WEBVTT - BONUS: Nvidia GTC Quantum Day Special

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<v Speaker 1>Bloomberg Audio Studios, Podcasts, radio News.

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<v Speaker 2>Yeah, welcome to this special edition of Bloomberg Technology. I

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<v Speaker 2>am Ed Ludlow. We're live in San Jose and in

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<v Speaker 2>videos GtC conference, where the major focus today has been

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<v Speaker 2>quantum computing. Over the next thirty minutes or so, we're

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<v Speaker 2>going to speak to Nvidia's senior director of Quantum, Tim Costa,

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<v Speaker 2>as well as the leaders of quantum computing companies ion

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<v Speaker 2>Q and d Wave. And actually I want to look

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<v Speaker 2>at the shares of those companies. There was some downward

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<v Speaker 2>pressure on a number of quantum computing stocks today. I

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<v Speaker 2>flagged that only because that pressure continued as they were

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<v Speaker 2>speaking on stage alongside Nvidia's CEO, Jensen Jog. Now, remember

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<v Speaker 2>quantum stocks went into free fall on January eighth, after

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<v Speaker 2>in Vidia's CEO said we were more than a decade

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<v Speaker 2>away from quantum computers being able to do anything useful. Now,

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<v Speaker 2>fast forward to today, and Juang had this to say

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<v Speaker 2>about those earlier comments. Listen, I'm a.

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<v Speaker 1>Public company CEO, and every so often someone asked me

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<v Speaker 1>a question, and most of the time, most of the time,

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<v Speaker 1>well some of the time, I'm trying to lower the

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<v Speaker 1>bar here. Some of the time I say something right

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<v Speaker 1>and sometimes sometimes it comes out wrong.

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<v Speaker 2>Now here's the thing in the world of technology. Historically,

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<v Speaker 2>at least, these have been two distinct fields, quantum computing

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<v Speaker 2>and accelerated computing or supercomputers for AI, but increasingly those

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<v Speaker 2>worlds are coming together. Joining me now is Tim Costa,

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<v Speaker 2>who's the senior director for quantum Computing Adam Vidia, and

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<v Speaker 2>I think a really important place to start is what

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<v Speaker 2>Nvidia does and what Nvidia does not do. Sure, Nvidia

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<v Speaker 2>does not make quantum computers, nor does it sell quantum computers,

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<v Speaker 2>but it does provide architecture software, and so this is

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<v Speaker 2>to that industry. Just explain your role, please. Yeah.

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<v Speaker 3>So you nailed it on the head with the easy part,

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<v Speaker 3>which is what we don't do. We do not build

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<v Speaker 3>a quantum quantum computer, but we have a vision about

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<v Speaker 3>how quantum computing will be useful. And it's really a

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<v Speaker 3>point at which we have the integration of larger scale

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<v Speaker 3>quantum technologies quantum processors as part of data centers and

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<v Speaker 3>large scale computing. It looks very similar to what we

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<v Speaker 3>have today right these If you look at today's large

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<v Speaker 3>scale computing infrastructure, it's CPUs and GPUs and storage and

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<v Speaker 3>memory and interconnects, and it's very complex, but better heterogeneous,

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<v Speaker 3>and each part plays the role that it's best suited for,

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<v Speaker 3>including the CPU and including the GPU. Quantum technology offers

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<v Speaker 3>promise to be very good at certain kinds of computation,

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<v Speaker 3>and so that will be an additional element of that

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<v Speaker 3>system and come in. And so what we're focused on

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<v Speaker 3>is really helping a helping the quantum tech companies who

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<v Speaker 3>are building those technologies to better develop those technologies because

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<v Speaker 3>we're interested in solving the problems that it will be

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<v Speaker 3>able to solve, but also be setting up that infrastructure

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<v Speaker 3>to be the best partner to that quantum device right

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<v Speaker 3>to be able to do things like error correction, calibration

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<v Speaker 3>of the devices. These are in some ways things you

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<v Speaker 3>can think about as physics experiments upon which we try

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<v Speaker 3>to compute as much as a computer, and so managing

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<v Speaker 3>that physics experiment and getting the right results out of

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<v Speaker 3>it is actually a complex computational task that we look

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<v Speaker 3>to our accelerated supercomputers to actually perform.

