NVIDIA has announced the launch of Ising, a family of open source artificial intelligence models that the company describes as the world’s first of their kind for quantum computing, designed to address two of the most fundamental engineering barriers standing between today’s experimental quantum processors and genuinely useful quantum computers: calibration and error correction. Named after a landmark mathematical model that dramatically simplified the understanding of complex physical systems, the NVIDIA Ising family provides high-performance, scalable artificial intelligence tools for quantum error correction and calibration, delivering up to 2.5x faster performance and 3x higher accuracy for the decoding process needed for quantum error correction compared to pyMatching, the current open source industry standard.
The Ising family comprises two primary components: Ising Calibration, a vision language model that can rapidly interpret and react to measurements from quantum processors, enabling artificial intelligence agents to automate continuous calibration and reducing the time needed from days to hours; and Ising Decoding, available in two variants of a three-dimensional convolutional neural network optimised for either speed or accuracy, to perform real-time decoding for quantum error correction. NVIDIA Chief Executive Officer Jensen Huang described artificial intelligence as essential to making quantum computing practical, framing Ising as turning artificial intelligence into the control plane and operating system of quantum machines, transforming what are currently fragile qubits into scalable and reliable quantum-graphics processing unit systems. The open model approach is deliberate: by making the models available freely, NVIDIA allows developers to build on high-performance artificial intelligence infrastructure while maintaining total control over their own data and hardware configurations.
Adoption of Ising Calibration is already underway 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 United Kingdom National Physical Laboratory, while Ising Decoding is being deployed by 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. The breadth of institutional adoption at launch, spanning national laboratories, Ivy League universities, and specialist quantum hardware companies across multiple countries, reflects the degree to which the field has been waiting for standardised, high-performance tools in this space.
NVIDIA Ising complements the NVIDIA CUDA-Q software platform for hybrid quantum-classical computing and integrates with the NVIDIA NVQLink quantum processing unit to graphics processing unit 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. The quantum computing market is expected to surpass USD 11 billion in 2030 according to analyst firm Resonance, and the pace at which progress in error correction and calibration is achieved will be a central determinant of whether that market projection materialises on schedule. The models are available on GitHub, Hugging Face, and build.nvidia.com, alongside a cookbook of quantum computing workflows, training data, and NVIDIA NIM microservices to support developers in fine-tuning for specific hardware architectures.
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