Nvidia releases Ising open AI models to solve quantum computing's biggest bottlenecks

Nvidia releases open AI models for quantum computing tasks -- 'Ising' said to be 2.5x faster and 3x more accurate than existing tools for decoding

Nvidia brings its open model onslaught to quantum computing. Nvidia has released Ising, a family of open-source AI models for quantum processor calibration and real-time error correction decoding. Integrated with Nvidia's CUDA-Q quantum software platform and the NVQLink QPU-GPU interconnect, which was first introduced last October, the Ising models have been released on GitHub, Hugging Face, and build.nvidia.com. Nvidia has designed Ising specifically to target two bottlenecks that exist between current quantum hardware and fault-tolerant computing: calibration and decoding. The former is the manual process a QPU so that its qubits behave consistently, while the latter translates redundant measurements from an error-corrected logical qubit into a correction signal, and it only works if it keeps pace with the rate at which new errors appear on the processor. Ising Calibration is a 35-billion-parameter vision-language model fine-tuned to read experimental measurements from a quantum processing unit (QPU) and infer the adjustments needed to tune it. This reduces calibration time from days to hours when paired with an agent, Nvidia claims. The Ising Decoding family, meanwhile, comprises two variants of a 3D convolutional neural network -- 0.9 million and 1.8 million parameters, optimized for speed and accuracy, respectively -- that perform pre-decoding for surface-code quantum error correction. Nvidia has benchmarked the decoder at 2.5 times faster and three times more accurate than pyMatching, which is the open-source decoder that most quantum research groups use, while requiring ten times less training data. Sam Stanwyck, director of quantum product at Nvidia, told The Next Platform that today's best quantum processors produce an error roughly once every thousand operations, and that the logical error rate is directly tied to how quickly decoding runs alongside the hardware. A 2.5 times speedup therefore raises the ceiling on how many gate operations a quantum processor can sustain before its logical qubits break down. While Ising is open-source, the stack it sits on isn't. The decoder needs NVQLink's low-latency interconnect to feed measurement data to a GPU inside the decoding window. The calibration workflows run through CUDA-Q, and the deployment tooling targets Nvidia hardware exclusively. Nvidia has run with the same pattern with the likes of Nemotron, Cosmos, and GR00T -- open the models but keep the surrounding platform proprietary, thereby driving GPU dependencies through the workflow. That way, Nvidia remains deeply integrated with the quantum computing industry despite not building quantum hardware. Named adopters include Fermilab, Harvard, the UK National Physical Laboratory, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed, IQM Quantum Computers, Infleqtion, and IonQ, which is using Ising Calibration directly. Follow Tom's Hardware on Google News, or add us as a preferred source, to get our latest news, analysis, & reviews in your feeds.

Nvidia New AI Models Spark Rally in Quantum Computing Stocks

Asian software and information-technology stocks surged after Nvidia Corp. unveiled a suite of new open-source AI models aimed at accelerating progress within quantum computing. In South Korea, shares of several software and cybersecurity firms including Axgate Co. and ICTK Co. briefly hit their daily trading limit of 30%. China's GuoChuang Software Co. and QuantumCTek Co., along with Japan's Fixstars Corp., each rose at least 8%. Nvidia's new Ising artificial intelligence model, which launchedBloomberg Terminal late Tuesday in Asia, has renewed expectations that AI can improve quantum computing and make it scalable and more useful. Still, "while these tools can potentially help accelerate developments, the deployment of practical, large-scale quantum computing remains a long way off," according to Bloomberg Intelligence analyst Robert Lea. Wednesday's gains come as tech and AI shares rebounded across the region, helped by signs that the US and Iran were reviving peace talks. The global quantum computing market is projected to jump to more than $11 billion by 2030 from nearly $1.7 billion in 2024, according to Stratistics Market Research Consulting.

