Bloomberg Technology Special Live From GTC

Summary

Bloomberg Technology Special: Live From GTC – March 20, 2025

Episode Overview

This special edition of Bloomberg Technology broadcasts live from Nvidia’s GTC (GPU Technology Conference) in San Jose, focused on quantum computing and its intersection with AI and accelerated computing. Host Ed Ludlow moderates conversations with industry leaders, including Nvidia’s Tim Costa, IonQ’s Peter Chapman, and D-Wave’s Alan Baratz. The main theme is examining the state of quantum computing, Nvidia’s role as an “accelerator” rather than a quantum computer builder, and a spirited debate over what “useful” quantum computing looks like—and when it will arrive.

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Key Discussion Points and Insights

1. Nvidia’s Role in Quantum Computing

Guest: Tim Costa (Senior Director of Quantum Computing, Nvidia)
Timestamps: [03:02]–[09:06]

• Nvidia’s Vision & Participation

  • Nvidia does not build or sell quantum computers, but provides critical architecture, GPUs, software, and support to quantum companies.
  • Their focus: Integration of quantum processors as data center components alongside CPUs, GPUs, and memory.
  • “Quantum technology offers promise to be very good at certain kinds of computation... we’re focused on helping quantum companies to better develop those technologies.” — Tim Costa [03:02]

• Supporting the Industry

  • Nvidia partners with over 160 quantum-focused groups, accelerating research, simulation, error correction, and device calibration.
  • Quantum devices, tightly integrated with GPUs, show promise in heterogeneous computing—each technology focuses on what it does best.
  • “These are, in some ways, physics experiments upon which we try to compute as much as a computer. Managing that physics experiment ... is actually a complex computational task.” — Tim Costa [03:02]

• The AI-Quantum Symbiosis

  • AI aids quantum by handling device calibration and complex error correction. Conversely, quantum computers could output unique physics data, valuable for AI model training.
  • “There are two sides of AI and quantum... AI for quantum... [and] quantum outputs as training data for AI models.” — Tim Costa [06:15]

• Establishing a Quantum Research Center

  • Nvidia announces a Boston research center to further quantum-accelerated supercomputing.
  • The center will let partners work directly on integrating quantum devices with Nvidia’s infrastructure, focusing on real-world quantum-accelerated computing.
  • “If we’re going to build a new kind of computer... that is a physical endeavor, it has a footprint, a place to do it.” — Tim Costa [07:36]

• Defining “Useful” in Quantum Computing

  • Tim Costa highlights chemistry and biochemistry as the first likely domains for practical quantum breakthroughs.
  • “I do think... chemistry, biochemistry related areas... will be the first area that’s disrupted, or at least I do.” — Tim Costa [08:19]

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2. IonQ’s Perspective: Demonstrating Progress & Commercialization

Guest: Peter Chapman (Executive Chairman, IonQ)
Timestamps: [11:38]–[16:24]

• Recalibrating Expectations

  • The session opens by addressing Nvidia CEO Jensen Huang’s earlier, controversial statement: Useful quantum computing is “a decade or more away.” Chapman and others use the day to “bring that timeline in.”
  • “He said on stage, I think twice it was his mea culpa... probably the first CEO in history to invite a panel of people to tell him that he was wrong.” — Peter Chapman [11:51]

• Practical Applications & Partnerships

  • Chapman highlights collaborations with Nvidia, AWS, AstraZeneca, and Ansys:
    • A recent demonstration yielded a 20x improvement on earlier benchmarks.
    • A partnership with Ansys produced a 12% performance gain using IonQ’s quantum computers.
    • “While those numbers are not enough to take over the market...that we did it on our 36-qubit system is remarkable.” — Peter Chapman [12:00]

• Use of Nvidia Technology at IonQ

  • IonQ employs Nvidia DGX GPU clusters for quantum computer design and for simulating small-qubit systems (up to ~35 qubits); higher counts require exponentially more resources.
  • “For small qubit counts...we run the simulation on a GPU... when you get to 64 qubits, you need two and a half billion GPUs...” — Peter Chapman [13:46]

