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NVIDIA AI Podcast

How AI Will Change Quantum Computing - Ep. 294

31 min episode · 2 min read
·
Nick Harrigan

Episode

31 min

Read time

2 min

Topics

Relationships, Startups, Fundraising & VC

AI-Generated Summary

Key Takeaways

  • Quantum Error Correction Bottleneck: Quantum processors require a classical "decoder" algorithm running thousands of times per second, processing terabytes of data with sub-microsecond latency to correct qubit errors. Without hitting these thresholds, the quantum processor fails entirely. AI models trained specifically for decoding can meet these demands where traditional methods struggle to keep pace.
  • NVIDIA ISING Open Models: NVIDIA released the first open AI model family built specifically for quantum computing workloads. At launch, ISING includes two model types: a visual language model for hardware calibration that reads measurement outputs and applies corrections autonomously, and a decoding model for quantum error correction. Both are available at build.nvidia.com with retraining recipes included.
  • Qubit Scaling Requirements: Fault-tolerant quantum computers require millions of physical qubits because error correction demands sacrificing many qubits to protect others. Current systems remain far below this threshold. Researchers can close this gap faster by offloading classical control tasks — calibration, decoding, error correction — to GPU supercomputers running AI models rather than custom-built classical hardware.
  • AI-Generated Quantum Algorithms: Generative AI models can construct quantum circuits the same way LLMs build sentences — predicting which quantum gate operation follows each prior step to produce a desired computational outcome. This approach to automated quantum algorithm discovery and compilation addresses the core challenge that humans cannot intuitively think in quantum mechanical terms.
  • Quantum-AI Data Pipeline: Near-term quantum processors can generate highly accurate molecular simulation data — otherwise computationally impossible to obtain classically — which can then train AI models for pharmaceutical and materials science applications. This positions early quantum hardware not as a standalone compute platform but as a specialized data source for AI training pipelines.

What It Covers

NVIDIA product marketing manager Nick Harrigan explains how quantum computing works, why qubits require constant error correction processing terabytes of data per second, and how NVIDIA's newly released open model family called ISING uses AI to accelerate quantum hardware calibration, error correction decoding, and algorithm development toward fault-tolerant quantum systems.

Key Questions Answered

  • Quantum Error Correction Bottleneck: Quantum processors require a classical "decoder" algorithm running thousands of times per second, processing terabytes of data with sub-microsecond latency to correct qubit errors. Without hitting these thresholds, the quantum processor fails entirely. AI models trained specifically for decoding can meet these demands where traditional methods struggle to keep pace.
  • NVIDIA ISING Open Models: NVIDIA released the first open AI model family built specifically for quantum computing workloads. At launch, ISING includes two model types: a visual language model for hardware calibration that reads measurement outputs and applies corrections autonomously, and a decoding model for quantum error correction. Both are available at build.nvidia.com with retraining recipes included.
  • Qubit Scaling Requirements: Fault-tolerant quantum computers require millions of physical qubits because error correction demands sacrificing many qubits to protect others. Current systems remain far below this threshold. Researchers can close this gap faster by offloading classical control tasks — calibration, decoding, error correction — to GPU supercomputers running AI models rather than custom-built classical hardware.
  • AI-Generated Quantum Algorithms: Generative AI models can construct quantum circuits the same way LLMs build sentences — predicting which quantum gate operation follows each prior step to produce a desired computational outcome. This approach to automated quantum algorithm discovery and compilation addresses the core challenge that humans cannot intuitively think in quantum mechanical terms.
  • Quantum-AI Data Pipeline: Near-term quantum processors can generate highly accurate molecular simulation data — otherwise computationally impossible to obtain classically — which can then train AI models for pharmaceutical and materials science applications. This positions early quantum hardware not as a standalone compute platform but as a specialized data source for AI training pipelines.

Notable Moment

Harrigan explains that observing a qubit directly destroys its quantum state, making error detection seemingly impossible. The solution, discovered in the 1990s, involves deliberately sacrificing some entangled qubits to infer errors in the remaining ones — a workaround that convinced researchers quantum computers could actually be built.

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Episode Transcript

And this has been a huge missing part of the quantum computing community. Access to open AI models to really use the latest in AI technology to help us accelerate how we get to these useful quantum applications. Welcome to the NVIDIA AI podcast. I'm Noah Kravitz. A quick note before we begin, you can now watch the AI podcast in full video. Check us out on the NVIDIA YouTube page. And of course, if you prefer the audio only feed, you can still get us wherever you get podcasts. Nick Harrigan is here. Nick is a product marketing manager for quantum computing at NVIDIA, and we're here to talk about the state of quantum computing, what AI means for quantum computing, and really all kinds of things that sound like science fiction until you hear Nick explain them. So Nick, thank you so much for joining the AI podcast. Glad to have you here. Thank you for having me. Excited to talk about it. So maybe we can start with the basics. Can you kind of give an overview for us of what quantum computing is and kind of the state of play of things right now? Yeah. Absolutely. So quantum computing is a new kind of way to build computing technology. So everything we have today in computers, which is incredible, what you can do with computing today, is fundamentally based on a transistor, a special kind of switch that can be zero or one. Quantum computing kind of asks, what if your switch was a quantum mechanical object? Something that obeys quantum laws of physics can do very strange things. And what if you rebuild how you compute based on that? And so if you do that, it turns out, if you can build such a device and you can integrate it into a supercomputer, you can start to solve problems with computing that we just wouldn't have even thought of as being addressable. They were just too hard or outside of the scope of computing. So it's really a new kind of technology that augments our existing kind of GPU supercomputers with whole new capabilities. And is it that and you're gonna have to correct me here as we go. But to kind of break it way down, is it that quantum allows for faster computation or more computations at once? Something different? How how does it work in that regard? Yeah. So in in some application areas, it might be that it can perform some things faster than a conventional computer can. But that really undersells the difference. In many cases, it's so much faster that the problems just were not tractable at all. It wasn't that, you know, today's technology was a bit slow. Mhmm. Maybe some future generation would be. In some cases, quantum computing can give us a kind of exponential or even, like, a very strong polynomial advantage over a normal computer. Meaning that as you make the problem you're trying …

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Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links. As an Amazon Associate, SignalCast earns from qualifying purchases.

Tools

  • build.nvidia.comRecommended

    by NVIDIA

    Both are available at build.nvidia.com with retraining recipes included.

Products

  • NVIDIA ISINGRecommended

    by NVIDIA

    NVIDIA released the first open AI model family built specifically for quantum computing workloads. At launch, ISING includes two model types: a visual language model for hardware calibration that reads measurement outputs and applies corrections autonomously, and a decoding model for quantum error correction.

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