Quantum

Riverlane and NVIDIA Use AI to Speed Up Quantum Decoder Calibration

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 15/10/2026, 15:34:28 UTC.

Briefings

Riverlane and NVIDIA are collaborating to accelerate quantum error correction by using AI-driven calibration on NVIDIA H100 GPUs. A reported test reduced surface-code training cycles from three minutes to 2.8 seconds on a single CPU core, while the GPU processed over four million syndromes per second versus 59,000 on a CPU. Steve Brierley, Riverlane's CEO, emphasized that QEC is critical for enabling useful quantum computers. The study explores how AI can create faster quantum-classical systems for future processors.

Key points

• Riverlane and NVIDIA are collaborating to accelerate quantum error correction by using AI-driven calibration on NVIDIA H100 GPUs.
• A reported test reduced surface-code training cycles from three minutes to 2.8 seconds on a single CPU core, while the GPU processed over four million syndromes per second versus 59,000 on a CPU.

Context

Quantum computing faces challenges from extreme noise sensitivity, requiring real-time error correction. Riverlane and NVIDIA are exploring AI and GPU acceleration to speed up decoder calibration, which is essential for maintaining hardware stability as quantum systems scale.

Limitations

The evidence is limited to a single reported test cycle on an NVIDIA H100 GPU. Results are presented as reported findings without further validation or independent verification. The study covers broader future questions but does not provide long-term performance data or scalability metrics.

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