• Pushing Toward Commercialization

  • Chapman agrees with Huang: early “usefulness” must be revenue-generating—chemistry and engineering are the likeliest early customers.
  • “You need to find a set of applications early on that you can start to make money on and...power your R&D.” — Peter Chapman [14:27]

• Handling Market Skepticism

  • Chapman responds to a recent short-seller report as part of being a public company, downplaying its impact.
  • “It’s a short report... these are the kinds of things you have to endure.” — Peter Chapman [15:07]

• Industry Momentum

  • Chapman compliments Nvidia for giving the sector a platform, but cautions that real progress will come from execution, not just messaging.
  • “What really matters is actually going and doing it, not actually getting the message out.” — Peter Chapman [15:49]

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3. D-Wave’s Contrarian View: Quantum Usefulness is Here

Guest: Alan Baratz (CEO, D-Wave)
Timestamps: [18:47]–[23:47]

• Disagreeing with the Decadal Timeline

  • Baratz maintains that D-Wave’s unique “annealing” approach already delivers practical results, contrary to Nvidia’s long-term timeline.
  • “We are actually able to support useful, important applications today.” — Alan Baratz [18:47]

• Scientific & Commercial Use Cases

  • D-Wave published results in Science: calculating properties of magnetic materials that classical computers cannot, enabling swift materials discovery.
  • Demonstrated quantum-powered blockchain/proof-of-work, enabling energy-efficient crypto mining.
  • “We can now create a blockchain that uses a quantum computer to do the proof of work...” — Alan Baratz [19:47]
  • “Cryptocurrency mining could be at a fraction of the energy cost...” — Alan Baratz [19:53]

• Readiness & Timelines

  • D-Wave is already fielding prototypes for quantum blockchain and commercial clients (e.g., NTT DoCoMo for cell tower optimization).
  • Baratz estimates blockchain commercialization is a year or two away—not 10–20 years.
  • “I think we’re looking at a year or two.” — Alan Baratz [20:30]

• Distinct Approach from Nvidia’s Narrative

  • D-Wave’s annealing systems don’t require the GPU-powered error correction that Nvidia offers; they self-calibrate their large (5,000+ qubit) machines.
  • For D-Wave’s gate-model (more conventional) research, Nvidia’s offerings may be more relevant.
  • “We calibrate them ourselves. We don’t need GPU power to calibrate those systems. So currently we do not.” — Alan Baratz [21:57]

• Reflections on Industry Debate

  • Baratz is ambivalent about the value of the GTC debate for the industry, emphasizing maturity and practical value over narrative control.
  • “I don’t think this event was all that helpful... We're still at the beginning of a learning curve with respect to how Nvidia and Jensen interact with quantum computing companies.” — Alan Baratz [23:21]

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Notable Quotes & Memorable Moments

• Jensen Huang (Nvidia CEO), on January 8th’s Infamous Caution:
  “We were more than a decade away from quantum computers being able to do anything useful.”
  [Referencing earlier comments, context at [01:08], direct quote replayed [02:01]]

• Tim Costa (Nvidia) on Nvidia’s role:
  “We do not build a quantum computer, but we have a vision about where quantum computing will be useful...” [03:02]

• Peter Chapman (IonQ) on Timelines and Product Milestones:
  “The fact that we managed to do it on our 36-qubit system is really quite remarkable.” [12:00]
  “You need to find a set of applications early on that you can start to make money on...” [14:27]

• Alan Baratz (D-Wave) on Quantum Usefulness:
  “We are useful today. Blockchain is one of our newest application areas. And yes, we’re just starting to roll that.” [21:07]
  “I don’t think this event was all that helpful to the industry or to D-Wave.” [23:21]

• Ed Ludlow (Host) on Defining ‘Useful’:
  “I don’t think... we’ve reached a definitive agreement on what useful is.” [07:07]