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<v Speaker 2>What we are literally talking about here is a computer

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<v Speaker 2>scientist or engineer at a quantum computing company with access

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<v Speaker 2>to some of your GPUs in whatever form. Fact, it

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<v Speaker 2>could just be the single GPU could be scaled up

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<v Speaker 2>to that server design, which we know as DGX. What's

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<v Speaker 2>the market there? You know? How widely is your technology

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<v Speaker 2>being used in parallel with many of the quantum names

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<v Speaker 2>that we saw today.

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<v Speaker 3>Yeah, So what who we saw on stage today was

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<v Speaker 3>a great selection of our partners who are all engaged

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<v Speaker 3>with and who all are using GPU technology as well

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<v Speaker 3>as accelerated computing technology including the force acts and other

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<v Speaker 3>components that we develop in their research program to simulate

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<v Speaker 3>their devices and build better versions of their QPUs, to

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<v Speaker 3>work with their clients and work on algorithm design by

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<v Speaker 3>simulating a quantum computer, and also doing the fundamental research

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<v Speaker 3>to drive towards that vision that I just discussed a

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<v Speaker 3>minute ago, where we actually have this tight integration of

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<v Speaker 3>these devices together. That involves work on the interconnect between

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<v Speaker 3>the QPU and the GPU, that involves developing new methods

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<v Speaker 3>for error correction that can be deployed at scale on

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<v Speaker 3>a large supercomputer using novel AI methods, among many other areas.

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<v Speaker 3>But those are not our only partners in this ecosystem.

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<v Speaker 3>We're working with over one hundred and sixty groups in

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<v Speaker 3>quantum computing, and the range of application areas is quite wide.

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<v Speaker 3>But they're all using in video technology to accelerate their

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<v Speaker 3>work because that's what we're ultimately here to do.

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<v Speaker 2>So we started the day with the idea that in

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<v Speaker 2>vidious technology can help quantum computers accelerate their own timeline

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<v Speaker 2>reach that useful metric. But the idea raised and put

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<v Speaker 2>Jensen Wong by those partners was actually the output of

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<v Speaker 2>a quantum computer. In other words, the computation can work

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<v Speaker 2>both ways. It could be submitted for use in the

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<v Speaker 2>training of a foundation model. What do you make of that?

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<v Speaker 3>Yeah, So I think that there's two sides of AI

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<v Speaker 3>and quantum and they're both really important and really fascinating.

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<v Speaker 3>And what I touched on a few minutes ago was

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<v Speaker 3>the AI four quantum right, using AI models to actually

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<v Speaker 3>control an error correct larger and larger and more capable

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<v Speaker 3>quantum devices. It's incredibly important and it pulls in the

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<v Speaker 3>timeline useful quantum computing.

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<v Speaker 2>Yes.

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<v Speaker 3>Now, the other side that was brought up today, and

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<v Speaker 3>it's a fascinating topic, is you know, what is a

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<v Speaker 3>quantum computer? If you start to boil it down and

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<v Speaker 3>I won't try to go too far down, but it's

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<v Speaker 3>they're really physics experiments. As I said, you're modeling quantum

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<v Speaker 3>physics in the device, and so they're able to potentially

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<v Speaker 3>provide data to train and fine tune models for understanding

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<v Speaker 3>the very phenomena which are inside of a quantum computer

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<v Speaker 3>in a way which will answer questions that humanity is

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<v Speaker 3>and they've unable to answer. So we think that that's

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<v Speaker 3>a really interesting and exciting area to pursue, and we're

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<v Speaker 3>engaged with our partners across all these different areas.

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<v Speaker 2>I don't think that in the context of all the

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<v Speaker 2>attendees I've spoken to you today and Jensen and the panelist,

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<v Speaker 2>that we've kind of reached definitive agreement and what useful is.

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<v Speaker 2>But I think we've definitely reached agreement. But within your industry,

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<v Speaker 2>that this accelerates what's happening pardon the pun, accelerated quantum computing.