Quantum stocks on pace for a massive week after Nvidia debuts AI models to boost the tech

A Rigetti quantum computer displayed at the Nvidia booth during the Nvidia GTC (GPU Technology Conference) in Washington, DC, US, on Wednesday, Oct. 29, 2025. Quantum stocks sank slightly on Thursday, but were still on track for a massive week-to-date rally fueled by enthusiasm for Nvidia's new open-source artificial intelligence models designed to advance the burgeoning computing technology. Since the start of the week, IonQ shares have skyrocketed 50%, as have shares of D-Wave Quantum. Quantum Computing and Rigetti Computing have surged more than 20% each. The rally comes on the heels of Nvidia's unveiling of Ising, a new family of open-source models aimed at accelerating the adoption of quantum computing. "AI is essential to making quantum computing practical," Nvidia CEO Jensen Huang said in a statement. "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits to scalable and reliable quantum-GPU systems." Nvidia explained further in a press release that Ising "provides high-performance, scalable AI tools for quantum error correction and calibration -- two of the most critical challenges in building hybrid-quantum classical systems." The chip giant named Ising after a famous mathematical model. Nvidia's announcement aired on what's become known as "World Quantum Day," ever since an international group of scientists announced in 2021 that April 14th should be used to promote public awareness of quantum technology. The date was chosen because 4.14 represents the first three digits of a key concept in quantum physics known as the Planck constant.

Ising: Nvidia AI tackles quantum computing's biggest bottlenecks

NVIDIA has launched a new family of open AI models aimed at solving two of the biggest challenges in quantum computing: calibration and error correction. Called Ising, the models are designed to help researchers and enterprises build more stable and scalable quantum systems by improving how quantum processors are tuned and how errors are detected and corrected in real time. Quantum computers are highly sensitive systems where even small disturbances can lead to errors. Fixing these issues has been one of the biggest barriers to building machines that can handle real-world applications at scale.

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 launches open-source AI models for quantum computing

Nvidia $NVDA has launched a family of open-source AI models called Ising, designed to help researchers and companies build quantum processors capable of running real-world applications. The announcement coincides with World Quantum Day. The Ising family launches with two model domains. Using a vision language model architecture, Ising Calibration handles the continuous tuning of quantum processors automatically, reducing a process that previously took days to one measured in hours, according to Nvidia. For error correction, the Ising Decoding component offers two distinct versions of a 3D convolutional neural network -- one built to prioritize processing speed, the other to maximize accuracy. Benchmarked against PyMatching -- the prevailing open-source standard for this work -- the combined model suite achieves error correction decoding that is up to 2.5 times faster and three times more accurate, Nvidia said. "AI is essential to making quantum computing practical," Nvidia CEO Jensen Huang said in a statement. "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits to scalable and reliable quantum-GPU systems." The two model domains address what Nvidia describes as quantum computing's core bottleneck: qubits are inherently noisy. Sam Stanwyck, Nvidia's director of quantum product, told reporters that current top-tier quantum processors achieve roughly one error per thousand operations -- impressive by today's standards, but far short of the one-in-a-trillion threshold that would make them genuinely useful accelerators, according to CIO. Calibration reduces noise in each processor, while error correction decoding catches and fixes errors in real time faster than they accumulate, Nvidia said. The Ising Calibration model, trained on data spanning multiple qubit types including superconducting qubits, quantum dots, ions, and neutral atoms, outperformed several general-purpose AI models on QCalEval, a benchmark Nvidia developed with quantum partners to evaluate calibration tasks. Nvidia is pairing the model release with a set of supporting resources: a cookbook covering quantum computing workflows and training data, plus NIM microservices developers can use to adapt the models to their specific hardware configurations. The models can also run locally on researchers' systems to protect proprietary data. Institutions already adopting Ising include Fermi National Accelerator Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed, IQM Quantum Computers, Infleqtion, Cornell University, Sandia National Laboratories, and the U.K. National Physical Laboratory, among others, Nvidia said. Ising joins Nvidia's broader open model portfolio, which includes Nemotron for agentic systems, Cosmos for physical AI, Alpamayo for autonomous vehicles, Isaac GR00T for robotics, and BioNeMo for biomedical research. Model weights are available on Hugging Face and build.nvidia.com, with training frameworks on GitHub under the Apache 2.0 license. AI and quantum computing have been converging as each technology proves useful to the other. AI tools have been improving quantum circuit design and error correction, while quantum processors show promise for specific AI tasks such as fraud detection and generating synthetic training data. The infrastructure requirements for the two technologies differ significantly, however -- quantum systems require extreme cooling and specialized facilities that have kept the technology largely confined to laboratories. A report published the same day by the Quantum Economic Development Consortium put the global quantum market at $1.9 billion for 2025, per CIO. Looking ahead, the consortium projects annual growth of 30%, with the market expected to hit $3 billion by 2028.

