Nvidia today unveiled a new family of open-source quantum AI models for building quantum processors. The announcement coincides with World Quantum Day, an international initiative by quantum scientists to promote public understanding of quantum science and technology.
Nvidia is calling its new family of quantum AI models Nvidia Ising, named after the Lenz-Ising model of ferromagnetism in statistical mechanics. That model dramatically simplified the understanding of complex physical systems.
Ising joins other Nvidia model families, including Nemotron for specialized agentic AI systems, Cosmos for physical AI systems, Isaac for robotics, Clara for biomedical and life sciences models, Apollo for AI physics, and Alpamayo for autonomous vehicles.
Split decision
Ising will consist of two model domains at launch: Ising Calibration and Ising Decoding.
Ising Calibration is a vision language model for interpreting and reacting to measurements from quantum processors, and enables automation of continuous calibration by AI agents. Ising Decoding consists of two variants of a 3D convolutional neural network model for real-time decoding for quantum error correction. One variant is optimized for speed while the other for accuracy.
“Both of these are targeting the fundamental challenge in quantum computing, which is that qubits are inherently noise,” said Sam Stanwyck, Nvidia’s director of quantum product, in a press briefing Monday. “That noise is the fundamental bottleneck standing between today’s quantum hardware and useful applications.”
A qubit, or quantum bit, is the basic unit of information in quantum computing. And where bits in traditional computing have two possible states of either 0 or 1, qubits can represent a superposition of all states between 0 and 1 simultaneously. This allows quantum algorithms to solve certain problems in a fraction of the time it would take the fastest traditional computer systems.
Physical qubits are noisy and error-prone, which has made machines that depend on them impractical for real-world applications. For the past several years, researchers have been developing logical qubits as a higher-level abstraction from physical qubits, which can be used in fault-tolerant quantum computing to protect against noise and errors. Nvidia says the Ising models will deliver up to 2.5 times faster performance and 3 times higher accuracy for the decoding process needed for quantum error correction for logical qubits.
“Today, the very best quantum processors make an error about once in every thousand operations, which is amazing,” Stanwyck said. “But to become useful accelerators for scientific and enterprise valuable problems, that number needs to become one in a trillion or even less.”
AI is the key to closing that gap, he said, and it’ll be the control plane or operating system for quantum machines. To that end, he added, the models must be open so they can be customized, fine-tuned, and continuously improved upon by the quantum community.
The test kitchen heats up
Along with Ising, Stanwyck said Nvidia is providing a cookbook of quantum computing workflows and training data along with Nvidia NIM microservices.
“The cookbook has fine tuning, quantization, and inference workflows, recipes for how to integrate this into agentic workflows, plus open research papers and benchmark data,” he said.
He also noted that leading enterprises, academic institutions, and research labs have already adopted Ising, including Atom Computing, Fermi National Accelerator Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Cornell University, and others.
Quantum leaps
The global quantum technology industry took a big step forward in 2025, according to a report released today by the Quantum Economic Development Consortium (QED-C). In its State of the Global Quantum Industry 2026 report, QED-C said the global quantum market reached $1.9 billion in 2025 while the global pure-play quantum workforce grew by 14%. It forecasts the market will grow at a 30% annual rate to reach $3 billion by 2028, and Nvidia plans to be a key player in that growth.
“Our AI leadership is going to directly accelerate the path to useful quantum computers,” Stanwyck said. “The same GPUs that are running the world’s AI can run the control plane for quantum hardware.”