NVIDIA AI Podcast – Ep. 294: How AI Will Change Quantum Computing

Guest: Nick Harrigan, Product Marketing Manager for Quantum Computing, NVIDIA
Host: Noah Kravitz
Date: April 14, 2026

Episode Overview

In this episode, host Noah Kravitz and guest Nick Harrigan take listeners on an insightful journey into the intersection of artificial intelligence and quantum computing. The discussion breaks down foundational concepts in quantum computing, explores how AI is accelerating progress towards practical quantum applications, and unveils new AI models—like NVIDIA Ising—designed to help solve longstanding challenges, such as quantum error correction. The conversation offers both high-level context and deep technical insight, emphasizing why open AI models are vital and how the quantum revolution is rapidly becoming real.

Key Discussion Points & Insights

1. Quantum Computing 101: What Is It?

• Quantum vs. Classical Computing:
  • Classical computers run on transistors (bits: 0 or 1); quantum computers use qubits, which obey quantum laws and can exist in "superpositions."
    • "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..." (01:09, Nick)
• Potential:
  • Quantum computers aren't just "faster"—for some problems, they’re exponentially beyond classical capabilities.
    • "In many cases, it's so much faster that the problems just were not tractable at all." (02:14, Nick)
  • Ideal for simulating quantum systems—e.g., drug discovery, materials science—where standard computers hit a wall.

2. Current State of Quantum Computing

• Where Are We Now?
  • Moving from demonstration hardware to the R&D threshold of machines integrated into supercomputing.
  • Still cannot fully tackle crucial applications—quantum error correction remains a hurdle.
    • "We're transitioning from kind of experiments...to the larger-scale kind of systems...that can start to solve some of these really promising and important problems." (03:17, Nick)

3. The Core Challenge: Quantum Error Correction

• Noise is the Enemy:
  • Qubits are fragile, sensitive to noise/errors. Directly observing them destroys information ("you can't just look at them").
  • Quantum error correction is a process where some qubits are "sacrificed" to infer and correct errors in the rest, based on entanglement and delicate measurement.
    • "Quantum error correction seeks to do what seems impossible...to look at those qubits to find out if they're correct, but you don't want to touch them or look at them." (07:03)

4. AI’s Role in Quantum Advancement

• AI Unlocks Quantum’s Potential:
  • Decoding—the "Sherlock Holmes" detective work of error correction—demands enormous data throughput and inferential capacity; AI excels here.
    • "That inference algorithm, the Sherlock Holmes algorithm, is the decoder, and that's very hard. It needs to process terabytes of data...thousands of times every second." (07:40, Nick)
• Other AI Applications:
  • Hardware calibration (tuning for optimal performance)
  • Algorithm/application discovery ("thinking quantumly"—something AI might do better than people)
  • Accelerating the pace and accessibility of quantum R&D

5. Barriers to AI Integration

• Access to Open Models:
  • Quantum hardware varies widely; open, adaptable AI models are necessary so researchers can fine-tune for their specific systems.
    • "One of the biggest challenges...is just getting access to [AI] tools...They need very open tools because they might need to retrain or fine-tune those models for their specific kind of hardware." (10:31, Nick)

6. Transformative Applications of Quantum Computing

• Where Will Quantum Help First?
  • Early wins are expected in tasks deeply rooted in quantum mechanics, e.g., drug and materials simulation, where the systems being modeled are inherently quantum.
    • "If you're trying to develop a new drug...that's your candidate drug...understanding it well enough ... is deep down a quantum system." (11:33, Nick)
• Future Applications:
  • Many applications are “unknown unknowns;” AI might help discover entirely new quantum use cases.
    • "Quantum mechanics, quantum computing is very unintuitive to a human...AI is a great tool to understand the deeper patterns in quantum algorithms." (12:44, Nick)

7. Thinking Quantumly—and the AI Edge

• Even experienced quantum developers rely on familiarity and exposure, but AI might learn and "think" quantum much more efficiently.
  • "People who get very good at writing quantum algorithms, they just do it through like an extreme amount of exposure, which is why...AI could be very good at discovering new quantum applications." (15:01, Nick)

8. Introducing NVIDIA Ising: Open AI Models for Quantum

• What is Ising?
  • The first family of open AI models, built specifically for quantum computing workloads.
    • "This is the first set of open models specifically for quantum computing." (15:41, Nick)
• Capabilities at Launch:
  • Calibration: Visual language models to adjust quantum hardware.
  • Decoding: Models for error correction decoding, crucial for practical quantum systems.
    • "This really marks a change in how quantum research is going to be conducted." (16:50, Nick)
• Open, Pre-trained, and Customizable:
  • Researchers can use models “out of the box,” fine-tune, or further train with proprietary data.
    • "They can very quickly bring AI into their workflows...and really make them work specifically for their kinds of system." (25:15, Nick)