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Important Segment Timestamps

• [03:02]–[09:06]: Tim Costa (Nvidia) on supporting quantum innovation, integration, and defining quantum “usefulness”
• [11:38]–[16:24]: Peter Chapman (IonQ) on timeline revision, practical breakthroughs, commercialization, and dealing with market critiques
• [18:47]–[23:47]: Alan Baratz (D-Wave) on real-world applications, differentiating D-Wave’s approach, and what’s needed for industry credibility

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Summary Table: Companies & Perspectives

| Company | Position on Usefulness | Nvidia Relationship | Key Use Cases Highlighted | |-------------|-----------------------------|----------------------------------|------------------------------------| | Nvidia | “Useful” is years off, but integration is imminent | Provides architecture, software, GPU access; supports quantum ecosystem | Simulation, calibration, error correction, chemistry, AI models | | IonQ | Timeline is shortening; demonstrated partial wins, needs revenue use cases | Leverages Nvidia GPUs for design/simulation | Chemistry, engineering, Ansys partnership, R&D-focused | | D-Wave | Useful quantum is here, via annealing architecture | Minimal dependency; self-sufficient calibration | Scientific research (magnetics), blockchain, NTT DoCoMo partnership |

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Overall Conclusion

This GTC special underscores both the dramatic progress and persistent debate in quantum computing. Nvidia aims to accelerate quantum’s arrival by supporting the entire ecosystem but maintains a cautious outlook on commercialization timelines. IonQ seeks to bridge the lab-market gap, demonstrating incremental progress and partnerships. D-Wave argues forcefully that its annealing approach is already unlocking real-world value, challenging the dominant industry narrative.

The episode is a snapshot of an industry in lively transition, balancing optimism, rivalry, and the relentless push for real, scalable quantum utility.

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For further listening: Find the full episode on the Bloomberg Terminal or wherever you get your podcasts.

Transcript

[A]

Hi, I'm Lisa Mateo, introducing you to the new Stock Movers report from Bloomberg. These are short audio reports, five minutes or less, delivered right to your podcast feed. Throughout the day, Stock Movers fills you in on the day's winners and losers on Wall street and tells you about the news and data that's driving those gains and losses. If you want to stay plugged into the stock market but don't want to spend all day watching tickers scroll across your screen, then Stock Movers is a place for to get informed. Listen a couple times throughout the day to find out what's moving equities and why. Search for Stock Movers on Apple podcasts, Spotify or anywhere else you listen. Get the latest stock news and data backed by reporting from Bloomberg's 3,000 journalists and analysts across the globe. Subscribe to Stock Movers wherever you get your podcasts.

[B]

Bloomberg Audio Studios Podcasts, Radio news yeah.

[C]

Welcome to this special edition of Bloomberg Technology. I'm Ed Ludlow. We're live in San Jose at Nvidia's GTC conference, where the major focus today has been quantum computing. Over the next 30 minutes or so, we're going to speak to Nvidia's senior director of Quantum, Tim Costa, as well as the leaders of quantum computing companies Ionq and, and D Wave. And actually, I want to look at the shares of those companies. There was some downward pressure on a number of quantum computing stocks today. I flagged that only because that pressure continued as they were speaking on stage alongside Nvidia CEO Jensen Huang. Now, remember, quantum stocks went into free fall on January 8th after Nvidia's CEO said we were more than a decade away from quantum computers being able to do anything useful. Now, fast forward to today and Huang had this to say about those earlier comments. Have a listen.

[D]

I'm a public company CEO, and every so often someone asks me a question. And most of the time, Most of the time. Well, some of the time I'm going to try to lower the bar here. Some of the time I say something right and sometimes it comes out wrong.