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<v Speaker 2>What happens next for Nvidia and their footprinting quantum. There

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<v Speaker 2>is going to be a research center in Boston. That

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<v Speaker 2>those can often be abstract things, But why is that

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<v Speaker 2>an important step?

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<v Speaker 3>It's an important step because we're going one of the

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<v Speaker 3>things that we have to do if we're going to

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<v Speaker 3>build a new kind of computer, and if we add

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<v Speaker 3>a quantum accelerator to a computer like what we build today,

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<v Speaker 3>that is a new kind of computer. You're adding a

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<v Speaker 3>new computer ELEMENTUS. That is a physical endeavor. It has

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<v Speaker 3>a footprint, needs a place to do it.

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<v Speaker 1>Now.

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<v Speaker 3>The center in Boston won't be the only place that

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<v Speaker 3>that happens in the world, but it's a place for

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<v Speaker 3>some of our partners and and US can work on

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<v Speaker 3>developing the interconnect, developing the air correction technologies literally string

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<v Speaker 3>up their quantum devices to our GPUs and build the

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<v Speaker 3>first versions of these quantum accelerated supercomputers that we're all

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<v Speaker 3>working towards.

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<v Speaker 2>You'll view on what is useful. What do you think

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<v Speaker 2>that a quantum computer will be able to achieve, whether

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<v Speaker 2>it's assisted by nvideo or not sure.

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<v Speaker 3>I think that one of the most important things that

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<v Speaker 3>Jensen talked about today on stage was really narrowing the

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<v Speaker 3>focus and deciding what the one the problem is so

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<v Speaker 3>that you can define success and chase after it. I

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<v Speaker 3>do think that there's fairly wide agreement in the community

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<v Speaker 3>that one of the first areas is to be to

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<v Speaker 3>be accelerated and to have new kinds of problems solved

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<v Speaker 3>that weren't before. Is in chemistry, biochemistry related areas. I mean,

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<v Speaker 3>there's some kind of sniff test. This passes.

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<v Speaker 4>Right.

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<v Speaker 3>You've got basically quantum physics in the quantum device, and

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<v Speaker 3>the ability for that to model quantum physics in terms

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<v Speaker 3>of what's required for very accurate chemistry just kind of

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<v Speaker 3>makes sense. So we think that that's going to be

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<v Speaker 3>the first area that's a disrupted early side you. I'm

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<v Speaker 3>sure there's people on my team in discree me as

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<v Speaker 3>we start today from the panelist. There are a wide

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<v Speaker 3>variety of opinions on everything in quantum, but we think

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<v Speaker 3>that's promise them.

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<v Speaker 2>Well, we're grateful for yours. Tim Coster, Senior director of

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<v Speaker 2>Quantum Computing at Nvidia, thank you very much. A lot

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<v Speaker 2>more coming up. We speak with ion Q executive chairman

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<v Speaker 2>Peter Chapman. That's next. We'll be right back. This is

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<v Speaker 2>Bloomberg Technology. Welcome back to a special Bloomberg Technology at

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<v Speaker 2>nvidia's GtC Quantum Day. Ion Q was one of the

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<v Speaker 2>quantum computing companies invited on stage today alongside Jensen Wang

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<v Speaker 2>here at gtc's Quantum Day. The company's stock was one

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<v Speaker 2>of those hardest hit January eighth, after Invidias Jensen Wong

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<v Speaker 2>made those comments about quantum computing and its usefulness being

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<v Speaker 2>quote a decade or more away. Since then, ARMQ has

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<v Speaker 2>gone through some changes, appointing Nicolo Demasi as its new CEO.

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<v Speaker 2>The former CEO Peter Chapman continues as executive chair and

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<v Speaker 2>I'm delighted to say joins us now on set. And

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<v Speaker 2>what was interesting in the conversation with Jensen Wang is

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<v Speaker 2>that you were balanced in saying I'm not I don't

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<v Speaker 2>think we necessarily agree on everything here. Still, let's start

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<v Speaker 2>with the main point, which is did we define usefulness

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<v Speaker 2>and do you have a sense that Jensen Wang has

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<v Speaker 2>changed his timeline of when he thinks usefulness of your

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<v Speaker 2>industry will be achieved?

