标题： 硬件噪声读出的动力学编码 显示英文标题

标题： Dynamical codes for hardware with noisy readouts

摘要： 动态稳定器码可能为大规模量子计算提供一条实用的路径。 这类码由一个错误检测测量的调度定义，这允许在构建时具有灵活性。 在本工作中，我们询问如何在各种噪声偏置极限下最优地优化动态压缩颜色码的测量调度。 我们特别关注测量引入的噪声比单元操作和空闲操作更多的情况——这是一种与某些硬件方案相关的噪声模型。 对于测量偏置的噪声模型，我们通过在调度中战略性地重复测量来提高码的性能。 对于无偏或$Z$偏置的噪声模型，我们发现重复测量带来的改进很小——这与我们的预期有些相反——并研究了原因。 为了进行此分析，我们将一个称为 teraquop 足迹的度量推广到 teraquop 体积。 这是所需量子比特数量和测量轮数的乘积，使得时空逻辑错误发生的概率小于$10^{-12}$。 在大多数情况下，我们发现性能差异主要取决于所需的测量轮数，而不是量子比特数量——强调了在分析中使用 teraquop 体积的重要性。 此外，我们的结果提供了另一个例子，说明在解码时利用相关错误的重要性，即使用信念匹配而不是最小权重完美匹配可以将给定噪声模型下的最差性能码转变为最佳性能码。 显示英文摘要

摘要： Dynamical stabilizer codes may offer a practical route to large-scale quantum computation. Such codes are defined by a schedule of error-detecting measurements, which allows for flexibility in their construction. In this work, we ask how best to optimise the measurement schedule of dynamically condensed colour codes in various limits of noise bias. We take a particular focus on the setting where measurements introduce more noise than unitary and idling operations - a noise model relevant to some hardware proposals. For measurement-biased noise models, we improve code performance by strategically repeating measurements within the schedule. For unbiased or $Z$-biased noise models, we find repeating measurements offers little improvement - somewhat contrary to our expectations - and investigate why this is. To perform this analysis, we generalise a metric called the teraquop footprint to the teraquop volume. This is the product of the number of qubits and number of rounds of measurements required such that the probability of a spacelike or timelike logical error occurring is less than $10^{-12}$. In most cases, we find differences in performance are primarily due to the number of rounds of measurements required, rather than the number of qubits - emphasising the importance of using the teraquop volume in the analysis. Additionally, our results provide another example of the importance of making use of correlated errors when decoding, in that using belief matching rather than minimum-weight perfect matching can turn a worst-performing code under a given noise model into a best-performing code.