[C]

Now, here's the thing. In the world of technology, historically at least, these have been two distinct fields, quantum computing and accelerated computing, or supercomputers for AI. But increasingly those worlds are coming together. Joining me now is Tim Costa, who's the senior director for quantum computing at Nvidia. And I think a really important place to start is what Nvidia does and what Nvidia does not do. Sure, Nvidia does not make quantum computers, nor does it sell quantum computers, but it does provide architecture software and services to that industry. Just explain your role, please.

[D]

Yeah, so you nailed it on the head with the easy part, which is what we don't do. We do not build a quantum computer, but we have a vision about where quantum computing will be useful. And it's really a point at which we have the integration of larger scale quantum technologies. Quantum processors as part of data centers and large scale computing looks very similar to what we have today. Right. If you look at today's large scale computing infrastructure, it's CPUs and GPUs and storage and memory and interconnects, and it's very complex but very heterogeneous. And each part plays the role that it's best suited for, including the CPU and including the gpu. Quantum technology offers promise to be very good at certain kinds of computation. And so that will be an additional element of that system and come in. And so what we're focused on is really helping A, helping the quantum companies who are building those technologies to better develop those technologies because we're interested in solving the problems that it will be able to solve, but also B, setting up that infrastructure to be the best partner to that quantum device, to be able to do things like error correction, calibration of the devices. These are in some ways things you can think about as physics experiments upon which we try to compute as much as a computer. And so managing that physics experiment and getting the right results out of it is actually a complex computational task that we look to our accelerated supercomputers to actually perform.

[C]

What we are literally talking about here is a computer scientist or engineer at a quantum computing company with access to some of your GPUs in whatever form factor can just be the single GPU could be scaled up to that server design, which we know is dgx. What's the market there? How widely is your technology being used in parallel with many of the quantum names that we saw today?

[D]

Yeah. So who we saw on stage today was a great selection of our partners who are all engaged with and who all are using GPU technology as well as accelerated computing technology, including the software stacks and other components that we develop in their research program to simulate their devices and build better versions of their QPU's to work with their clients and work on algorithm design by simulating a quantum computer and also doing the fundamental research to drive towards that vision that I just discussed a minute ago, where we actually have this tight integration of these devices together that involves work on the interconnect between the QPU and the gpu. That involves developing new methods for error correction that can be deployed at scale on a large supercomputer using novel AI methods, among many other areas. But those are not our only partners in this ecosystem. We're working with over 160 groups in Quantum computing and the range of application areas is quite wide. But they're all using Nvidia technology to accelerate their work, because that's what we're ultimately here to do.

[C]

So we started the day with the idea that Nvidia's technology can help quantum computers accelerate their own timeline, reach that useful metric. But the idea raised and put to Jensen Huang by those partners was actually the output of a quantum computer. In other words, the computation can work both ways. It could be submitted for use in the training of a foundation model. What do you make of that?

[D]

Yeah, so I think that there's two sides of AI and quantum, and they're both really important and really fascinating. And what I touched on a few minutes ago was the AI for quantum using AI models to actually control and error correct larger and larger and more capable quantum devices. It's incredibly important and it pulls in the timeline on useful quantum computing.

[C]

Yes.

[D]

Now, the other side that was brought up today, and it's a fascinating topic, is, you know, what is a quantum computer? If you start to boil it down, and I won't try to go too far down, but they're really physics experiments. As I said, you're modeling quantum physics in the device. And so they're able to potentially provide data to train and fine tune models for understanding the very phenomena which are inside of a quantum computer in a way which will answer questions that humanity has been unable to answer. So we think that that's a really interesting and exciting area to pursue, and we're engaged with our partners across all these different areas.

[C]

I don't think that in the context of all the attendees I've spoken to today and Jensen and the panelists that we've come kind of reached definitive agreement on what useful is. But I think we've definitely reached agreement within your industry that this accelerates what's happening, pardon the pun, accelerated quantum computing. What happens next for Nvidia and that their footprint in quantum. There is going to be a research center in Boston, that those can often be abstract things. But why is that an important step?

