NVIDIA Corporation Launches Ising Open Source Quantum AI Models

Published: 2026-04-14 14:36:05 ET NVDA

Published on 04/14/2026 at 02:36 pm EDT

NVIDIA Corporation announced the world?s first family of open source quantum AI models, NVIDIA Ising, designed to help researchers and enterprises build quantum processors capable of running useful applications. The NVIDIA Ising open model family delivers the world?s best AI-based quantum processor calibration capabilities, as well as quantum error-correction decoding that is up to 2.5x faster and 3x more accurate than traditional approaches. To achieve useful quantum applications at scale, significant breakthroughs are needed in quantum processor calibration and quantum error correction.

AI is key for turning today?s quantum processors into large-scale, reliable computers. Open models empower developers to build high-performance AI while maintaining total control over their data and infrastructure. Named after a landmark mathematical model that dramatically simplified the understanding of complex physical systems, the NVIDIA Ising family provides high-performance, scalable AI tools for quantum error correction and calibration ?

two of the most critical challenges in building hybrid-quantum classical systems. Ising models run the world?s best quantum processor calibration and enable researchers to tackle much larger, more complex problems with quantum computers by delivering up to 2.5x faster performance and 3x higher accuracy for the decoding process needed for quantum error correction. The quantum computing market is expected to surpass $11 billion in 2030, according to analyst firm Resonance. This growth trajectory is highly dependent on continued progress in addressing critical engineering challenges, such as quantum error correction and scalability.

NVIDIA Ising includes customizable models, tools and data that accelerate quantum processors: Ising Calibration is 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 includes 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.

Ising Calibration is already in use by 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 being deployed by 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 is providing a cookbook of quantum computing workflows and training data along with NVIDIA NIM microservices, equipping developers to fine-tune models for specific hardware architectures and use cases with minimal setup.

The models can also run locally on researchers? systems, protecting proprietary data. NVIDIA Ising complements the NVIDIA CUDA-Q software platform for hybrid quantum-classical computing and integrates with the NVIDIA NVQLink QPU-GPU hardware interconnect for real-time control and quantum error correction, providing researchers and developers with a full suite of tools needed to turn today?s qubits into tomorrow?s accelerated quantum supercomputers.

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. These open models, data and frameworks are available on GitHub, Hugging Face and build.nvidia.com.