9. Quantum Data Demands & Future Directions

• Unique Data Challenges:
  • Though not as extreme as some AI data transfer challenges, quantum workflows can require processing up to 10 terabytes/second, with microsecond latencies.
    • "You need to be able to process 10 terabytes of data a second..." (17:05, Nick)
• AI/Quantum Synergy:
  • Quantum can also become a generator of high-quality data for AI (e.g., molecule interactions for training bio models).
  • Potential for new hybrid AI + Quantum workflows, agentic workflows, and automated discovery/design processes.
    • "It might be that quantum processes are an incredible source of otherwise effectively impossible to obtain data that you can train an AI on." (19:31, Nick)

10. Timelines and Scalability

• How Soon Will We See Real Quantum Impact?
  • Exact timing is unclear, but AI will be “a huge accelerator.”
    • "We don't know when it will be. But we know that the more advances we make...the much shorter that timeline is going to end up being." (20:03, Nick)
• Scaling Quantum:
  • Moving from “impressive but small” systems to scalable arrays of thousands/millions of qubits, leveraging hybrid supercomputing expertise.
    • "You can need like thousands, tens of thousands, hundreds of thousands, millions of qubits..." (20:40, Nick)

11. AI Everywhere—or Not?

• There will be quantum tasks where classical acceleration matters more than AI (e.g., certain simulations), but it’s worth exploring AI’s utility everywhere.
  • "Anywhere you can bring in AI, you will want to...But there are definitely problems in quantum computing where...you need accelerated computing, like, you need something to support it that might not necessarily be an AI model." (22:14, Nick)

12. Standardization & Ecosystem Growth

• Multiple qubit technologies means standards are still emerging; open tools and platforms (e.g., CUDA-Q, NVIDIA Ising, MVQ Link) will help steer this process.
  • "It's early days quantum...But one of the things that really will help...is having a powerful platform that people can use to start to integrate their qubits into existing supercomputing." (26:23, Nick)

13. Looking Ahead: Generative AI for Quantum

• Agentic Workflows:
  • AI agents can automate calibration, tuning, and even quantum algorithm generation, a bit like having Copilot or Claude/Nemo for quantum development.
    • "Agentic workflows are probably going to be really critical in controlling quantum hardware in ways that are just beyond the capabilities of humans..." (29:24, Nick)

Notable Quotes & Memorable Moments

• On Quantum’s leap vs. Classical Computing:
  • "In many cases, it's so much faster that the problems just were not tractable at all." (02:14, Nick)
• On Quantum Error Correction:
  • "Quantum error correction seeks to do what seems impossible...but at the same time, you don't want to touch them or look at them." (07:03, Nick)
  • "That inference algorithm, the Sherlock Holmes algorithm, is the decoder, and that's very hard...you have to do it thousands of times every second." (07:40, Nick)
• On AI Unlocking New Quantum Applications:
  • "Our brains...they're not quantum mechanical...it might be that an AI, deep down, is a great tool to understand the deeper patterns in quantum algorithms." (12:44, Nick)
• On the Ising Models:
  • "This is the first set of open models specifically for quantum computing." (15:41, Nick)
  • "They can start to retrain them or fine-tune them and they can really make them work specifically for their kinds of system." (25:15, Nick)
• On Timelines:
  • "Everyone wants to know, right? How far until we get a quantum computer?...the more that we can bring AI...the much shorter that timeline is going to end up being." (20:03, Nick)
• On Open Ecosystem:
  • "Having open models really opens access to this whole broad quantum ecosystem." (10:31, Nick)

Timestamps for Key Segments

• 01:02 – 01:55: Intro to quantum computing and its unique capabilities vs. classical computers.
• 04:00 – 05:35: Technical challenges; focus on quantum error correction.
• 07:03 – 08:37: Deep-dive into quantum error correction and what makes it so complex.
• 09:29 – 10:55: The crucial need for open AI models in a diverse hardware landscape.
• 11:16 – 12:44: Early and future applications for quantum computing and AI’s role in "discovering the undiscoverable."
• 15:38 – 17:05: The launch of NVIDIA Ising and what sets it apart for the community.
• 17:05 – 19:31: Data throughput, latency, hybrid use cases—quantum generating training data for AI.
• 20:03 – 21:56: When will true quantum impact be felt? Scaling hardware and timelines.
• 23:38 – 24:26: The importance of benchmarking and open model performance validation.
• 27:49 – 29:24: AI in optimizing/creating quantum algorithms; LLMs/generative AI for quantum.
• 29:24 – 30:46: Agentic workflows and the future of automated quantum development.

Further Resources

• NVIDIA Ising Open Models: build.Nvidia.com
• CUDA-Q Platform (for hybrid quantum-classical development): Available on GitHub and NVIDIA developer channels
  • "It's really a great time to start experimenting with this and an exciting time to accelerate research." (30:46, Nick)

Conclusion

This episode paints an exciting—if "mind bending"—picture of how AI is set to accelerate humanity’s path to useful quantum computing, surmount core technical barriers, and unlock brand new scientific and industrial possibilities. The creation and release of the NVIDIA Ising open model family is poised to empower a wider segment of quantum researchers and developers, seeding the next wave of breakthroughs at the intersection of AI and quantum.