[D]

It's an important step because we're going to one of the things that we have to do if we're going to build a new kind of computer and if we add a quantum accelerator to a computer like what we build today, that is a new kind of computer, you're adding a new compute element that is a physical endeavor, it has a footprint, is a place to do it. Now, the center in Boston won't be the only place that that happens in the world, but it's a place where some of our partners and us can work on developing the interconnect, developing the air correction technologies, literally string up their quantum devices to our GPUs and build the first versions of these quantum accelerated supercomputers that we're all working towards.

[C]

Your view on what is useful, what do you think that a quantum computer will be able to achieve, whether it's assisted by Nvidia or not?

[D]

Sure. I think that one of the most important things that Jensen talked about today on stage was really narrowing the focus and deciding what the one the problem is so that you can define success and chase after it. I do think that there's fairly wide agreement in the community that one of the first areas is to be to be accelerated and to have new kinds of problems solved that weren't before is in chemistry, biochemistry related areas. I mean, there's some kind of sniff test this passes, right? You've got basically quantum physics in the quantum device and the ability for that to model quantum physics in terms of what's required for very accurate chemistry just kind of makes sense. So we think that that's going to be the first area that's disrupted, or at least I do. I'm sure there's people on my team who disagree with me. As, as we saw today from the panelists, there are a wide variety of opinions on everything in quantum, but we think that's promising.

[C]

Well, we're grateful for yours. Tim Costa, senior director of Quantum computing at Nvidia, thank you very much. A lot more coming up. We speak with IonQ Executive Chairman Peter Chapman. That's next. We'll be right back. This is Bloomberg Technology. Hello, I'm Caroline Hoepke. And I'm Stephen Carroll. Every weekday morning, we bring you the Bloomberg Daybreak Europe podcast. It's 15 minutes to get your day started. All the top global news stories, how they affect the economy and your finances.

[D]

We have to understand the changing world.

[C]

When it comes to trade.

[E]

Wage growth is still really, really high.

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The disinflation process is well on track these days. A lot can change overnight. So we're here first thing every morning to bring you up to date. We have thousand journalists around the world to tell you what happened and why it matters. US Exceptionalism is being questioned heavily.

[B]

Some damage is done.

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[B]

Economic stability is non negotiable.

[C]

At Bloomberg we say that context changes everything. That means the data and expertise to help you understand a world that's changing at breakneck speed. So join us every weekday morning for the Bloomberg Daybreak Europe podcast. Subscribe today to get us in your feed by 7am foreign welcome back to a special Bloomberg Technology at Nvidia's GTC Quantum Day. Ionq was one of the quantum computing companies invited on stage today alongside Jensen Huang here at GTC Quantum Day. The company's stock was one of Those hardest hit January 8th after Nvidia's Jensen Huang made those comments about quantum computing and its usefulness being, quote, a decade or more away. Since then, IonQ has gone through some changes, appointing Nicola de Massi as its new CEO. The former CEO Peter Chapman continues as executive chair and I'm delighted to say joins us now on set. And what was interesting in the conversation with Jensen Huang is that you were balanced in saying I'm not. I don't think we necessarily agree on everything here. Still, let's start with the main point, which is did we define usefulness and do you have a sense that Jensen Huang has changed his timeline of when he thinks usefulness of your industry will be achieved?

[F]

Well, I think today was kind of the purpose of today was to bring that timeline in to kind of take back what it is that he had said before he said on stage, I think twice it was his mea culpa.

[C]

Yes.

[F]

Right. So that was their purpose of today's.

[C]

Kind of he said that he would be the first CEO probably in history to invite a panel of people to tell him that he was wrong.

[F]

He was wrong is exactly so. And it was funny today just in in general, we've been working with Nvidia on a demonstration for today that was with Nvidia, AWS and AstraZeneca where we gotten a 20x improvement on what we had done previously. Also we had taken with Ansys with a product that they do, which is normally run on GPUs and managed to get a 12% increase in using our quantum computers. So while those numbers are not really enough to take over the market, because usually you need one or two orders of magnitude to be disruptive to a market, the fact that we managed to do it on kind of our 36qubit system is really quite remarkable.

