Quantum Computer

NVIDIA Launches AI Models for Quantum Processor Calibration and Error Correction

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By Yohai Schwiger

NVIDIA has announced NVIDIA Ising, a family of open-source AI models aimed at accelerating the development of quantum computers capable of running practical applications. The models focus on two of the field’s most critical challenges: precise quantum processor calibration and quantum error correction—engineering hurdles that currently limit the scalability of quantum systems.

Quantum computing relies on qubits—units of information that are highly sensitive to environmental noise. As a result, quantum systems are inherently unstable, prone to errors, and require continuous calibration alongside complex error-correction mechanisms. Without effective solutions to these issues, scaling such systems and running real-world workloads remains difficult.

The Ising family is designed to directly address these challenges using AI-based tools. One of its key components, Ising Calibration, is a vision-language model (VLM) capable of analyzing measurements from quantum processors and responding in real time. According to NVIDIA, the model can reduce calibration processes from days to hours by automating adjustments, while relying on a significantly smaller model footprint compared to existing approaches.

Another component, Ising Decoding, is based on a 3D convolutional neural network designed for real-time decoding in quantum error correction. It is offered in two variants—optimized for speed or accuracy—and demonstrates improved performance over existing tools, delivering faster processing and higher accuracy in decoding tasks.

The models are accompanied by supporting tools, training data, and microservices, enabling developers to tailor them to specific hardware architectures and use cases. NVIDIA emphasizes that the models can run locally, allowing organizations to maintain full control over their data and infrastructure.

The technology is already being adopted by a range of industry players, including quantum companies such as IonQ and IQM Quantum Computers, as well as leading research institutions like Harvard, Fermilab, and Lawrence Berkeley National Laboratory.

Beyond its functional capabilities, the launch reflects a broader approach by NVIDIA to integrating AI with quantum computing. The two central challenges—calibration and error correction—are fundamentally problems of real-time pattern analysis. Quantum systems generate streams of noisy, non-linear measurement data, and the task is to interpret and respond to these signals efficiently. In this context, deep learning models are particularly well suited, as they excel at identifying complex statistical structures and operating under uncertainty.

The Calibration model effectively serves as a “perception layer” for the quantum system: it receives data from the hardware, interprets it, and determines how to adjust the system. Meanwhile, the Decoding model operates across a vast space of possible error configurations, estimating the most likely solution in real time. This represents a shift from rigid algorithmic approaches toward learning-based methods that leverage statistical generalization rather than exhaustive computation.

At the same time, NVIDIA is not positioning Ising as a large-scale language model in the traditional sense. Instead, it applies advanced AI architectures tailored to specific tasks. The models themselves are relatively compact, require less training data, and are optimized to operate in complex and sensitive computational environments.

Ising integrates into NVIDIA’s broader quantum computing platform, which includes CUDA-Q for quantum application development, cuQuantum for GPU-accelerated simulation, and NVQLink for connecting quantum processing units (QPUs) with GPUs. Together, these components are designed to enable hybrid systems in which quantum and classical computing operate in tandem.

From a market perspective, quantum computing remains at a relatively early stage, but is expected to grow as solutions to error correction and scalability challenges mature. In this context, the use of AI as a system-level control layer could become a key factor in advancing the field.

The launch of Ising reflects an emerging view that quantum computing is not solely a physics or hardware challenge, but also a software and machine learning problem. The central question is whether this approach will gain traction as an industry standard—and to what extent it will help bridge the gap between experimental systems and practical quantum computing.

Q-Factor to Build Million-Qubit Quantum Computer

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Photo above (left to right): Prof. Nir Davidson, Dr. Guy Raz, Prof. Yoav Sagi, Prof. Ofer Firstenberg

Tel Aviv based  Neutral Atom quantum computing company, Q-Factor, announced $24 million in seed funding. The round was led by NFX and TPY Capital, with participation from Intel Capital, Korea Investment Partners, Deep33, and the Matias family. The Technion and Weizmann Institute of Science are also shareholders in the company. Neutral atoms have rapidly emerged as one of the most promising approaches to quantum computing. They are naturally inert, capable of holding quantum information for extended periods, yet precisely controllable using light alone, without the need for extreme cooling or complex wiring.

