NVIDIA Unveils: First Open Family of Quantum AI Models for Hybrid Quantum-Classical Systems

Quantum Computing has spent years living in the future. Hardware has advanced, research has converged, and business dollars have followed — but the gap between a quantum processor working in the lab and one running a real-world application remains stubbornly wide. NVIDIA has moved to close that gap with the launch of the NVIDIA Egthe world's first family of open source quantum AI models designed to help researchers and businesses build quantum processors capable of running useful applications.

Here is the core problem that Ising designed to solve: quantum computers are incredibly sensitive. Their basic unit of calculation, i qubitit is so easily disturbed by natural noise that errors quickly accumulate during the calculation. Before you can use anything meaningful in a quantum processor, two things must work properly – balancing (to make sure the hardware is tuned and working properly) and to correct the error (detecting and correcting errors as they occur in real time). Both of these have historically been manual, slow, and difficult to measure. NVIDIA is betting that AI can do both.

What Makes a Singing Model Family Really Go Together

NVIDIA Ising consists of two separate components: The Rating of Songs again Ising Decoding.

The Rating of Songs a conceptual language model — a structural model familiar to anyone who has worked with multimodal AI — designed to rapidly interpret and respond to measurements from quantum processors. Think of it as an AI agent that continuously monitors diagnostic readings from quantum hardware and automatically adjusts the system to keep it running smoothly. This allows AI agents to perform continuous calibration, reducing the time required from days to hours. That's no small acceleration – in quantum hardware development, days of measurement time between experiments is a major bottleneck.

Ising Decoding comes in two types of a 3D convolutional neural network (3D CNN) model, each optimized for a different trade-off: one for speed and one for accuracy. These models perform real-time decoding for quantum error correction. If you've worked with signal processing or linear modeling, debugging is defined in a similar way — you're trying to determine what the 'correct' state of the system should be, given a noisy observation. Ising Decoding models up to 2.5x faster and 3x more accurate than pyMatching, the current open source industry standard.

The Ecosystem Is Already Moving

Ising Calibration is already used 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, Quantum National Laboratory, Quantum National Laboratory's Advanced, Quantum National Laboratory, UK Testbed National Laboratory. Ising Decoding is distributed 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. That's a one-day wide acceptance that takes national labs, Ivy League institutions, and quantum hardware companies all over the many qubit paths.

How It Fits into NVIDIA's Quantum Stack

NVIDIA Ising is compatible with the NVIDIA CUDA-Q software platform for hybrid quantum-classical computing and includes NVIDIA NVQLink QPU-GPU hardware connectivity for real-time control and quantum error correction. CUDA-Q is NVIDIA's comprehensive model for integrated quantum-classical workflows – if you wrote CUDA characters for GPU acceleration, CUDA-Q follows the same philosophy of tightly combining classical and accelerated computing. NVQLink is a hardware bridge that allows GPUs to communicate with quantum processing units (QPUs) during the latency required for real-time error correction.

Key Takeaways

• NVIDIA Ising is the world's first family of open quantum AI modelsis purpose-built to solve two of the most difficult engineering problems holding back real quantum computing – measurement and error correction – using AI instead of slow, manual processes.
• Ising Calibration uses a visual language model to automatically tune quantum processorsreducing the time required for continuous measurement from days to hours by allowing AI agents to interpret and react to hardware measurements in real time.
• Ising Decoding uses a 3D convolutional neural network (3D CNN) to correct the quantum error in real time.delivers up to 2.5x faster performance and 3x higher accuracy compared to pyMatching.
• Discovery has been wide and varied since day onewith leading institutions including Fermi National Accelerator Laboratory, Harvard, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed, IQM Quantum Computers, Sandia National Laboratories, and more than a dozen universities and businesses deploying Ising Calibration and Ising Decoding in all qubit modes.
• Ising integrates directly with NVIDIA's quantum-classical software and hardware stackcomplements the NVIDIA CUDA-Q platform for hybrid quantum-classical computing and the NVIDIA NVQLink QPU-GPU hardware interconnect, with models available on GitHub, Hugging Face, and build.nvidia.com and configurable via NVIDIA NIM microservices.

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