[C]

Nvidia has a quantum computing business insofar as and we got into it during the panel that you have access to architecture, DGX, their high performance GPUs, also some, some software and open source solutions. And what they would say is that that that will help your engineers and computer scientists calibrate, reduce error counts, design better. Is that how it actually plays out for you?

[F]

Some aspects, we have a DGX cluster that we use for design, for designing other quantum computers. Not so much in the error correction aspect. That's something we do ourselves. But certainly we use, you know, a number of GPUs for designing the quantum computer itself. We also for small qubit counts, because as soon as you get into beyond about 35 qubits, you can no longer simulate one of these on a gpu.

[C]

Right.

[F]

So for those, we actually run the simulation on a GPU just to make sure our hardware is working correctly. The problem is when you get to 64 qubits, you need two and a half billion GPUs because for each qubit.

[C]

You add, performance essentially doubles. Right, Right.

[F]

And it means that the matrix math that you have to do suddenly is doubling as well. So basically, is it about 35 qubits to fully simulate it, you can only get it on a single digit x100.

[C]

Peter, I got to hold you to this and I wonder if it ties into the new CEO and you know, sort of reset a little bit. There's a difference between lab experiment and commercial use. You know, making money, revenue generation, that's the question I get for you most.

[F]

Yes. So what Jensen said, we actually. One area we definitely agree on, which is you need to find a set of applications early on that you can start to make money on and to build that firewheel to be able to power your R and D. And so the examples we're talking today, for instance in the chemistry application and also with answers, is exactly those kinds of things. So that's exactly what our plan is.

[C]

Peter, you are the executive chairman now. You were CEO. One week ago, Kerrysdale issued a short report on your stock on your company. I just want to give you a chance to respond as we've not had a chance to speak since then.

[F]

Sure. It's a short report. So their goal is obviously to try to cast doubt about the company and make money that way. Unfortunately, it's just if you're a public company, these are the kinds of things you have to endure. We don't put much credence to those things.

[C]

The last thing I want to ask you is about how big today was in the change of trajectory or momentum for your industry. Jensen Huang is a character and he was honest on stage. But GTC is an incredible event. It has scale, it has eyeballs. Do you think you'll see something of substance come out of this for your company, for your sector?

[F]

You know, it's an interesting, it's certainly Jensen's goal was to give us the microphone today to be able to get out our story. And that's certainly important. But I say if you were Sam Altman 5 years ago trying to convince the world that AI is coming, probably he wouldn't have been successful. And the question is, is how much time should Sam Altman try to convince the world AI is coming instead, just go back and actually make it happen. So in that sense, actually, it's probably not that significant. What really matters is actually going and doing it, not actually getting the message out.

[C]

IQ Executive Chairman Peter Chapman, thank you for your time here in San Jose at GTC. Okay, much more to come. Alan Baratz, CEO D Wave, another one of the quantum computing CEOs on stage joins us. And they have an example of success or something useful. Blockchain architecture progress. That's next. This is Bloomberg Technology.

[B]

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[C]

Welcome back to a special edition of Bloomberg Technology live at Nvidia's GTC Quantum Day. So shares of some quantum computing companies sank today after leaders spoke at an event with Nvidia CEO Jensen Huang. The plunge follows a similar move back in January after Huang said it would be more than a decade before the technology quantum computing could do something that's useful. One of those quickest to Say in the month of January that Huang was wrong was D WAVE CEO Alan Baratz. D WAVE shares under pressure today for whatever reason. But, Alan, I'm grateful for your time, and I think it's fair to say that among the panelists, you maintain those areas that you don't agree with Jensen Huang on. How in any way is your mind changed on those differences through the course of today?

