发表机构
German Aerospace Center (DLR)（德国航空航天中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对量子优化中比特翻转与相位翻转噪声影响不对称的现象，提出利用非对称量子纠错码（如QPC）高效分配纠错资源，在对称噪声下也能提升性能。
AI 中文摘要
噪声是当前量子计算机面临的主要挑战。噪声大致可分为比特翻转和相位翻转两类错误。这两类错误对执行的算法（从而对应用）的影响不一定相同。我们以量子近似优化算法（QAOA）应用于飞行门分配（FGA）问题的小规模实例为例，阐述这一普遍效应。我们在层级和门级噪声模型下，使用同一理想QAOA酉的两种电路分解（基于CNOT的分解和基于原生$R_{ZZ}$的分解）比较比特翻转和相位翻转的泡利噪声。在模拟中，比特翻转噪声对量子优化性能的退化影响更大。这种不对称性在层级和原生$R_{ZZ}$模拟中最为明显。我们通过错误如何影响混合、最终测量以及它们在电路内部的传播来解释这一点。然后，我们利用这些见解，使用非对称纠错码特别高效地处理噪声。作为示例，我们使用量子奇偶校验码（QPC），它是9量子比特肖尔码的推广，并展示较小的非对称码可以达到与较大的对称选择几乎相同的改进。这表明纠错资源的分配不仅应根据物理错误率，还应根据每个错误通道对应用的影响强度。因此，即使在噪声模型对称的情况下，非对称量子纠错也被证明是有用的。最后，我们讨论了如何利用通过校准获得的噪声信息在我们的方法中发挥作用。
英文摘要
Noise is a major challenge for current quantum computers. It can be broadly categorized into bit-flip and phase-flip errors. These two types do not necessarily affect the executed algorithm, thus also the application, in the same way. We illustrate this general effect for the example of the quantum approximate optimization algorithm (QAOA) applied to a small instance of the flight-gate assignment (FGA) problem. We compare bit-flip and phase-flip Pauli noise under both layer-level and gate-level noise models, using two circuit decompositions of the same ideal QAOA unitary: a CNOT-based decomposition and a native-$R_{ZZ}$ decomposition. In the simulations, bit-flip noise produces the larger degradation in the performance of the quantum optimization. The asymmetry is most visible in the layer-level and native-$R_{ZZ}$ simulations. We explain this by how the errors affect mixing, final measurements, and how they propagate inside the circuit. We then exploit these insights to tackle noise particularly efficiently using asymmetric error-correcting codes. As an illustration, we use the quantum parity code (QPC), a generalization of the 9-qubit Shor code, and show that a smaller asymmetric code can achieve nearly the same improvement as a larger symmetric choice. This demonstrates that error-correction resources should be assigned not only according to physical error rates, but also according to how strongly each error channel affects the application. As a result, asymmetric quantum error correction proves useful even in cases where the noise model is symmetric. Finally, we discuss how information about the noise obtained through calibration can be exploited in our approach.
Comments20 pages, 9 figures, 1 table
