NVIDIA Ising AI Models Target Quantum Computing's Biggest Bottlenecks With Open-Source Approach

NVIDIA Launches Ising, the World's First Open AI Models to Accelerate the Path to Useful Quantum Computers

NVIDIA Ising Delivers Breakthrough Performance in Quantum Calibration and Error Correction, Empowering Researchers and Enterprises to Build Scalable, High-Performance Quantum Systems * 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. * Leading quantum enterprises, academic institutions and research labs adopting Ising include Academia Sinica, Fermi National Accelerator Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Infleqtion, IQM Quantum Computers, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed and the U.K. National Physical Laboratory (NPL). NVIDIA today 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. 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. "AI is essential to making quantum computing practical," said Jensen Huang, founder and CEO of NVIDIA. "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits to scalable and reliable quantum-GPU systems." 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 state-of-the-art customizable models, tools and data that accelerate quantum processors: * Ising Calibration: 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: 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. Ecosystem Adoption Leading enterprises, academic institutions and research labs are adopting Ising for quantum computing development. 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. In addition, 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. Get Started With NVIDIA Open Models 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. Learn more by watching the special address from NVIDIA Quantum Day and tuning in to this NVIDIA AI Podcast episode.

Nvidia unveils open source quantum AI model Ising

Ising models are designed to help perform quantum error correction and calibration. Nvidia has announced a new family of open source quantum AI models on World Quantum Day (14 April). 'Ising', the "world's first" models for building quantum processors, joins a growing list of Nvidia open source models including 'Alpamayo', for autonomous vehicles, 'Nemotron', for agentic systems, and 'Cosmos', for physical AI. Ising models are designed to help researchers and enterprises perform quantum error correction and calibration. The family includes Ising Calibration, a vision language model that can interpret and react to measurements from quantum processors, and Isling Decoding, two variants of a neural network model that can perform real-time decoding for quantum error correction. Ising Decoding can deliver up to 2.5-times faster performance and 3-times higher accuracy than current industry standards, Nvidia said. The models are available for download on GitHub, Hugging Face and Nvidia. Ising is already in use at the Harvard John A Paulson School of Engineering and Applied Sciences, IQM Quantum Computers, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed, the UK National Physical Laboratory and the University of California San Diego, among a list of prominent names disclosed by the company. "AI is essential to making quantum computing practical," said Jensen Huang, the founder and CEO of Nvidia. "With Ising, AI becomes the control plane - the operating system of quantum machines - transforming fragile qubits to scalable and reliable quantum GPU systems." Nvidia's Ising joins other quantum-specific products, including the CUDA-Q quantum software platform, and the NVQ Link that connects GPU computing with quantum processors. With major funding rounds, and a strong focus on research and development, the quantum sector is expected grow to more than $11bn in value by 2030. In Ireland, home-grown start-up Equal1, which announced a $60m round in January, is working towards bringing its rack-mounted quantum processing units to the enterprise market. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

Nvidia unveils Ising AI models for quantum error correction and calibration - SiliconANGLE