[E]

Well, my mind has not changed. The fact of the matter is that we at D Wave have taken a very different approach to quantum computing from everybody else in the industry. And as a result of that, we are actually able to support useful, important applications today. And I think the best example of that is the paper that we published in Science last week, where we have demonstrated that we can compute properties of magnetic materials that just cannot be computed classically. And this gives us the opportunity to create new materials discovery platforms, which will dramatically reduce the time and cost to create new materials. And that seems pretty useful to me.

[C]

Yeah, there's also some. Some applications that the audience might find harder to understand. For example, blockchain architecture. Why is a quantum computer able to improve that process where a supercomputer classically coded in ones and zeros cannot?

[E]

So basically what we did was we were able to show that that same computation that we use to compute properties of magnetic materials could be used to basically compute the hashing functions that are used in blockchain and cryptocurrency. What this means is that we can now create a blockchain that. That uses a quantum computer to do the proof of work. What's so important about that is that quantum computers consume far less energy than classical computers. So this means cryptocurrency mining could be at a fraction of the energy cost of what we're seeing today.

[C]

When are those two case studies revenue generators for you?

[E]

Well, the first prototype of this blockchain is right running right now. We have it running on four of our quantum computers. It's the first distributed quantum application where each of the quantum computers can create hashes or validate hashes, basically run the proof of work algorithm. And we're now in the process of building that out so we can get to the point where we can support a full commercial blockchain. How long do I think that'll take? I think we're looking at a year or two.

[C]

Not the 10 or 15, 20 years?

[E]

Absolutely not the 10 or 15 or 20 years.

[C]

So there is work that Nvidia.

[E]

But by the way, I mean, that's just one application, right? I mean, the Materials discovery that's running today. We have customers like NTT DoCoMo that are using us today for cell tower resource optimization. So we are useful today. Blockchain is one of our newest application areas. And yes, we're just starting to roll that.

[C]

The point of difference, I think, I still think is. Is Jensen's definition of usefulness?

[F]

Perhaps.

[C]

But you know, Nvidia does do work with your industry. To summarize, it's basically offering GPU access on the architecture side as well as some research and open source facilities on the other side. And their argument is you can take that and use it to make your quantum computers better. Calibration, error count reduction, and I think your colleagues mentioned design. How do you work with Nvidia?

[E]

Okay, so annealing quantum computers do not have the same error correction requirements as gate model quantum computers. So we are solving useful problems today without error correction. And as a result, that component of what Nvidia brings to the table is not all that important to us today. Today, when it comes to calibration, we have the largest quantum computers in the world. Our current systems are at 5,000 qubits and growing. We calibrate them ourselves. We don't need GPU power to calibrate those systems. So currently we do not. I mean, I know Jensen says he works with all the quantum computing companies, but D Wave is quite different. We are a different, We've taken a different approach. We are at a different level of maturity, much more mature than the other quantum computing companies. We, we are delivering useful applications and useful value today. Now that having been said, we are also developing a gate model quantum computer, the approach that everybody else has taken. For that effort, we will be looking to leverage some of the same sorts of things that the other quantum computing companies are leveraging. But we view Annealing and Gate as very complementary. They solve different classes of problems.

[C]

We only have 30 seconds. It was a pretty public disagreement, argument, debate. Will it help you in the long run what happened today here in San Jose?

[E]

I don't think this event was all that helpful to the industry or to D Wave. I think I thank Jensen for the opportunity to participate. I think that it was great to have the opportunity to try to get the message out, but I think, I think we're still at the beginning of a learning curve with respect to how Nvidia and Jensen interact with quantum computing companies.

[C]

D Wave CEO Alan Baratz, thank you for your time here in San Jose. Whether you agree with your, your host or not. Well, that does it for this special edition of Bloomberg Technology a lot to recap, particularly when it comes to quantum computing. So don't forget our podcast. You can find it on the Bloomberg Terminal as well as online on platforms like Apple, Spotify, and Iheart. Live From San Jose, California at GTC Nvidia's Quantum Day. This is Bloomberg Technology.