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 Unveils Ising Open Models for Quantum Computing Control and Correction

NVIDIA has announced Ising, a new family of open AI models aimed at one of the hardest problems in quantum computing: turning fragile experimental hardware into systems that can eventually run useful applications. Rather than focusing on qubit counts alone, the release targets the software and control layer needed to keep quantum processors stable, tuned and error-managed during operation. The Ising family is built around two main tasks. The first is calibration, where quantum hardware needs constant adjustment to remain within usable operating conditions. This is still a major bottleneck in the field because quantum processors can drift over time and often require repeated measurement and retuning. NVIDIA's Ising Calibration model is designed to process measurement data and help automate those tuning cycles, with the company saying it can reduce workflows that currently take days down to hours. The second part of the release focuses on quantum error correction. That is a core requirement for scalable quantum systems, since qubits are highly sensitive to noise and errors accumulate quickly. NVIDIA says its Ising Decoding models use 3D convolutional neural networks to perform real-time decoding, with separate variants aimed at either lower latency or better correction accuracy. According to the company, these models can deliver up to 2.5 times higher performance and up to three times greater accuracy than pyMatching, one of the better-known open-source tools currently used for quantum decoding workloads. Strategically, the launch fits into NVIDIA's larger effort to build infrastructure around hybrid quantum-classical computing. Ising is meant to work alongside CUDA-Q, the company's software platform for quantum-classical development, and can integrate with NVQLink, NVIDIA's QPU-GPU interconnect intended for low-latency communication and control. In other words, NVIDIA is not just shipping models, it is trying to define part of the control plane that sits between AI acceleration and future quantum hardware. The company is also emphasizing flexibility. Alongside the models, NVIDIA is providing training data, workflow cookbooks and NIM microservices so developers can adapt Ising to different hardware architectures and research needs. Local deployment is supported as well, which matters for labs and enterprises handling proprietary calibration data, internal device telemetry or hardware-specific operating models. NVIDIA says adoption is already underway across a broad mix of commercial and academic groups. On the calibration side, it names Atom Computing, IonQ, IQM Quantum Computers, Lawrence Berkeley National Laboratory's Advanced Quantum Testbed and the U.K. National Physical Laboratory among users. For decoding, the list includes Cornell University, Sandia National Laboratories, UC Santa Barbara, USC and Yonsei University. For now, Ising is best viewed as an infrastructure play rather than a breakthrough in quantum hardware itself. But that is exactly what makes it notable. Calibration and error correction remain two of the largest obstacles between current quantum processors and useful real-world machines. NVIDIA is betting that AI will become a practical control layer for solving both, and that open models will help move that process faster.Source:

NVIDIA's new Ising AI could finally make quantum computing usable

TL;DR: NVIDIA's new open-source Ising AI models aim to enhance quantum computing by improving qubit stability and reducing error rates. Integrated with the CUDA-Q platform, Ising offers 3x accuracy, 2.5x faster performance, and requires 10x less training data, significantly accelerating quantum system calibration. NVIDIA has officially unveiled a new family of open-source AI models it's calling Ising, which is aimed at solving some of the biggest challenges holding back quantum computing. Quantum computing is the next stage of computing, and while we know that current quantum computers are intensely powerful, some solving equations that a classic computer would take millions of years to solve, in just seconds, they aren't particularly useful in everyday life. NVIDIA proposes to use AI to bridge the gap between quantum computing and real-world usefulness, and at the core of its recent announcement is its CUDA-Q platform. NVIDIA explained that this new platform is designed to be "qubit-agnostic," meaning it can work with different types of quantum hardware without being tied to a specific architecture. Think "open-source-level" interoperability, but between hardware powering quantum computers. Ising introduces a layer of intelligence, designed to stabilize quantum processors and yield more consistent results. That last point is quite a big deal, as the current biggest problem quantum computers are facing is error rates. Qubits, or quantum bits, are the foundation of quantum computers and are extremely sensitive to environmental factors, which can disturb them and cause errors. At the moment, error rates are at about one out of every thousand operations. For comparison, for a quantum computer to become practical for everyday computation, it would need to be closer to one error per trillion operations. Here's where NVIDIA's Ising comes in. NVIDIA claims to deliver 3x greater accuracy with Ising, along with 2.5x faster performance than current industry-standard tools. Another key efficiency gain is the 10x less training data, making it cheaper and faster to deploy models. There is also significant improvement on the calibration front, with NVIDIA claiming it can reduce calibration time for quantum systems from days to just hours.

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.

These Quantum ETFs Flip As Nvidia's AI Catalyst Triggers Rally - IonQ (NYSE:IONQ), Defiance Daily Target

Nvidia Sparks A Quantum Re-rating The trigger behind this trend reversal was rather obvious. Nvidia's latest push to develop quantum computing software through an AI control layer has reframed the sector's near-term potential. Its new models aim to stabilize quantum processors and accelerate real-world usability, a development analysts say could shorten the commercialization timeline. "Quantum Processor Units (QPUs) are likely to become the next important co-processor in data centers, sitting alongside CPUs and GPUs," Business Insider quoted Bernstein analysts. The move has also revived the "quantum trade" narrative, with investors once again betting on hybrid quantum-AI infrastructure as the next frontier after GPUs. ETFs Caught On The Wrong Side For leveraged and inverse ETFs betting against the theme, the timing couldn't be worse. Funds like IONZ, QBTZ, and RGTZ, designed to profit from declines in speculative tech or quantum-related equities, have seen steep drawdowns as the rally gained traction. These products are particularly vulnerable to sharp, sentiment-driven reversals due to daily rebalancing and leverage decay. The dynamic mirrors past episodes in high-beta themes like AI and semiconductors, where sudden narrative shifts, often led by Nvidia, have triggered violent short squeezes. Tactical Trade Or Structural Shift? While the rally has proven to be effective, concerns over its longevity continue. Quantum computing is in its infancy with regard to commercialization, generating minimal revenue and requiring extensive timeframes to grow. Despite the rally, even some of these names are still in the red year-to-date. IONQ is down 4%, RGTI has lost 17%, and QBTS has plunged more than 22% YTD. However, the most recent event demonstrates that it is possible to see thematic shorts in emerging technologies unravel quickly when a credible catalyst emerges. For now, the lesson learned by quantum bears is that betting against a niche that is now being backed by Nvidia, is a costly affair. Photo: Alexander56891 from Shutterstock Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

NVIDIA Just Made Quantum Computing Practical With Ising, The World's First Open AI Models For Quantum Computers

