只处理实际错误的稀疏量子纠错解码器，速度提升超400倍。
Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromes
- 聚焦缺陷事件，仅处理活跃检测点，复杂度降为O(k)
- 在多种噪声下逻辑错误率降低最多49%，速度比现有方法快95-467倍
- 适用于真实量子硬件，常数级延迟，适合实时纠错
量子纠错（QEC）是构建容错量子计算机的关键，要求解码器兼具高精度、高速和可扩展性。现有最先进神经解码器虽精度高，但无论实际错误率如何，均需处理大小为O(d²R)的密集奇偶校验阵列，其中d为码距，R为测量轮数。在物理相关错误率（p ~ 0.1%）下，少于5%的奇偶校验条目包含活跃检测事件，但现有方法仍处理整个奇偶校验体。本文提出稀疏Mamba解码器（SMD），一种以缺陷为中心的神经解码器，仅对k个活跃检测事件进行处理，每个缺陷使用13维特征表示，并采用Mamba状态空间主干网络，实现O(k)复杂度。在去极化、均匀电路级、SI1000及谷歌Sycamore实验基准测试中，SMD在d ≤ 5时将MWPM逻辑错误率降低最多49%（基于SI1000噪声），运行速度比Tesseract近似最大似然解码器快95–467倍，比信念匹配快232–463倍，且在均匀电路级噪声下，码距d=3–9时延迟保持稳定（24–57微秒）。在Sycamore实验数据集上，SMD集成模型表现与Varbanov等人提出的密集Mamba解码器相当或略优。所有结果均在通用NVIDIA GPU（750万–1600万参数）上完成，无需专用加速器。
原文摘要 · Abstract (English)
Quantum error correction (QEC) is essential for building fault-tolerant quantum computers, requiring decoders that are simultaneously accurate, fast, and scalable. Most state-of-the-art neural decoders achieve high accuracy but process the full dense syndrome array of size $O(d^2 R) $regardless of the actual error rate, where d is the code distance and R is the number of measurement rounds. At physically relevant error rates (p ~ 0.1%), fewer than 5% of syndrome entries contain active detection events -- yet existing decoders process the entire syndrome volume. We introduce the Sparse Mamba Decoder (SMD), a defect-centric neural decoder that processes only the k active detection events using a 13-dimensional feature representation per defect and a Mamba state-space backbone, achieving $O(k)$ complexity. Across depolarizing, uniform circuit-level, SI1000, and Google Sycamore experimental benchmarks, SMD reduces the MWPM logical error rate by up to 49% at $d \le 5$ under SI1000 noise, runs 95-467x faster than the Tesseract near-MLD decoder and 232-463x faster than Belief Matching, and maintains nearly constant latency (24-57 us) across d = 3-9 under uniform circuit-level noise. On the Sycamore experimental dataset, the SMD ensemble matches or slightly surpasses the dense Mamba decoder of Varbanov et al. All results are obtained on commodity NVIDIA GPUs with 7.5-16M parameters, without specialized accelerators.
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