However, current quantum computers remain too small by orders of magnitude to deliver real commercial value. Breaking past a few thousand qubits to the hundreds of thousands or millions required for useful computation demands. Q-Factor was founded to tackle this challenge. Q-Factor was founded by Prof. Nir Davidson, a world-renowned authority in ultracold atoms with 280 published papers and former dean of physics at the Weizmann Institute of Science; Prof. Ofer Firstenberg of the Weizmann Institute, an expert in quantum optics and Rydberg atoms, formerly of Harvard and MIT; Prof. Yoav Sagi of the Technion, a leading authority in neutral-atom manipulation, formerly of JILA and the University of Colorado; and Dr. Guy Raz, a physicist with 20 years of technical leadership for multiple deep tech startups.

The founders closely analyzed the limitations of current neutral atom quantum computing, and have identified the architectural bottlenecks that prevent current platforms from scaling beyond a few thousand qubits. Q-Factor has developed an approach to overcome them and scale to over one million. “The quantum computing industry needs a revolution, not an evolution,” said Prof. Ofer Firstenberg, co-founder and chief scientist of Q-Factor.

“Current systems are too small to deliver on the promise of quantum computing, and incremental improvements alone aren’t going to close that gap. We’ve developed an architecture designed for continuous scalability, a Moore’s Law-like trajectory that can take neutral atom systems from thousands of qubits to millions and beyond.”

“Q-Factor’s founding team combines world-class scientific depth with a clear-eyed understanding of what it will take to build a commercially viable quantum computer,” said Lisa Cohen, Investment Director at Intel Capital. “They’ve watched the field evolve, learned from the challenges others have encountered, and assembled the right expertise to tackle the hardest remaining problem in quantum computing: scale.”

Viewbix to Merge With Tel Aviv–Based Quantum X Labs, Bringing the Quantum Startup to Nasdaq

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[Photo above: IBM’s commercial quantum computer under construction. Credit: IBM]

Under the agreement, Viewbix will acquire Quantum X Labs in exchange for roughly 65% of its shares and equity, giving the quantum startup control of the combined entity. Alongside the merger, Viewbix will raise $3 million through a private investment in public equity (PIPE). The PIPE component is designed to satisfy the financial guarantees required for a transaction in which a publicly traded company with capital but minimal assets merges with a private company that has assets and technology but is not publicly listed.

The company expects both the PIPE financing and the merger to close by December 2025. In practice, the structure mirrors a SPAC-like mechanism, effectively ushering Quantum X Labs into Nasdaq during a period in which quantum technologies are gaining momentum among global investors and are increasingly viewed as the next “breakout” domain after artificial intelligence.

Quantum X Labs operates with a forward-looking strategy, developing intellectual property for future quantum-computing infrastructures through several specialized subsidiaries. QuantumX focuses on predictive-maintenance algorithms for the automotive industry, CliniQuantum works on quantum-enhanced analysis of medical and biological data, Quantum Gyro is developing a quantum-based alternative to GPS, and QuantumQ Security is building cybersecurity solutions designed for the post-quantum era.

Viewbix, through its subsidiaries Gix Media and Cortex Media Group, operates in digital advertising and performance marketing. The company returned to Nasdaq’s main listing in June 2025 after several years trading over-the-counter. In July, it raised $4.5 million in a private equity offering and signaled its intent to expand into new business domains.

Following the merger announcement, Viewbix’s share price plunged nearly 40%, leaving the company with a market capitalization of approximately $22.3 million.

NTT Data to develop quantum algorithm for credit risks assessment

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Classiq Company announced a new cooperation in the quantum algorithm field with the IT Services Company NTT Data. This is the first cooperation revealed publicly by Classiq, which within its framework NTT will use the Classiq’s quantum programming platform to develop designated quantum algorithms. NTT will use those algorithms to perform complex calculations in the credit risk analysis field, as part of its novel IT services it delivers to customers in the finance world.

NTT Data, part of NTT Group, is one of the largest IT Services companies in the world. According to Gartner’s ranking, NTT Data is the sixth largest IT Company, revenue-wise, with $20.3 billion revenue in FY2021. It focuses in broad areas such as digitization processes, cloud, business intelligence, business consulting and more, and it serves customer in a wide range of sectors, to include the financial one.  Shunichi Amemiya, Head of Research and Development HQ, NTT Data, says: “We are interested in applying quantum computer technology to financial engineering and believe that the need to compute complex business models will increase in the future”.