Nvidia unveils Ising AI models for quantum error correction and calibration Technology and computing giant Nvidia Corp. today announced the release of Ising, the world's first open artificial intelligence model family aimed at quantum computing calibration and error correction. Nvidia, whose main business is the graphics processing units that power AI, said these AI models will allow researchers and enterprise companies to build better quantum computers capable of running useful applications at scale. To build and run useful applications, quantum computers must handle millions of qubits -- the atomic computational units of quantum information. The essential problem is that qubits are fragile, error-prone and susceptible to noise at scale. As quantum computers grow, they must be error-corrected and calibrated in real time to account for environmental factors and remain useful. "AI is essential to making quantum computing practical," founder and Chief Executive Jensen Huang said. "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits into scalable and reliable quantum-GPU systems." Ising is named after the landmark mathematical model that helped simplify the understanding of complex physical systems by describing how interacting particles, or spins, influence one another. Nvidia is providing two models: one for real-time error correction and one for calibration. The need for error correction is obvious: It turns noisy systems into coherent outputs. That is where Ising Decoding comes in. Decoding comes in two variants of a 3D convolutional neural network model, one optimized for speed and the other for accuracy, that perform real-time decoding for quantum error correction. Nvidia said the models provide up to 2.5 times more speed and three times more accuracy than pyMatching, the current open-source industry standard. Ising Calibration allows physicists to prepare systems by tuning, measuring and optimizing physical control signals, such as microwaves or lasers. This calibration is necessary to ensure high-fidelity outputs by correcting for noise, hardware instability and parameter drift over time. It's a vision-language model that can rapidly interpret and react to measurements from quantum processors, driving AI agents that automate continuous calibration. Speaking at a briefing, Sam Stanwyck, Nvidia's director of quantum product, said the company chose decoding and calibration first because they address the most immediate obstacles to scaling quantum systems. He described both as "AI-shaped workloads," where models can make an immediate impact today, but said Nvidia's longer-term vision goes further. Over time, the company expects AI to help build and optimize quantum circuits as well, making decoding and calibration the first milestones on a broader path toward scalable quantum-GPU-based supercomputers. Ising Decoding and Ising Calibration are already being adopted by enterprise and research organizations. Decoding is being deployed by Cornell University, Sandia National Laboratories, the University of California at San Diego and UC Santa Barbara, among others. Calibration is already in use by Atom Computing, Academia Sinica, EeroQ, IonQ, IQM Quantum Computers, Q-CTRL and others. Additionally, Nvidia released a cookbook of guides, including quantum computing workflows and training data, along with an Nvidia NIM microservice. This will allow developers to customize, train, fine-tune and build models for different hardware setups, and run them locally on researchers' systems to protect sensitive data.

Nvidia launches open-source 'Ising' AI models to tackle quantum computing bottlenecks

Firms such as IBM, Google and Microsoft are building quantum hardware and software stacks, while startups like IonQ are focused on specialised systems. However, all players face a common challenge -- making quantum computers stable enough for real-world use. Chipmaker Nvidia on Tuesday announced a new family of open-source AI models, called Ising, aimed at addressing key challenges in quantum computing, including error correction and processor calibration. The company said the models are designed to help researchers and enterprises build more reliable quantum systems capable of running practical applications. Also Read: From OpenAI to Nvidia, tech firms channel billions into AI infrastructure as demand booms What Nvidia is launching The Ising family includes AI tools that improve how quantum computers are tuned and how errors are corrected during computations -- two of the biggest hurdles in scaling the technology. Quantum computers rely on qubits, which are highly sensitive and prone to errors. Nvidia said its models can significantly improve performance, delivering faster and more accurate decoding for quantum error correction compared to existing open-source methods. The suite includes: Ising Calibration: An AI model that automates the tuning of quantum processors, reducing the process from days to hours Ising Decoding: Neural network-based models for real-time error correction Why this matters While quantum computing has long been seen as the next frontier in computing, its real-world use has been limited by reliability issues. Nvidia's move reflects a broader industry shift where companies are increasingly combining artificial intelligence with quantum systems to overcome these limitations. Instead of relying solely on hardware improvements, firms are now using AI to stabilise and optimise quantum machines. Nvidia CEO Jensen Huang said AI would act as the "control plane" for quantum systems, effectively turning fragile qubits into scalable and reliable computing platforms. Also Read: How Nvidia CEO Jensen Huang personally uses AI in his daily life: 'I am not asking it to think for me' The bigger industry picture The announcement comes as major technology companies race to make quantum computing commercially viable. Firms such as IBM, Google and Microsoft are building quantum hardware and software stacks, while startups like IonQ are focused on specialised systems. However, all players face a common challenge -- making quantum computers stable enough for real-world use. Nvidia is positioning itself differently. Rather than building quantum hardware, it is focusing on the AI layer that can make these systems usable, aligning with its broader push to expand beyond chips into full-stack AI infrastructure. Part of a larger AI push The Ising models also fit into Nvidia's wider strategy of developing open AI models across industries, from robotics to healthcare. The company has been steadily expanding its AI ecosystem, arguing that artificial intelligence is becoming core infrastructure across sectors, from scientific research to industrial applications.

