Engineering Low-Latency, High-Throughput Quantum Decoders on NVIDIA GPUs
Stefano Markidis, Luca Pennati, Ivy Peng · arXiv: 2610.28867v1 · Summary page
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Quantum error correction meets GPU engineering
Quantum computers need classical decoders to turn error signals into timely corrections. This preprint optimizes a message-passing decoder and its post-processing for NVIDIA GPUs, combining better memory access, reduced-precision arithmetic and queues that separate unresolved cases from already converged ones. The engineering goal is to improve both throughput and the delay before a correction becomes available.
For a tested bivariate bicycle code workload, INT8 arithmetic halved amortized decoding time relative to independently tuned FP32 on the RTX 5080, with a negligible increase in logical error rate. With concurrent decoder instances, that commodity GPU delivered 91% of the GH200’s highest measured throughput and an 8.9x shorter median submission-to-correction latency. These results suggest that affordable decoder clusters deserve attention, although the comparisons use independent syndrome batches rather than establish performance inside a live quantum computer’s correction loop.
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