Quantum risk management

Credit risk analysis is a weighing process intended to evaluate the risk that borrowers or vendors will not return a loan. The higher the risks – the higher is the interest the lender requests, or even refuse to loan. Amir Naveh, Co-Founder and Head of Algorithms in Classiq, explains to Techtime why quantum computing capabilities are required in calculating credit risk analysis. “Calculating and ranking credit risk requires weighing of many variables, the same way it’s done in financial option pricing process. Usually, an enormous number of scenarios and possibilities should be considered in order to achieve the correct evaluation. Using quantum computing, this process could be accelerated and more accurate, which – in turn – improves risk management of the financial body”.

Classiq is developing CAD solutions that will make it possible to write applications for quantum computers. Nir Minerbi, Classiq co-founder and CEO, told Techtime in an earlier interview: “The quantum revolution consists of two things: hardware and software. Nowadays it is almost impossible to develop applications for a quantum computer, since you have to program at the logic gate level. It’s like designing a chip at the transistor level. We build the tools that allow developing applications at a higher level of abstraction. The next layer in the quantum stack.”

Recently, Classiq launched its proprietary platform’s Beta version which allows, for the first time in the industry, to compose functional algorithms for Quantum computers. The company made the new platform available to several customers, and intends to expand the beta version access to several dozen customers in the next few months.

IBM develops a Giant 1,000-qubits Quantum Computer

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Above: Members of the IBM Quantum team at work. Credit: Connie Zhou for IBM

IBM announced an ambitious quantum computing roadmap that includes an array of 1,000 qubits (Qbit) quantum computer by the end of 2023. Today’s machinres consist of only a few dozen qubits. According to IBM, the number of qubits in quantum processors will double every year or two. In 2022 IBM will complete the development of a quantum processor with 400 cubits, and in 2023 it will launch a processor with 1,121 cubits to be called Condor.

IBM’s vision is very ambitious: “Our future computers will include more than a million qubits.” IBM is one of the most advanced players in quantum computing. In 2016, it was the first to offer public access to its quantum computer via the cloud. Today, IBM’s cloud provides access to more than 20 quantum computers of 5-qubits and 24-qubits. Earlier this year it launched a new 65-qubit quantum computer, which is the most powerful quantum computer to date.

“Super-fridges” for millions of qubits

IBM is using superconductors to build the new computers, due to their zero resistance at low temperatures. As part of the needed infrastructure, it will build a 10-foot-tall and 6-foot-wide super-refrigerator (to be called Goldeneye), which can accommodate arrays of 1,000 qubits. The long range goal is to build a network of interconnected “super-fridges” that together provide a computing capability of one million qubits.

These fridges keep the qubit array at a temperature close to absolute zero, in order to avoid any electromagnetic interference that may interrupt the quantum circuit. In quantum computing, the smallest radiation can destroy the computational process, thus the biggest challenge in developing a large quantum computer is the ability to preserve the quantum state of the qubits, until the computational process is completed.

Cracking the cholesterol mystery

Speaking with Techime, Nir Minerbi, CEO of the Israel-based Classiq which develops software solutions for quantum computing, explained the practical significance of IBM’s roadmap. “The very fact that a company like IBM, which does not usually release far-reaching statements, presents a detailed technological roadmap with clear goals–  increases the industry’s confidence in the future of quantum computing.”

According to Minerbi, quantum computing is a “tie-breaker” in exactly the types of problems that classical computers, and even supercomputers, have difficulty dealing with. “All the supercomputers in the world, together, will never be able to simulate a single cholesterol molecule. But a quantum computer with several hundred qubits will be able to do this, and will be able to test how different molecules react with cholesterol and to develop drugs.”

The next layer of Quantum Stack

Classiq is developing CAD solutions that will make it possible to write applications for quantum computers. “The quantum revolution consists of two things: hardware and software. Nowadays it is almost impossible to develop applications for a quantum computer, since you have to program at the logic gate level. It’s like designing a chip at the transistor level. We build the tools that allow developing applications at a higher level of abstraction. The next layer in the quantum stack.”

For Classiq, IBM’s roadmap is good news. “As computers get stronger, more companies are interested in developing applications for quantum computers. Today, the entire industry is looking at IBM’s statement. Now there is a clear horizon, and companies know that in a few years there will be quantum computers running significant algorithms. That’s why they are now starting to invest in software development and will need solutions like ours.”

