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First Open Quantum AI Models

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Nvidia investor class cleared in crypto revenue suit

NVIDIA Ising has launched as the world’s first family of open-source quantum AI models, targeting the two biggest engineering bottlenecks in quantum computing: processor calibration and error correction decoding.

Summary

  • NVIDIA Ising delivers up to 2.5x faster and 3x more accurate quantum error correction decoding than current open-source benchmarks, with calibration workflows shrinking from days to hours.
  • The model family includes Ising Calibration, a 35-billion-parameter vision-language model, and Ising Decoding, a 3D convolutional neural network framework, both available on GitHub and Hugging Face.
  • Early adopters include Fermi National Accelerator Laboratory, Harvard, IQM Quantum Computers, Lawrence Berkeley National Laboratory, and the UK National Physical Laboratory.

NVIDIA Ising launched April 15, 2026, as the world’s first open-source AI model family purpose-built for quantum computing, providing researchers and enterprises with tools to address processor calibration and error correction, the two engineering barriers standing between today’s fragile qubits and large-scale useful quantum systems.

The models achieve up to 2.5x faster and 3x more accurate quantum error correction decoding compared to pyMatching, the current open-source benchmark.

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The family has two domains. Ising Calibration is a 35-billion-parameter vision-language model that automates quantum processor tuning, compressing calibration workflows that previously required days of manual setup to hours of automated execution. Ising Decoding is a 3D convolutional neural network framework for real-time quantum error correction, available in two variants optimized for either speed or accuracy depending on the application.

Both models are distributed through GitHub, Hugging Face, and NVIDIA’s build.nvidia.com platform, integrated with CUDA-Q and NVQLink. NVIDIA is also releasing a quantum workflow cookbook, training datasets, and hardware-specific fine-tuning tools so researchers can adapt the models to their own quantum processor architectures without exposing proprietary data.

Jensen Huang, NVIDIA’s founder and CEO, framed the launch in infrastructure terms. “AI is essential to making quantum computing practical. With Ising, AI becomes the control plane, the operating system of quantum machines, transforming fragile qubits to scalable and reliable quantum-GPU systems,” he said.

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Who Is Already Using It

Adoption at launch spans a range of institutions including Academia Sinica, Fermi National Accelerator Laboratory, Harvard’s John A. Paulson School of Engineering and Applied Sciences, IQM Quantum Computers, Lawrence Berkeley National Laboratory’s Advanced Quantum Testbed, Sandia National Laboratories, UC San Diego, the UK National Physical Laboratory, and Yonsei University.

The breadth of early adopters reflects a deliberate open-model strategy. By releasing pre-trained weights, training frameworks, and benchmarks publicly, NVIDIA positions Ising as a foundation layer that other developers can build on without starting from scratch.

Crypto and AI Market Implications

The Ising launch reinforces NVIDIA’s positioning as the dominant infrastructure provider across both classical AI and the emerging quantum-classical hybrid computing stack. For the crypto sector, quantum computing has long represented a future threat to existing blockchain encryption standards, particularly RSA and elliptic curve cryptography used to secure Bitcoin wallets.

Progress in quantum error correction, which Ising specifically targets, is the technical precondition for cryptographically relevant quantum computers to exist. The timeline remains distant, but every improvement in error correction decoding accuracy shortens it.

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NVIDIA news has historically triggered moves in AI tokens across the crypto market, as the chip company’s hardware underpins the AI infrastructure that powers many blockchain AI projects. The Ising launch adds a new quantum AI vertical to that relationship.

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Crypto World

With No Bipartisan Leadership, CFTC ‘Won’t Slow Down‘ on Rulemaking

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Government, CFTC, United States, Commodities Investment, Prediction Markets

The chair of the Commodity Futures Trading Commission (CFTC), Michael Selig, said he would not wait for the appointment of additional commissioners to lead the regulatory agency before moving ahead on rulemaking potentially related to digital assets and prediction markets.

In a Thursday hearing of the House Agriculture Committee, Selig responded to questions from ranking member Angie Craig, who called out the lack of leadership at the CFTC, which normally has a bipartisan panel of five commissioners. The Minnesota representative asked the chair to commit to not finalizing regulations while he is the only commissioner.

“In the interim, we cannot, for the sake of the American people, slow down in our rulemaking,” said Selig. “It’s very important that we get investor protections, consumer protections and safeguards for our markets. And so, I cannot, unfortunately, commit to not do my job that I was appointed to do by the president.”

Government, CFTC, United States, Commodities Investment, Prediction Markets
CFTC Chair Michael Selig speaking on Thursday. Source: US House Committee on Agriculture

Selig, who has served as the CFTC’s sole commissioner and chair since December, has come under scrutiny from many lawmakers for unilaterally leading the agency on rules favoring crypto and prediction markets with no bipartisan group of commissioners. As of Thursday, President Donald Trump had not publicly announced any nominations to staff the agency nor signaled he intended to do so.

“We’re going to do more through rulemaking,” said Selig in response to a question on the CFTC’s leadership from Representative Don Davis. “We can’t have the staff deciding on discretion what the rules are.”

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Related: CFTC probes oil futures trades tied to Trump’s moves in Iran: Report

The CFTC chair proposed rulemaking in March that could amend or issue new regulations over event contracts on prediction markets. Selig has been outspoken about claiming that the agency has “exclusive jurisdiction” over prediction markets as the companies behind some platforms face state-level lawsuits related to sports betting laws and proposed legislation to crack down on insider trading.

CFTC’s legal fight over prediction market continues

Gaming authorities in several US states have filed lawsuits against prediction market companies like Kalshi and Polymarket, alleging the platforms offered sports betting in violation of state laws.

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New Mexico Representative Gabe Vasquez questioned Selig at Thursday’s hearing with a visual aid showing that bets on event contracts and through state-level gaming “aren’t much of a difference, yet they are regulated completely differently.” He accused the CFTC of using “loopholes” to bypass state laws and requirements for prediction markets, causing some jurisdictions to miss out on revenue.

“The CFTC was not created or intended to regulate sports gambling,” said Vasquez, adding:

“Are we regulating real economic risk, or are we allowing prediction markets to steal billions of dollars in an unregulated free-for-all, with no consumer protection as Congress and the CFTC turns a blind eye?”

Companies like Kalshi have argued that they are under the sole jurisdiction of the CFTC. This argument led the company to court wins in Arizona and New Jersey, where this month judges blocked state officials from taking action against Kalshi.

Magazine: Should users be allowed to bet on war and death in prediction markets?

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