StreamingQEC：通过认证递归在紧密集成的量子-经典系统中实现流量子纠错
TandemQEC: Joint Provisioning of Streaming Quantum Error Correction in Tightly-Integrated Quantum-Classical Systems
- Fordham University（福特汉姆大学）
- Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究容错量子计算中工作负载在多资源间的竞争排队问题，提出StreamingQEC系统级模拟器，通过显式离散事件模拟等方法，能将逻辑工作负载转换为流QEC管道，实现快速近似设计空间探索，有效再现模拟度量并加速模拟。
AI中文摘要：
容错量子计算需要一个连续的混合量子纠错（QEC）管道，包括测量读出、症候传输、解码、反馈和控制。现有QEC模拟器主要评估电路、噪声模型、解码器和协议级结果。然而，系统架构师还必须了解在受保护的逻辑执行期间，这些工作负载如何在控制器、计算、加速器和通信资源之间竞争和排队。我们引入了StreamingQEC，这是一个系统级模拟器，它将容错逻辑工作负载转换为资源受限的流QEC管道。显式离散事件模拟提供参考执行语义。自动分级流体模式实现更快的近似设计空间探索，而认证递归机制仅在重复转换的调度状态和度量贡献与显式执行跟踪匹配时压缩重复转换。我们组装了一个包含9998次测量的解码器运行时数据集，其中8174次用于拟合性能配置文件。递归在35个校准配置文件配置以及其他工作负载和节奏验证案例中再现了报告的显式模拟度量。对于16个作业的锚定工作负载，它保留了59,7,43,936次解码事件，同时实现了24.0倍的主机端加速，并且递归模拟扩展到超过12.2亿个事件。在17个参考配置中，自动分级流体模式的平均完工时间误差为2.60%，最坏情况误差为6.45%。设计空间研究揭示了传输受限的资源匹配、解码器驱动的管道停顿以及在微秒级QEC周期下专用资源的饱和。
英文摘要：
Fault-tolerant quantum applications require timely classical decoding and feedback. Component benchmarks leave open how contention across processors, links, and finite buffers affects application progress. We propose TandemQEC, a system-level simulator connecting applications, code-specific quantum error correction schedules, and physical modalities. It models dependencies, resource availability, and buffer occupancy to explain joint provisioning decisions. We validate TandemQEC using analytical and CUDA-Q Logical references and measured pipelines. Across 960 repeated GPU/CPU runs using CUDA-Q Realtime, median absolute relative errors are 1.06% for median latency and 1.74% for 95th-percentile latency. Separate CPU-window validation covers PyMatching and BP-OSD across five code families. Studies span superconducting, trapped-ion, and neutral-atom modalities. At 32 concurrent jobs, increasing decoder capacity and bandwidth by 8x together achieves a 4.17x speedup, while either upgrade alone reduces runtime by at most 2.1%. 4x faster syndrome extraction even raises runtime under fixedduration protection due to generating more classical work. Joint upgrades recover reference performance. These findings show why component improvements require balanced provisioning to benefit applications. TandemQEC identifies useful resource combinations before complete systems are available.