QUA, A Universal Language for Quantum Computers

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A new computers language called QUA may be the first standard universal language for Quantum Computing. QUA allows researchers to intuitively program even the most complex quantum programs that are tightly integrated with classical processing and real-time decision-making. It was developed by Quantum Machines from Tel Aviv, whose prime activity is building a complete hardware and software solution for the control and operation of quantum computers.

A primary challengetoday is that every quantum computer has its own unique hardware. The unique nature of every system results in teams spending big amounts of time coding and programming new programs and algorithms. QUA is a pulse-level programming language for quantum devices, aimed to be a universal quantum computing software abstraction layer, suitable for all. To achieve this, several different criteria had to be fulfilled: semantical, technological, commercial and qubit agnostic.

From a semantic perspective, QUA combines universal quantum operations at the pulse-level, together with universal classical operations, namely, Turing-complete classical processing and comprehensive control-flow such as used in classical standard computers. QUA is relying on QM’s proprietary compiler, XQP, to do the heavy lifting for optimizing the many entangled quantum and classical operations. XQP compiles quantum programs to QM’s Pulse Processor assembly language which can then run them. Finally, QUA is qubit agnostic and supports all quantum processors.

$17.5M ivestment round

Used as part of Quantum Machines’ existing Quantum Orchestration Platform, QUA is a universal language. The company announced that its beta version has already been adopted by leading teams in multinational organisations, startups, national-labs, and academic institutions that develop quantum computers. “QUA is the first-ever programming language designed with the needs of quantum research in mind and offers teams a powerful and intuitive language designed not only for their present needs but also those of the future,” said Itamar Sivan, CEO of Quantum Machines.

QM was founded in 2018 by Drs. Itamar Sivan, Yonatan Cohen and Nissim Ofek, three physics Ph.Ds from Israel’s Weizmann Institute of Science. The QM team has since grown to nearly 30 employees — more than half of them physicists. In March 2020 it has secured $17.5M ivestment, led by the Israeli entrepreneur Avigdor Willenz, who sold Habana Labs to Intel in 2019 for $2 billion.

“The race to commercial quantum computers is one of the most exciting technological challenges of our generation,” said Willenz. “Our goal at QM is to make this happen faster than anticipated, and establish ourselves as an essential player in this emerging industry.”

Avidgor Willenz led the funding of Quantum Machines

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Israeli entrepreneur Avigdor Willenz, who recently sold Habana Labs to Intel for approximately $2 billion, led the recent funding for the Tel Aviv based Quantum Computing startup, Quantum Machines. The company announced that it has secured $17.5M in funding to accelerate the already rapid adoption of the company’s Quantum Orchestration Platform.

Quantum Machines (QM) has developed a complete hardware and software solution for the control and operation of quantum computers. Its Quantum Orchestration Platform (OPX) works with all quantum technologies, giving researchers and development teams everything they need to run the most complex quantum algorithms and experiments. It lays the ground for tackling some of the most challenging hurdles facing quantum computing, such as complex multi-qubit calibrations, quantum-error-correction, and scaling up to many hundreds of qubits.

Willenz (photo above) said that he had decided to back QM after the massive enthusiasm he’s witnessed from across the quantum computing industry. “The race to commercial quantum computers is one of the most exciting technological challenges of our generation,” said Willenz. “Our goal at QM is to make this happen faster than anticipated, and establish ourselves as an essential player in this industry.”

Quantum Machines' Quantum Orchestration Platform (OPX)
Quantum Machines’ Quantum Orchestration Platform (OPX)

QM was founded in 2018 by Drs. Itamar Sivan, Yonatan Cohen and Nissim Ofek, three physics Ph.Ds who met at Israel’s Weizmann Institute of Science. Today the QM team has grown to nearly 30 employees, about half of them are physicists. The company said its Orchestration Platform has already been adopted by multinational corporations and startups. In January, 2020 the company had joinedthe IBM’s Q Network. As part of the collaboration, a compiler between IBM’s quantum computing programming languages, and those of QM will be developed.

The IBM Q Network brings together startups, research labs and Fortune 500 companies including, the University of Oxford, Oak Ridge National Laboratory, ExxonMobil, Accenture and others, together with IBM scientists and engineers. IBM Q Network members have access to IBM’s quantum expertise and resources, open source Qiskit software and developer tools, and cloud-based access to the IBM Quantum Computation Center, which now includes 15 computers, including a 53-qubit system.