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 $\mu$s on average, with an incremental overhead of 130 ns per additional hop. Using a parallel window strategy, we demonstrate real-time decoding (1 $\mu$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.
Lingling Lao, Qiang Wang, Yuan-Qi Liu et al.· 0 citations
The Message Passing Interface (MPI) is a de-facto standard programming model for developing high-performance applications on large-scale supercomputers, where the performance of its collective communication operations over the underlying interconnection network is critical to the overall scalability of the applications. While the MPI defines the semantics of these operations, it leaves the algorithmic implementation to Open MPI, and selecting the algorithm typically relies on hard-coded decision logic. However, in real-world scenarios, this static approach often leads to sub-optimal performance, as it ignores the specific properties of the underlying system, particularly the complex and dynamic interconnection topologies. In this work, we address the challenge of selecting an efficient algorithm for a given collective, taking into account interconnection topologies. First, we demonstrate through a simulation study that interconnect topological features significantly influence the choice of the optimal collective algorithm for specific communication instances. Second, we propose a novel, machine learning-based, topology-aware framework for MPI collective algorithm selection. Third, we integrate this predictive framework directly into Open MPI, and show that our method is effective in selecting efficient collective algorithms for the Next Generation Tianhe Supercomputer, thereby unlocking the performance potential of the underlying interconnect.
Meng-Cheng Jia, Wei Zhang, Hui-Ze Zhang et al.· Fall Joint Computer Conferen...· 0 citations