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<v Speaker 5>Well, I think today was kind of the purpose of

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<v Speaker 5>today was to bring that timeline in to kind of

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<v Speaker 5>take back what it is that he had said before.

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<v Speaker 5>He said on stage I think twice it was his

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<v Speaker 5>mea copper yes, right, So that was the purpose of

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<v Speaker 5>today's kind of.

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<v Speaker 2>He said that he would be the first CEO probably

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<v Speaker 2>in history to invite a panel of people to te

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<v Speaker 2>them that he was wrong.

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<v Speaker 5>It was wrong, is exactly so, and it was funny today.

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<v Speaker 5>Just in general, we've been working with Nvidia on a

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<v Speaker 5>demonstration for today that was with Nvidia AWS and Astrozeneca,

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<v Speaker 5>where we'd gotten a twenty x improvement on what we

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<v Speaker 5>had done previously. Also, we had taken with answers with

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<v Speaker 5>a product that they do which is normally run on GPUs,

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<v Speaker 5>and managed to get a twelve percent increase in using

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<v Speaker 5>our quantum computers. So while those numbers are not really

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<v Speaker 5>enough to take over the market, because usually you need

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<v Speaker 5>one or two orders of magnitude to be disruptive to

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<v Speaker 5>a market, the fact that we managed to do it

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<v Speaker 5>on the kind of our thirty six cubit system is

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<v Speaker 5>really quite remarkable.

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<v Speaker 2>Nvidia has a quantum computing business in so far as

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<v Speaker 2>we got into it during the panel. That you have

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<v Speaker 2>access to architecture, DGX, the hype performance, GPUs, also some

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<v Speaker 2>software and open source solutions, and what they would say

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<v Speaker 2>is that that will help your engineers and computer scientists calibrate,

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<v Speaker 2>reduce err accounts, design better. Is that how it actually

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<v Speaker 2>plays out for you some aspects.

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<v Speaker 5>We have a DGX cluster that we use for a

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<v Speaker 5>design for designing other quantum computers, not so much in

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<v Speaker 5>the error correction aspect. That's something we do ourselves, but

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<v Speaker 5>certainly we use a number of GPUs for designing the

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<v Speaker 5>quantum computer itself. We also for small cube accounts, because

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<v Speaker 5>soon as you get into me on about thirty five cubits,

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<v Speaker 5>you can no longer simulate one of these on a GPU,

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<v Speaker 5>So for those we actually run the simulation on a

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<v Speaker 5>GPU just to make sure our hardware is working correctly.

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<v Speaker 5>The problem is, when you get to sixty four cubits,

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<v Speaker 5>you need two and a half billion GPUs.

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<v Speaker 2>Because for each cubit you add to perform its essentially

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<v Speaker 2>double bubbles.

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<v Speaker 5>Right, and it means that the matrix math that you

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<v Speaker 5>have to do suddenly is doubling as well. So basically

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<v Speaker 5>is it about thirty five cubas To fully simulate it,

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<v Speaker 5>you can only get it on a single dgx one hundred.

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<v Speaker 2>I got to hold you to this, and I wonder

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<v Speaker 2>if it ties into the new CEO and you know,

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<v Speaker 2>sort of reset a little bit. There's a difference between

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<v Speaker 2>lab experiment and commercial use, you know, making money, revenue generation.

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<v Speaker 2>That's the question I get for you most.

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<v Speaker 5>Yes, so what Jensen said, we actually one area we

0:12:32.040 --> 0:12:34.560
<v Speaker 5>definitely agree on, which is you need to find a

0:12:34.640 --> 0:12:38.560
<v Speaker 5>set of applications early on that you can start to

0:12:38.600 --> 0:12:41.400
<v Speaker 5>make money on and to build that firewheel to be

0:12:41.440 --> 0:12:45.240
<v Speaker 5>able to power your R and D. And so the

0:12:45.640 --> 0:12:49.480
<v Speaker 5>examples we're talking today, for instance in the chemistry application

0:12:50.000 --> 0:12:52.960
<v Speaker 5>and also with answers is exactly those kinds of things.