NVIDIA has introduced Ising, its newest OpenAI models designed to make Quantum Computers useful and faster with brand new capabilities. Quantum Computing has been cited as the next frontier of computing for decades. Several companies have been trying to perfect quantum computing for years now, and only now have a few started to break the code. NVIDIA already offers an open-source development platform for quantum computing called CUDA-Q. The platform is "qubit-agnostic" and works seamlessly with QPUs and Qubit Modalities. Today, NVIDIA is announcing its first family of open source quantum AI models, called Ising. The new model is designed to help researchers and enterprises build quantum processors that are not only capable but also useful for running applications, AI in specific. But the main bottleneck in quantum computing currently stems from quantum processor calibration and quantum error correction. Qubits are noisy and have many errors. Currently, Quantum processors produce an error once every thousand operations, but for Quantum Computers to become more practical, this needs to be reduced to once every trillion operations. NVIDIA says that AI is the key to eliminating this bottleneck and enabling quantum processors for large-scale, reliable computing. Ising includes two "state-of-the-art" and customizable models. As per NVIDIA, these Ising models offer 2.5x faster performance and 3x higher accuracy for the decoding process, a crucial step required for quantum error correction. What is also interesting is the fact that Ising Calibration is 15x smaller than alternatives, while Ising Decoding requires 10x lower data to train. NVIDIA confirms that its Ising open AI models are currently being used by leading researchers, academic institutions, and enterprises. Once again, this is just one step ahead in the Quantum Computing era.

Nvidia Says AI Can Finally Make Quantum Computing Work | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. Before one can run a useful calculation, a team of specialists has to spend days manually tuning the system. Even after that, the machine keeps making mistakes faster than existing software can catch them. Banks running complex risk models and drug companies testing new molecules have been waiting on the same problem for years. Nvidia thinks it has a fix. In a Tuesday (April 14) news release, the artificial intelligence company announced its launch of Ising, the world's first family of open-source AI models built to solve those two problems: getting quantum systems ready to run, and keeping them running accurately. The models don't change the underlying hardware. They make the hardware that already exists usable. Quantum processors pick up interference from their surrounding environment constantly, which throws off their calculations. Getting one ready to use has meant days of manual work -- specialists reading output, finding where performance has slipped, and adjusting the system back into working order. Even once it's running, mistakes pile up faster than existing software can fix them. In a Tuesday blog post, Nvidia said the best quantum processors today make an error roughly once in every thousand operations. To become genuinely useful for business problems, that rate needs to reach one in a trillion. Ising directly targets both problems. According to Nvidia, one model reads the processor's output in real time and automates the tuning process, cutting setup time from days to hours. A second model catches and corrects mistakes as the system runs, delivering up to 3x improvement in accuracy compared to the current industry standard at relevant operating conditions. Both models run on an organization's own systems, so data stays on site. "AI is essential to making quantum computing practical," Nvidia founder and CEO Jensen Huang said in the release. "With Ising, AI becomes the control plane, the operating system of quantum machines." Banks have long seen quantum computing as a way to run risk and portfolio calculations that today's computers cannot fully handle at scale. Classical systems manage this by approximating -- they simplify the problem to make it solvable. Quantum processors don't need to. JPMorgan Chase's applied research team has been developing quantum algorithms for portfolio optimization, option pricing and risk analysis. The team has noted that standard simulation methods for option pricing require millions of computational samples, while quantum approaches can reach comparable results in far fewer. Unreliable machines have been among the biggest obstacles to moving that work out of the lab. In the healthcare realm, developing a new medicine means understanding how molecules behave at an atomic level, a problem so complex that today's computers have to simplify it to make it manageable. As reported by McKinsey, pharmaceutical companies including AstraZeneca and Boehringer Ingelheim and have been running quantum pilots aimed at shortening drug development timelines. Those programs have produced early results. Unstable hardware has limited how fast they can move. According to Nvidia, the Ising models are already in use at Fermi National Accelerator Laboratory, Harvard's School of Engineering and Applied Sciences, IonQ, IQM Quantum Computers, and the U.K. National Physical Laboratory. The release fits a pattern Nvidia has used before: put down the software layer first, grow the ecosystem around it and build hardware relationships on top. Ising connects to Nvidia's existing quantum software platform and its hardware interconnect between quantum and classical processors. Google last month said organizations should complete their shift to quantum-resistant security by 2029, citing faster-than-expected progress in quantum hardware and error correction, as PYMNTS reported. Nvidia's Ising launch is the latest signal that the industry is moving from preparing for quantum computing to building the infrastructure to run it.

