NVIDIA released the NVIDIA Ising family of open-source AI models for quantum processor calibration and error correction. The models allow researchers and enterprises to process quantum system data while retaining control over data and computing infrastructure.
The models run calibration routines and decode error correction data generated by quantum systems. These functions address two core engineering challenges in scaling quantum processors into reliable computing systems.
Named after a mathematical model used to describe physical systems, the Ising models process data from quantum hardware and support calibration workflows. They also decode error correction outputs required for stable operation in hybrid quantum-classical systems.
The Ising models scale across larger datasets and problem sizes used in quantum computing. NVIDIA reports up to 2.5x faster performance and 3x higher accuracy in decoding tasks for quantum error correction.
“AI is essential to making quantum computing practical,” said Jensen Huang, founder and CEO of NVIDIA. “With Ising, AI becomes the control plane – the operating system of quantum machines – transforming fragile qubits to scalable and reliable quantum-GPU systems.”
The quantum computing market is projected to exceed $11 billion by 2030, according to analyst firm Resonance. Progress in calibration, error correction, and system scaling remains critical for broader deployment.
NVIDIA Ising includes customizable models, tools and data that accelerate quantum processors:
- Ising Calibration: A vision-language model processes measurements from quantum processors and supports automated calibration workflows. The system uses AI agents to run calibration, reducing calibration time from days to hours.
- Ising Decoding: Two variants of a 3D convolutional neural network model perform quantum error correction decoding, with configurations for speed and accuracy. The Ising Decoding models achieve up to 2.5x faster processing and 3x higher accuracy than the open-source pyMatching decoder.
Ecosystem Adoption
NVIDIA deployed Ising AI models for quantum calibration and error correction, with use across research institutions and quantum hardware developers.
Ising Calibration is in use at Atom Computing, Academia Sinica, EeroQ, Conductor Quantum, Fermi National Accelerator Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Infleqtion, IonQ, IQM Quantum Computers, Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed, Q-CTRL and the U.K. National Physical Laboratory (NPL).
Ising Decoding is deployed at Cornell University, EdenCode, Infleqtion, IQM Quantum Computers, Quantum Elements, Sandia National Laboratories, SEEQC, University of California San Diego, UC Santa Barbara, University of Chicago, University of Southern California and Yonsei University.
NVIDIA provides workflow templates, training datasets, and NVIDIA NIM microservices to support model tuning for different quantum hardware architectures. The models can run on local systems, allowing teams to keep proprietary data within their own environments.
NVIDIA integrates the Ising models with CUDA-Q software for hybrid quantum-classical computing and connects them through the NVQLink QPU-GPU interconnect for real-time control and error correction processing.
NVIDIA Open Models
NVIDIA Ising joins NVIDIA’s open model portfolio, which includes NVIDIA Nemotron for agentic systems, NVIDIA Cosmos for physical AI, NVIDIA Alpamayo for autonomous vehicles, NVIDIA Isaac GR00T for robotics and NVIDIA BioNeMo for biomedical research.
Source: NVIDIA
About NVIDIA
NVIDIA, founded in 1993 and headquartered in Santa Clara, CA, designs and manufactures graphics processing units, systems on chips, networking hardware, and AI intelligence software such as CUDA. Its products serve industries including gaming, data centers, autonomous vehicles, professional visualization, robotics, health care, and energy. The company introduced the GPU in 1999 and later expanded into accelerated computing and AI infrastructure. In gaming, its GPUs support high-performance rendering, while in AI and high-performance computing, its systems provide the infrastructure for training and deploying large-scale models. NVIDIA also develops tools for robotics and autonomous driving.