By Mohib Ur Rehman
Publication Date: 2026-10-15 09:30:00
Insider Brief
- 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 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 corrects these errors. It’s essential for building reliable and useful quantum computers, but this also creates enormous demands for real-time data processing. Future systems may need to process terabytes of information every second, with near-perfect accuracy, to support error-free quantum computing.
To explore how to meet these demands, Riverlane and NVIDIA are collaborating on the use of AI and NVIDIA accelerated computing for QEC, including how GPUs can make decoder calibration faster, and help quantum systems keep up with…