NVIDIA Ising open-source models aim to accelerate quantum scaling with AI

AI-driven calibration and decoding accelerate quantum system scalability If you think about it, AI has gone beyond the hype and is here to stay, and all within just 3-4 years. With quantum computing, despite decades of research, that moment has stubbornly refused to arrive. Despite cutting-edge efforts from Google, IBM, and other frontier tech labs around the world, quantum computing's progress has been slow and frustrating. Wish there was a way to speed things up a bit? With its newly announced Ising model family, NVIDIA is effectively suggesting that the key to unlock quantum computing lies not inside the quantum machine itself but in the AI wrapped around it. Over the past few years, "AI models and agents have become dramatically more capable, and demand for agentic AI is accelerating adoption for industry as well as for science," said Sam Stanwyck, Director of Quantum Product at NVIDIA, during a briefing on Ising family of open-source models tuned for quantum computing tasks. Supercharging quantum computing's roadmap matters, argued Stanwyck, because quantum computing's biggest limitation isn't raw ambition -- it's fragility. Qubits are notoriously unstable, and Stanwyck didn't sugarcoat the scale of the problem. Also read: QpiAI: India's quantum leap from Bangalore to the world "Today the very best quantum processors make an error about once in every thousand operations but to become useful accelerators that number needs to become one in a trillion or even less." That reduction gap is where AI steps in, according to NVIDIA. "The good news is AI is the answer for how you manage this noise at scale." NVIDIA Ising is built precisely around that premise. Positioned as "the world's first family of open AI models for building quantum processors," it's meant to tackle two of the most stubborn bottlenecks to quantum computing progress - calibration and error correction decoding. What's obviously interesting is how NVIDIA is choosing to tackle both these problems through open-source models - not closed, proprietary breakthroughs. This is a decisive move, explained Stanwyck. Also read: Quantum Diamonds explained: How diamond defects are driving the quantum revolution "Open source enables a diverse dynamic ecosystem and drives success for all," Stanwyck noted. "It lowers the barrier to entry, advances innovation and promotes interoperability across the global AI landscape." In a field as niche as quantum computing, that's the only strategy to ensure rapid progress in the quantum computing realm. The implications of this strategy became clearer when Stanwyck reframed the architecture entirely. "AI is becoming the control plane for quantum hardware." It's a deceptively simple line to miss, but one that signals a shift in how the industry at large is starting to think about quantum systems - especially in this GenAI renaissance. Instead of treating quantum processors as standalone marvels, NVIDIA is effectively turning them into components. Like accelerators governed by AI-driven intelligence running on classical systems. This is where Ising's practical impact begins to emerge, because all said and done calibration today is still a deeply manual process. "You have PhD physicists who are spending days tuning these machines," Stanwyck said, highlighting a bottleneck that simply won't scale. With NVIDIA Ising, "AI agents can automate the full calibration workflow reducing calibration time from days to hours." On the decoding side, the gains are equally impressive. "Quantum error correction is a problem that requires decoding algorithms that process terabytes of data thousands of times per second," he explained. "Both speed and accuracy are critical." NVIDIA claims Ising improves both of these key parameters while remaining interoperable with existing systems. But perhaps the most important part of this NVIDIA Ising announcement is what it means for countries like India, where quantum ambition faces serious bottlenecks in terms of access to hardware. When asked directly about this, Stanwyck's answer was telling and refreshingly pragmatic. "You can actually, without access to any physical quantum computer, take Ising, take our platform and do cutting edge research in quantum computing and quantum error correction," he said. NVIDIA's simulation stack, combined with Ising's open models, effectively creates a parallel pathway. It makes sure that meaningful research isn't gated by access to million-dollar hardware setups. It's a big deal, because if quantum computing is to go global in the future like AI right now, it cannot remain confined to a handful of labs with unlimited resources. Stanwyck summed it up pretty well: "If there's one idea I want you to take away from this briefing, it's that AI is becoming the control plane for quantum computing." And with Ising, NVIDIA is betting that the fastest way to make quantum useful isn't to wait for perfect hardware, but to build smarter systems around imperfect ones.