通过提前启动聚类降低量子纠错中的解码延迟
Reducing Decoding Latency in Quantum Error Correction by Early Starting Clustering
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中文总结 AI 辅助
提出CAYG解码器，在稳定子测量期间提前聚类纠错，缩短解码延迟，虽略降精度但提升速度-精度权衡，适用于表面码等量子纠错码。
中文摘要 AI 辅助
在量子纠错中，快速低延迟的解码对于容错量子计算至关重要，因为处理综合征数据的延迟可能导致积压问题。现有的解码器，包括诸如Union-Find等可并行化的方法，仅在接收到一个纠错周期中所有稳定子测量结果后才开始解码，这固有地在解码开始前引入了延迟。我们引入了Cluster-As-You-Go（CAYG），这是对Union-Find解码器的一种修改，它在稳定子测量过程中处理综合征信息，通过聚类并在错误出现时进行纠正。这种方法减小了量子纠错周期结束时剩余解码问题的规模，从而缩短了完成解码过程所需的时间。虽然这种提前启动聚类会导致解码精度略有降低，但它保持了解码器的可扩展性。表面码模拟表明，由此产生的测量后空闲时间减少可以超过精度损失，从而获得改进的速度-精度权衡。这些结果表明，在量子纠错周期中实时、提前启动解码既是可行的，而且对量子纠错是有利的。这里针对表面码进行了演示，CAYG广泛适用于其他允许基于聚类的解码方法的量子纠错码，并可扩展到专用的实时解码硬件。
英文摘要
In quantum error correction, fast low-latency decoding is essential for fault-tolerant quantum computation, as delays in processing syndrome data can lead to the backlog problem. Existing decoders, including parallelizable approaches such as Union-Find, begin decoding only after all stabilizer measurement outcomes from an error-correction cycle have been received, inherently introducing a delay before decoding begins. We introduce Cluster-As-You-Go (CAYG), a modification of the Union-Find decoder that processes syndrome information during stabilizer measurements by clustering and correcting errors as they appear. This approach reduces the size of the remaining decoding problem at the end of the quantum error correction cycle and, consequently, the time required to complete the decoding process. While this early start of clustering incurs a modest reduction in decoding accuracy, it preserves the decoder's scalability. Surface-code simulations show that the resulting reduction in post-measurement idling can outweigh the accuracy loss, yielding an improved speed-accuracy trade-off. These results demonstrate that real-time, early-starting decoding during QEC cycles is both feasible and can be advantageous for quantum error correction. Demonstrated here for the surface code, CAYG is broadly applicable to other quantum error-correcting codes, which allow for clustering-based decoding approaches, and is extensible to dedicated real-time decoding hardware.
发表机构
- University of Basel（巴塞尔大学）
- Forschungszentrum Jülich（于利希研究中心）
- RWTH Aachen University（亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。