0:12:53.400 --> 0:12:55.120
<v Speaker 5>So that's exactly what our plan is.

0:12:55.760 --> 0:12:59.160
<v Speaker 2>PCU or the executive chairman. Now you were CEO one

0:12:59.200 --> 0:13:03.480
<v Speaker 2>week ago, Kerisdale issued a short report on your stock

0:13:03.520 --> 0:13:04.880
<v Speaker 2>on your company. I just want to give you a

0:13:04.960 --> 0:13:07.160
<v Speaker 2>chance to respond, as we've not had a chance to

0:13:07.160 --> 0:13:07.839
<v Speaker 2>speak since then.

0:13:07.920 --> 0:13:11.839
<v Speaker 5>Sure, it's a short report, so their goal is obviously

0:13:12.000 --> 0:13:14.880
<v Speaker 5>to try to cast out about the company and make

0:13:14.920 --> 0:13:18.600
<v Speaker 5>money that way. Unfortunately, it's just a if you're a

0:13:18.640 --> 0:13:20.800
<v Speaker 5>public company, these are the kinds of things you have

0:13:20.880 --> 0:13:24.440
<v Speaker 5>to endure. You know, we don't put much credence to

0:13:24.480 --> 0:13:25.040
<v Speaker 5>those things.

0:13:25.440 --> 0:13:26.840
<v Speaker 2>The last thing I want to ask you is about

0:13:26.840 --> 0:13:31.360
<v Speaker 2>how big today was in the change of trajectory or

0:13:31.400 --> 0:13:36.600
<v Speaker 2>momentum for your industry. Jensen Wang is a character and

0:13:36.760 --> 0:13:42.160
<v Speaker 2>he was honest on stage. But GtC is an incredible event.

0:13:42.200 --> 0:13:44.800
<v Speaker 2>It has scale, it has eyeballs. Do you think you'll

0:13:44.840 --> 0:13:47.439
<v Speaker 2>see something of substance come out of this for your company,

0:13:47.480 --> 0:13:48.720
<v Speaker 2>for your sector?

0:13:49.240 --> 0:13:52.160
<v Speaker 5>You know, it's an interesting it's certainly Jensen's goal was

0:13:52.200 --> 0:13:54.600
<v Speaker 5>to give us the microphone today to be able to

0:13:54.600 --> 0:13:58.040
<v Speaker 5>get out our story, and that's certainly important. But I say,

0:13:58.559 --> 0:14:01.600
<v Speaker 5>if you were Sam Altman five years ago trying to

0:14:01.640 --> 0:14:06.120
<v Speaker 5>convince the world that AI is coming, probably he wouldn't

0:14:06.120 --> 0:14:08.880
<v Speaker 5>have been successful. And the question is is how much

0:14:08.920 --> 0:14:12.199
<v Speaker 5>time should Sam Altman try to convince the world AI

0:14:12.360 --> 0:14:15.560
<v Speaker 5>is coming? Instead just go back and actually make it happen.

0:14:16.080 --> 0:14:19.640
<v Speaker 5>So in that sense, actually it's probably not that significant.

0:14:20.200 --> 0:14:23.040
<v Speaker 5>What really matters is actually going and doing it, not

0:14:23.080 --> 0:14:24.760
<v Speaker 5>actually getting the message out.

0:14:24.960 --> 0:14:27.400
<v Speaker 2>I'm Q Executive Chairman Peter Chapman. Thank you for your

0:14:27.440 --> 0:14:30.480
<v Speaker 2>time here in San Jose at GtC. Okay, much more

0:14:30.520 --> 0:14:33.360
<v Speaker 2>to come. Alan Barrat's CEO d Wave, another one of

0:14:33.400 --> 0:14:36.640
<v Speaker 2>the quantum computing CEOs on stage, joins us, and they

0:14:36.680 --> 0:14:42.280
<v Speaker 2>have an example of success or something useful blockchain architecture progress.

