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基于高性能计算的量子纠错码实时解码

Real-time decoding of quantum error correction codes using high-performance computing

Lingling Lao, Qiang Wang, Yuanqi Liu, Yantong Liu, Haowen Wang, Yitao Chen, Yankang Zhao, Zhenwei Wu, Wei Zhang, Yong Dong, Yingwen Liu, Mingche Lai, Junjie Wu

arXiv 2608.03948首次发表:更新:

AI 中文总结

本研究提出基于高性能计算的THQLink架构,实现表面码等量子纠错码的实时解码,平均往返延迟2.944μs,可支持量子中心超级计算机的混合量子-经典算法等工作负载。

AI 中文摘要

量子纠错(QEC)是构建可扩展容错量子计算机不可或缺的技术,有效的量子纠错要求严格的实时解码:解码器必须在典型为微秒级的时间尺度内处理差错症测量结果并确定纠错操作,以避免数据积压。向大量逻辑量子比特扩展进一步需要大量计算资源。本研究提出一种名为THQLink的架构,用于利用高性能计算(HPC)资源实现量子纠错码的实时解码。连接HPC与量子处理单元(QPU)控制系统的网络基于TH-Express构建,可适配不同量子技术及其相关控制栈。我们报告平均往返延迟为2.944微秒,每增加一跳的增量开销为130纳秒。采用并行窗口策略,我们在CPU上使用基于匹配的解码器实现了距离达19的表面码的实时解码(每个量子纠错周期耗时1微秒)。本研究提出了一种用于容错量子计算的可扩展实时解码框架,可直接应用于QPU与HPC资源紧密集成的量子中心超级计算机,从而为混合量子-经典算法及从QPU卸载的计算密集型工作负载提供高效支持。

英文摘要

Quantum error correction (QEC) is indispensable for building scalable fault-tolerant quantum computers. Effective QEC demands stringent real-time decoding: the decoder must process syndrome measurements and determine corrections within a time scale--typically on the order of microseconds, to avoid data backlog. Scaling to large number of logical qubits further necessitates significant computational resources. In this work, we propose an architecture, called \emph{THQLink}, for real-time decoding of quantum error correction codes using high-performance computing (HPC) resources. The network connecting the HPC and the control system of quantum processing unit (QPU) is built on TH-Express and can be adapted to different quantum technologies and their associated control stacks. We report a round-trip latency of 2.944 $μ$s on average, with an incremental overhead of 130 ns per additional hop. Using a parallel window strategy, we demonstrate real-time decoding (1 $μ$s per QEC round) of the surface code up to distance 19 using a matching-based decoder on CPUs. Our work presents a scalable framework for real-time decoding in fault-tolerant quantum computing. It can be readily applied to quantum-centric supercomputers that feature tight integration between QPU and HPC resources, thereby enabling efficient support for hybrid quantum-classical algorithms and computation-intensive workloads offloaded from the QPU.

Comments11 pages, 11 figures

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