# Riverlane and NVIDIA use H100 GPUs to speed up quantum error-correction decoder recalibration 65-fold

Canonical page: https://technooptimist.io/stories/riverlane-and-nvidia-use-h100-gpus-to-speed-up-quantum-error-correction-b1e595f3
Date: 14 Oct 2026
About: [Riverlane](https://technooptimist.io/companies/riverlane.md) · [Deltaflow](https://technooptimist.io/quantum/deltaflow.md)
Original source: https://quantumcomputingreport.com/riverlane-and-nvidia-demonstrate-accelerated-qec-decoder-calibration-using-gpu-processing/

Riverlane and NVIDIA reported benchmarks showing GPUs can recalibrate quantum error-correction decoders much faster. Decoder weights track hardware error rates, which drift over time, so they must be recalibrated between experimental runs; the GPU loop learns better settings from experimental data.

## Key facts

- Training epoch time fell from about 180 seconds on a single CPU core to 2.8 seconds on one NVIDIA H100, a 65x speedup for the surface code.
- Syndrome throughput rose from about 59,000 per second on one CPU core to more than 4,200,000 per second on the H100, a 71x gain.
- Calibrating against device drift cut logical error rates by several percent up to a factor of two versus uncalibrated priors, depending on the code.
- The page presents GPUs as the offline learning layer, with real-time decoders such as Riverlane's Deltaflow still handling decoding during circuit execution; it gives no timelines.

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