0:14:42.320 --> 0:15:01.400
<v Speaker 2>That's next. This is Bloomberg Technology. Welcome back to a

0:15:01.440 --> 0:15:05.640
<v Speaker 2>special edition of Bloomberg Technology Live in Videos. GtC Quantum

0:15:05.720 --> 0:15:09.320
<v Speaker 2>Day so shares of some quantum computing companies sank today

0:15:10.240 --> 0:15:12.880
<v Speaker 2>after leaders spoke at an event with in video CEO

0:15:12.960 --> 0:15:16.720
<v Speaker 2>Jensen Huang. The plunge follows a similar move back in

0:15:16.840 --> 0:15:19.640
<v Speaker 2>January after Huang said it would be more than a

0:15:19.720 --> 0:15:24.360
<v Speaker 2>decade before the technology quantum computing could do something that's useful.

0:15:25.080 --> 0:15:26.840
<v Speaker 2>One of those quickest to say in the month of

0:15:26.920 --> 0:15:30.440
<v Speaker 2>January that Wang was wrong was d Wave CEO Alan Barrats.

0:15:31.160 --> 0:15:33.920
<v Speaker 2>D Wave shares under pressure today for whatever reason. But Alan,

0:15:33.960 --> 0:15:36.240
<v Speaker 2>I'm grateful for your time, and I think it's fair

0:15:36.280 --> 0:15:40.040
<v Speaker 2>to say that among the panelists you maintained those areas

0:15:40.040 --> 0:15:43.120
<v Speaker 2>that you don't agree with Jensen Hwang on. How in

0:15:43.520 --> 0:15:47.240
<v Speaker 2>any way is your mind changed on those differences through

0:15:47.320 --> 0:15:48.120
<v Speaker 2>the course of today.

0:15:48.760 --> 0:15:52.200
<v Speaker 4>Well, my mind has not changed. The fact of the

0:15:52.240 --> 0:15:55.360
<v Speaker 4>matter is that we at d Wave have taken a

0:15:55.440 --> 0:15:58.320
<v Speaker 4>very different approach to quantum computing from everybody else in

0:15:58.320 --> 0:16:01.680
<v Speaker 4>the industry, and as a result of that, we are

0:16:01.840 --> 0:16:06.640
<v Speaker 4>actually able to support useful, important applications today.

0:16:06.960 --> 0:16:08.160
<v Speaker 2>And I think the best.

0:16:07.880 --> 0:16:10.440
<v Speaker 4>Example of that is the paper that we published in

0:16:10.480 --> 0:16:14.520
<v Speaker 4>Science last week, where we have demonstrated that we can

0:16:14.600 --> 0:16:20.520
<v Speaker 4>compute properties of magnetic materials that just cannot be computed classically,

0:16:20.760 --> 0:16:24.240
<v Speaker 4>and this gives us the opportunity to create new materials

0:16:24.440 --> 0:16:29.480
<v Speaker 4>discovery platforms which will dramatically reduce the time and cost

0:16:30.000 --> 0:16:31.240
<v Speaker 4>to create new materials.

0:16:31.480 --> 0:16:34.200
<v Speaker 2>And that seems pretty useful to me. Yeah, there's also

0:16:34.360 --> 0:16:37.440
<v Speaker 2>some applications that the audience might find harder to understand.

0:16:37.440 --> 0:16:43.120
<v Speaker 2>For example, blockchain architecture. Why is a quantum computer able

0:16:43.160 --> 0:16:46.840
<v Speaker 2>to improve that process where a supercomputer classically coded in

0:16:46.880 --> 0:16:48.160
<v Speaker 2>ones and zeros cannot.

0:16:48.840 --> 0:16:51.640
<v Speaker 4>So basically, what we did was we were able to

0:16:51.680 --> 0:16:56.000
<v Speaker 4>show that that same computation that we use to compute

0:16:56.040 --> 0:17:00.960
<v Speaker 4>properties of magnetic materials could be used to basicly we

0:17:01.000 --> 0:17:05.199
<v Speaker 4>compute the hashing functions that are used in blockchain and cryptocurrency.

