aiqbench analysis
This research introduces TandemQEC, a system-level simulator designed to optimize the joint provisioning of streaming quantum error correction in tightly integrated quantum-classical architectures. By modeling dependencies across processors, links, and buffers, the study demonstrates that balanced upgrades to both decoder capacity and bandwidth are essential for achieving significant speedups, whereas isolated improvements can sometimes degrade performance due to increased classical workload. These findings provide critical guidance for scaling fault-tolerant quantum applications across various physical modalities before complete hardware systems are available.
TandemQEC: Joint Provisioning of Streaming Quantum Error Correction in Tightly-Integrated Quantum-Classical Systems
arXiv:2607.13351v2 Announce Type: replace Abstract: 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.
Abstract: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.
Quantum Physics (quant-ph); Performance (cs.PF)
arXiv:2607.13351 [quant-ph]
(or arXiv:2607.13351v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.13351 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 268 words
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