NVIDIA unveils ‘Ising’ open AI models to boost quantum computing

Ising models, named after the landmark mathematical model, offer high-performance, scalable AI tools for quantum error correction and calibration – two of the most critical challenges to building hybrid-quantum classical systems. The company said that Ising models run the world’s best quantum processor calibration and allow researchers to combat larger and complex problems with quantum computers. “With Ising, AI becomes the control plane—the operating system of quantum machines—transforming fragile qubits into scalable and reliable quantumGPU systems,” said Jensen Huang, founder and CEO of NVIDIA.

NVIDIA Ising comes with state-of-the-art customisable models, tools, and data that accelerate quantum processors. Two of the key components are Ising calibration and Ising decoding. Ising Calibration is essentially a vision language model that can rapidly interpret and react to measurements from quantum processors. In simple words, this allows AI agents to automate continuous calibration, essentially reducing time needed from days to hours. On the other hand, Ising decoding comes with two variants of a 3D convolutional neural network model – optimised for either speed or accuracy. The Ising decoding models are up to 2.5x faster and 3x more accurate than pyMatching, which is the current open-source industry standard.

When it comes to adoption, NVIDIA said that 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 UK National Physical Laboratory (NPL). Meanwhile, 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.

Reportedly, the quantum computing market is expected to surpass $11 billion in 2030, according to Resonance, an analyst firm. This growth is largely dependent on addressing critical engineering challenges such as quantum error correction and scalability, which are essential for the development of reliable quantum systems that can perform complex computations effectively.