0:17:06.000 --> 0:17:08.679
<v Speaker 4>What this means is that we can now create a

0:17:08.720 --> 0:17:12.879
<v Speaker 4>blockchain that uses a quantum computer to do the proof

0:17:12.920 --> 0:17:15.920
<v Speaker 4>of work. What's so important about that is that quantum

0:17:15.960 --> 0:17:20.640
<v Speaker 4>computers consume far less energy than classical computers, So this

0:17:20.800 --> 0:17:25.879
<v Speaker 4>means cryptocurrency mining could be at a fraction of the

0:17:26.040 --> 0:17:28.280
<v Speaker 4>energy cost of what we're seeing today.

0:17:28.400 --> 0:17:31.320
<v Speaker 2>One of those two case studies revenue generates this VIE.

0:17:31.880 --> 0:17:37.119
<v Speaker 4>Well, the first prototype of this blockchain is running right now.

0:17:37.400 --> 0:17:40.240
<v Speaker 4>We have it running on four of our quantum computers.

0:17:40.280 --> 0:17:43.720
<v Speaker 4>It's the first distributed quantum application where each of the

0:17:43.800 --> 0:17:49.640
<v Speaker 4>quantum computers can create hashes or validate hashes, basically run

0:17:49.680 --> 0:17:52.199
<v Speaker 4>the proof of work algorithm. And we're now in the

0:17:52.240 --> 0:17:54.720
<v Speaker 4>process of building that out so we can get to

0:17:54.760 --> 0:17:58.200
<v Speaker 4>the point where we can support a full commercial blockchain.

0:17:58.280 --> 0:18:00.600
<v Speaker 4>How long do I think that'll take? Yeah, I think

0:18:00.640 --> 0:18:02.000
<v Speaker 4>we're looking at a year or two.

0:18:02.080 --> 0:18:05.960
<v Speaker 2>Not the ten or fifteen. The ten or fifteen or

0:18:06.000 --> 0:18:08.280
<v Speaker 2>twenty two. So there is work than a video.

0:18:08.359 --> 0:18:12.080
<v Speaker 4>But by this, I mean that's just one application, right,

0:18:12.240 --> 0:18:15.560
<v Speaker 4>I mean the materials discovery that's running today. We have

0:18:15.680 --> 0:18:19.360
<v Speaker 4>customers like Entity DoCoMo. They're using us today for cell

0:18:19.440 --> 0:18:24.119
<v Speaker 4>tower resource optimization. So we are useful today. Blockchain is

0:18:24.160 --> 0:18:27.280
<v Speaker 4>one of our newest application areas, and yes, we're just

0:18:27.400 --> 0:18:28.280
<v Speaker 4>starting to roll there.

0:18:28.520 --> 0:18:30.800
<v Speaker 2>The point of difference I think, I still think is

0:18:31.200 --> 0:18:35.200
<v Speaker 2>Jensen's definition of usefulness. Perhaps, But you know, Nvidia does

0:18:35.240 --> 0:18:39.399
<v Speaker 2>do work with your industry. To summarize, it's basically offering

0:18:39.480 --> 0:18:42.680
<v Speaker 2>GPU access on the architecture side, as well as some

0:18:43.359 --> 0:18:47.159
<v Speaker 2>research and open source facilities on the other side. And

0:18:47.200 --> 0:18:50.080
<v Speaker 2>their argument is you can take that and use it

0:18:50.280 --> 0:18:54.440
<v Speaker 2>to make your quantum computers better calibration, air account reduction,

0:18:54.640 --> 0:18:57.679
<v Speaker 2>and I think other your colleagues mentioned design, how do

0:18:57.760 --> 0:18:58.720
<v Speaker 2>you work with video?

