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At GTC 2026, NVIDIA introduced cudaq-realtime, a new library built on NVQLink that provides a runtime API for microsecond-latency callbacks between GPUs and quantum controllers. CUDA-Q QEC 0.6 integrates directly with cudaq-realtime to bring GPU-accelerated decoding into the real-time quantum control loop. Now any quantum hardware vendor using NVQLink is able to draw on the power of the CUDA-Q QEC library for real-time decoding.

CUDA-Q QEC 0.6 ships with two new real-time-capable decoder pipelines: the RelayBP belief-propagation decoder for qLDPC codes and an NVIDIA Ising convolutional neural network (CNN) pre-decoder paired with a global decoder (PyMatching) for the surface code. These pipelines enable quantum vendors and QEC researchers to deploy real-time GPU decoding for two popular code families via NVQLink.

This blog describes in detail how CUDA-Q QEC 0.6 interacts with NVQLink, allowing developers to see how they can begin to build highly optimized quantum error correction workflows.

CUDA-Q 0.14 Simplifies Application Development and Brings macOS Support

GTC26 sees the release of CUDA-Q 0.14 - delivering several important advancements for developers and researchers. Notably, it introduces the CUDA-Q Application Hub as a Brev Launchable, providing easy access and streamlined experimentation with sample applications across domains like quantum chemistry, AI, and optimization.

In addition, this release adds long-awaited macOS support, enabling efficient local development and prototyping through Python using a local CPU. Furthermore, CUDA-Q provides options to scale experiments by connecting to more supported quantum processing units (QPUs) and cloud simulators, allowing users to seamlessly transition from local testing to more advanced deployments.

CUDA-Q Application Hub Launchable

Fig 1: CUDA-Q Application Hub Launchable