不要让CNOT门压倒解码器：为快速容错量子计算（FTQC）调度横向门
Do Not Let CNOTs Overwhelm the Decoder: Scheduling Transversal Gates for Fast FTQC
AI总结：
针对TCNOT调度导致解码过载的问题，提出PACE框架，通过混合窗口解码等技术优化TCNOT调度，平衡量子加速与解码成本，揭示当前解码系统的局限性。
AI中文摘要：
横向CNOT（TCNOT）门可通过将逻辑操作之间的 syndrome 提取轮数从$O(d)$减少到$O(1)$，加速表面码中的容错量子计算（FTQC），这对具有长程连接的量子平台（如中性原子）特别有吸引力。然而，密集的TCNOT调度会大幅增加经典解码工作量：TCNOT会在多个表面码块之间传播错误，扩大必须联合解码的时空区域，因此更密集的TCNOT调度会增加解码延迟和内存需求，甚至可能超出可用解码器容量。此外，由于每个解码窗口的检测器错误模型（DEM）取决于TCNOT调度，穷尽预计算所有可能的窗口级DEM不可行，需要即时（JIT）编译DEM。因此，TCNOT门的实际优势不仅受量子硬件性能限制，还受经典解码和DEM编译能力限制。我们提出PACE，一种用于基于TCNOT的FTQC的感知解码器的调度框架。PACE通过三种互补技术缓解激进TCNOT调度的解码器侧成本：混合窗口解码根据每个解码窗口的DEM结构为其分配不同的解码器；DEM拼接通过组装可重复使用的预编译片段即时生成特定调度的窗口级DEM；子窗口并行解码将大窗口分解为更小的子窗口，采用图着色公式。基于这些技术，PACE执行感知解码器的调度，以在可用解码器资源内最大化TCNOT的并发性。我们的评估显示了量子加速与经典解码成本之间的权衡，揭示了当前解码系统对基于TCNOT的FTQC的局限性。
英文摘要：
Transversal CNOT (TCNOT) gates can accelerate fault-tolerant quantum computation (FTQC) in the surface code by reducing the number of syndrome extraction rounds required between logical operations from $O(d)$ to $O(1)$. This is particularly attractive for quantum platforms with long-range connectivity, such as neutral atoms. However, dense TCNOT schedules substantially increase the classical decoding workload. TCNOTs propagate errors across multiple surface-code patches, enlarging the spatiotemporal region that must be decoded jointly. Consequently, denser TCNOT schedules increase decoding latency and memory requirements and potentially exceed available decoder capacity. Moreover, because the detector error model (DEM) of each decoding window depends on the TCNOT schedule, exhaustively precomputing all possible window-level DEMs is infeasible, requiring just-in-time (JIT) DEM compilation. Thus, the practical benefit of TCNOT gates is limited not only by quantum hardware performance but also by classical decoding and DEM-compilation capacity. We introduce PACE, a decoder-aware scheduling framework for TCNOT-based FTQC. PACE first mitigates the decoder-side costs of aggressive TCNOT scheduling through three complementary techniques. Hybrid Window Decoding assigns different decoders for each decoding window according to its DEM structure. DEM Stitch generates schedule-specific window-level DEMs just in time by assembling reusable precompiled fragments. Sub-window Parallel Decoding decomposes large windows into smaller sub-windows with graph-coloring formulation. Building on these techniques, PACE then performs decoder-aware scheduling to maximize TCNOT concurrency within the available decoder resources. Our evaluation shows the trade-off between quantum acceleration and classical decoding cost, revealing the limitations of current decoding systems for TCNOT-based FTQC.
