rates in the distance relative to constant-interval schedules. Furthermore, for time-dependent idling noise, we develop an adaptive timing strategy based on the measured syndrome activity that outperforms every fixed-interval protocol, with largest gains for short but strong noise bursts. Simulations of rotated surface-code memories with matching decoding validate the phenomenological model, the distance-dependent optimum, and the adaptive-strategy improvement. Moreover, with the experimental noise parameters reported by Google in Nature 638 (2025), our model predicts reductions in logical-error rates per unit time of up to $40\%$.
发表机构
- Technology Innovation Institute（技术创新研究所）
- Joint Center for Quantum Information and Computer Science, NIST/University of Maryland（量子信息与计算机科学联合中心，美国国家标准与技术研究院/马里兰大学）
- Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland（马里兰大学计算机科学与高级计算机研究所）
机构由 AI 辅助整理，请以论文原文为准。
