NVIDIA Launches Ising, the World’s First Open AI Models to Accelerate the Path to Useful Quantum Computers
NVIDIA has announced the launch of NVIDIA Ising, the world’s first family of open-source quantum AI models, aimed at helping researchers and enterprises develop quantum processors capable of supporting practical applications.
Advancing quantum computing at scale requires major progress in areas such as processor calibration and error correction. AI plays a crucial role in transforming today’s quantum systems into reliable, large-scale computers. By offering open models, NVIDIA enables developers to build high-performance AI solutions while retaining full control over their data and infrastructure.
Named after a foundational mathematical model that simplified the study of complex physical systems, the NVIDIA Ising family delivers scalable, high-performance AI tools designed to address two of the most critical challenges in hybrid quantum-classical computing: error correction and processor calibration.
These models enhance quantum processor calibration and allow researchers to solve larger and more complex problems, offering up to 2.5 times faster performance and three times greater accuracy in the decoding processes required 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.”
NVIDIA Ising includes state-of-the-art customizable models, tools and data that accelerate quantum processors:
- Ising Calibration: A vision language model that can rapidly interpret and react to measurements from quantum processors. This enables AI agents to automate continuous calibration, reducing the time needed from days to hours.
- Ising Decoding: Two variants of a 3D convolutional neural network model — optimized for either speed or accuracy — to perform real-time decoding for quantum error correction. Ising Decoding models are up to 2.5x faster and 3x more accurate than pyMatching, the current open source industry standard.
Ecosystem Adoption
Leading enterprises, academic institutions, and research labs are already adopting Ising to advance quantum computing development.
Ising Calibration is currently being used by organizations such as Atom Computing, Academia Sinica, EeroQ, Conductor Quantum, Fermi National Accelerator Laboratory, Harvard’s 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).
Meanwhile, Ising Decoding has been deployed by institutions including Cornell University, EdenCode, Infleqtion, IQM Quantum Computers, Quantum Elements, Sandia National Laboratories, SEEQC, the University of California San Diego, UC Santa Barbara, the University of Chicago, the University of Southern California, and Yonsei University.
In addition, NVIDIA is providing a comprehensive set of quantum computing workflows and training data, along with NVIDIA NIM™ microservices. These resources enable developers to fine-tune models for specific hardware architectures and use cases with minimal setup. The models can also run locally on researchers’ systems, helping to safeguard proprietary data.