0:18:58.840 --> 0:19:03.199
<v Speaker 4>Okay, so annealing quantum computers do not have the same

0:19:03.880 --> 0:19:08.119
<v Speaker 4>error correction requirements as gate model quantum computers. So we

0:19:08.160 --> 0:19:12.439
<v Speaker 4>are solving useful problems today without error correction, and as

0:19:12.480 --> 0:19:16.320
<v Speaker 4>a result, that component of what Nvidia brings to the

0:19:16.359 --> 0:19:19.840
<v Speaker 4>table is not all that important to us today. When

0:19:19.880 --> 0:19:22.960
<v Speaker 4>it comes to calibration. We have the largest quantum computers

0:19:22.960 --> 0:19:25.200
<v Speaker 4>in the world. Our current systems are at five thousand

0:19:25.280 --> 0:19:29.080
<v Speaker 4>cubits and growing. We calibrate them ourselves. We don't need

0:19:29.160 --> 0:19:33.119
<v Speaker 4>GPU paler to calibrate those systems, so currently we do

0:19:33.200 --> 0:19:35.919
<v Speaker 4>not I mean, I know Jensen says he works with

0:19:36.000 --> 0:19:39.440
<v Speaker 4>all the quantum computing companies, but d Wave is quite different.

0:19:39.680 --> 0:19:43.040
<v Speaker 4>We are a different We've taken a different approach. We

0:19:43.160 --> 0:19:45.840
<v Speaker 4>are at a different level of maturity, much more mature

0:19:45.920 --> 0:19:48.919
<v Speaker 4>than the other quantum computing companies. We are delivering useful

0:19:48.920 --> 0:19:52.720
<v Speaker 4>applications and useful value today. Now that having been said,

0:19:53.200 --> 0:19:56.639
<v Speaker 4>we are also developing a gate model quantum computer. The

0:19:56.680 --> 0:20:01.120
<v Speaker 4>approach that everybody else has taken for that effort, we

0:20:01.240 --> 0:20:05.159
<v Speaker 4>will be looking to leverage some of the same sorts

0:20:05.200 --> 0:20:08.160
<v Speaker 4>of things that the other quantum computing companies are leveraging.

0:20:08.359 --> 0:20:11.280
<v Speaker 4>But we view a kneeling and gate as very complementary.

0:20:11.520 --> 0:20:13.280
<v Speaker 4>They solve different classes of problems.

0:20:13.359 --> 0:20:16.159
<v Speaker 2>We only have thirty seconds. It was a pretty public disagreement,

0:20:16.280 --> 0:20:19.240
<v Speaker 2>argument debate. Will it help you in the long run?

0:20:19.280 --> 0:20:20.720
<v Speaker 2>What happened today here in San Jose.

0:20:22.960 --> 0:20:27.399
<v Speaker 4>I don't think this event was all that helpful to

0:20:28.240 --> 0:20:29.600
<v Speaker 4>the industry or to d WAVE.

0:20:29.840 --> 0:20:30.480
<v Speaker 2>I think.

0:20:31.880 --> 0:20:35.359
<v Speaker 4>I thank Jensen for the opportunity to participate. I think

0:20:35.440 --> 0:20:37.800
<v Speaker 4>that it was great to have the opportunity to try

0:20:37.800 --> 0:20:40.920
<v Speaker 4>to get the message out. But I think we're still

0:20:40.960 --> 0:20:43.720
<v Speaker 4>at the beginning of a learning curve with respect to

0:20:44.320 --> 0:20:48.800
<v Speaker 4>how Nvidia and Jensen interact with quantum computing companies.

0:20:49.200 --> 0:20:51.680
<v Speaker 2>D Wave CEO Alan Barratz, thank you for your time

0:20:52.040 --> 0:20:54.640
<v Speaker 2>here in San Jose. Whether you agree with your host

0:20:54.840 --> 0:20:57.359
<v Speaker 2>or not, Well, that does it for this special edition

0:20:57.760 --> 0:21:01.440
<v Speaker 2>of Bloomberg Technology a lot to re cap, particularly when

0:21:01.480 --> 0:21:04.160
<v Speaker 2>it comes to quantum computing, So don't forget our podcast.

0:21:04.240 --> 0:21:06.320
<v Speaker 2>You can find it on the Bloomberg terminal as well

0:21:06.320 --> 0:21:10.320
<v Speaker 2>as online on platforms like Apple Spotify, and iHeart Live

0:21:10.440 --> 0:21:14.800
<v Speaker 2>from San Jose, California, at GtC and Video's Quantum Day.

0:21:15.200 --> 0:21:17.119
<v Speaker 2>This is Bloomberg Technology