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Riverlane and NVIDIA demonstrate a significant acceleration in quantum error correction decoder calibration by leveraging AI models trained on NVIDIA H100 GPUs. This collaboration addresses a critical bottleneck where traditional CPU-based processing is too slow to keep pace with real-time hardware noise fluctuations in quantum systems. By reducing training cycles from minutes to seconds, this integration enables the high-throughput data processing required for scalable fault-tolerant quantum computing.

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Riverlane and NVIDIA Use AI to Speed Up Quantum Decoder Calibration

The Quantum InsiderOctober 15, 20265 min read

Insider Brief PRESS RELEASE — Quantum computing has the potential to solve problems beyond the reach of today’s computers, from drug discovery to materials science and optimisation. But before that can happen, the industry must first overcome the errors that are created by quantum computing’s extreme sensitivity to noise. Quantum error correction (QEC) detects and […]

Riverlane and NVIDIA are exploring how AI and GPU-accelerated computing can speed up decoder calibration for quantum error correction as quantum hardware behaviour changes. Using Riverlane’s Local Clustering Decoder on an NVIDIA H100 GPU, the reported surface-code training cycle fell from three minutes on a single CPU core to 2.8 seconds. The H100 processed more than four million surface-code syndromes per second in the reported tests, compared with around 59,000 per second on a single CPU core, supporting more frequent decoder recalibration. PRESS RELEASE — Quantum computing has the potential to solve problems beyond the reach of today’s computers, from…

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