Jensen Huang Just Pulled Quantum Computing Into Nvidia's Orbit - IonQ (NYSE:IONQ), NVIDIA (NASDAQ:NVDA),

The company unveiled Ising, a family of open‑source AI models designed to accelerate quantum‑processor calibration and error correction. XNDU stock is soaring. See the chart and price action here. Quantum Stocks React The release immediately set off a chain reaction in the market as traders read Nvidia's announcement as a sector‑wide catalyst rather than a niche technical update. U.S.‑listed quantum stocks rocketed higher and the rally looks to continue on Thursday. The chart below shows the year-to-date price action for IONQ, RGTI, QBTS and XNDU: Ising Details Ising includes two core components. Ising Calibration uses a vision‑language model to interpret measurements from quantum processors and automate continuous tuning. Ising Decoding applies 3D convolutional neural networks to real‑time error correction. Nvidia says both capabilities are essential for scaling quantum systems toward practical applications. CEO Jensen Huang framed it bluntly: "AI is essential to making quantum computing practical," he said. "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits to scalable and reliable quantum-GPU systems," Huang added. Early adopters already include major research institutions such as Harvard University, IQM Quantum Computers, and the U.K. National Physical Laboratory, signaling that the tools are entering real scientific workflows rather than remaining theoretical. The Takeaway A niche tooling release turned into a global market event -- and a reminder that in 2026, even quantum computing moves when Jensen Huang does. As AI, accelerated computing and quantum research overlap, Nvidia is positioning itself as the connective layer -- and the market is pricing that in. This image was generated using artificial intelligence via Gemini. This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Quantum Computing Stocks Rally After NVIDIA Launches New Ising AI Models

NVIDIA's new open-source AI models lifted quantum computing shares on Wednesday, as investors reacted to the company's latest push into the sector. The rally spread across Asia and extended to US premarket trading, with software, cybersecurity, and quantum-related firms posting sharp gains. The move followed NVIDIA's launch of its Ising model family, which targets two core quantum computing challenges: processor calibration and error correction. Ising as a family of open-source AI models built for quantum computing research. The company said the models are designed to help researchers and businesses improve quantum processor calibration and strengthen error correction. Both areas remain major technical barriers for the industry. The company said the models can deliver up to 2.5 times faster performance and three times higher decoding accuracy in quantum error correction tasks. NVIDIA also made the models available through GitHub, Hugging Face, and its own platform. As a result, the launch immediately drew attention from researchers, developers, and investors watching the quantum sector. NVIDIA Chief Executive Jensen Huang said, "AI is essential to making quantum computing practical." He added, "With Ising, AI becomes the control plane -- the operating system of quantum machines -- transforming fragile qubits to scalable and reliable quantum-GPU systems."

Quantum stocks surge as Nvidia launches new AI models By Investing.com

Investing.com -- Shares in quantum computing companies rose in premarket trading Wednesday after Nvidia unveiled the world's first family of open-source quantum AI models, designed to accelerate progress in the space. D-Wave Quantum jumped more than 8% in the premarket trade by 05:44, and IonQ gained 6.2%. Infleqtion, Rigetti Computing and Quantum Computing also rose between 3.9% and 5.5%. Nvidia's new model family, called NVIDIA Ising -- named after a mathematical model used to describe complex physical systems -- is built to help researchers and enterprises develop quantum processors capable of running practical applications. The tech behemoth said the models target two of the most significant obstacles in quantum computing: error correction and processor calibration, delivering up to 2.5x faster performance and 3x higher accuracy in the decoding process required 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 global quantum computing market is projected to exceed $11 billion by 2030, Nvidia said, citing analyst firm Resonance, with much of that growth contingent on continued engineering breakthroughs in error correction and scalability.

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.