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Hi, everyone! Thank you for joining WisdomTree's Office Hours on the Quantum Inflection, From Lab Curiosity to 2 Billion Federal Bet in One Week, where you'll hear from Chris Ganati, WisdomTree's Global Head of Research, and Elvira Kurumshina, Associate Director on our Quantitative research team.

Christopher Gannatti

Thank you, Irene. Thank you, Elvira, for joining me today, and thank you, everyone. In the audience, sort of taking some time with us on a nice, Friday morning, in the U.S, Eastern time. Quantum computing has been coming up more and more. It's a very interesting topic, a topic near and dear to our hearts here at WisdomTree, and it's absolutely correct to think that we're hearing these sort of key milestones, and it's feeling like a lot of these key milestones, they're happening with a greater and greater intensity, a greater and greater rapidity. If you think just this week. Quintinuum. just did its IPO yesterday. WisdomTree has a quantum strategy. We actually just posted that we will seek to include, Quintinuum. Some trades will occur, basically next week. Our thought is, in this particular space, you want to be early, and you want to be having exposure to those, what we term, pure play. companies in your strategy as quickly as possible. You also want those pure play companies to basically be driving, in our view, the bulk of the overall exposure. You want to see them in the top 10. And when I'm saying pure plays, I'm thinking companies like IonQ, which people following the space may have heard of. That has tended to be the largest market cap pure-play quantum company. D-Wave has been around a long time. They do an interesting approach called annealing, and they've been offering that for far longer than a lot of people would assume or suspect, certainly more than 10 years. Rigetti, gets, gets a lot of attention. They were on the government's list of companies receiving, funding and an equity stake. That would have been about 2 weeks ago. Xanadu, a Canadian company using photonic qubits. Quantinum, which just went public yesterday. That's a trapped ion, company, running a very interesting machine. They were inside of Honeywell, and I think Honeywell still does own roughly 49 or so percent, so it's not as though Honeywell has completely left the picture. So, the key thing that people can have in mind, and Quantinum, the most recent example of this, is You're going from an environment where it used to be, even a few years ago, that you needed to be in the private markets to really have true exposure. Yes, Microsoft has some things they do in quantum, yes. Google has some things they do in quantum, and they had the Will of Ship a few years ago. Amazon has some things they do, they have the Cat qubits, and they have the Bracket platform that you can use to get access to a lot of these other systems. So, it's not as though the biggest companies in the world are absent. They're certainly there, they have efforts, they're fully aware of these systems. But the change, and it's been accelerating in that the ability for companies to raise capital and to enter the public markets is, you know, on a nice sort of rise of late, with all the IPO news, and I know, I know Quintinuum probably is getting swamped, even though it happened yesterday by the SpaceX, which is, you know, coming in basically one week's time. But still, the option that investors have to gain that earlier stage pure play exposure prior to the full realization, the full development, the full, sort of. quantum supremacy that many of these companies, and something we'll discuss later in this call, are some of the timelines that are put out by real firms in the space. Now, something that we can do, Elvira and I can do together, is sort of provide the context, and something I just wanted to show people is, we have this report. So this is one page off of our monthly thematic report, and it's interesting to sort of think… we're talking about quantum computing. And what this… now, this is as of the end of April, because the May data hasn't come through yet, but as of the end of April, in the U.S. market, it's mostly just one ETF, if we're honest here, but the total IUM in the quantum computing topic was about $4.3 billion. US dollars. I would estimate today, because I look at that other ETF, I look at our ETF, and there might be a few others. I would estimate today it's probably around 6 to 6 point something, and that's what we'll ultimately see soon. But you compare that to, for instance, semiconductors, and there are some really, really big and well-established semiconductors. strategies out there. That's almost 100, almost certainly would be above 100 when we get, the new monthly data. broad-based artificial intelligence is approaching $30 billion, probably will be above $30 billion when we get the new monthly data. So, you are seeing interest, and significant money to a degree, but not… Significant money, say, relative to certain other strategies that are out there. Now. There are other events, too, it's not just the Continuum IPO yesterday, that Microsoft was making some announcements on the 2nd of June. And then the announcement that this year, in our opinion, sort of kicked everything off, and I'm gonna… pass the microphone over to Elvira to, discuss it, further, but it relates to this first bullet here, IMQ's successful entanglement-based networking of two commercial quantum computers. This was announced on… World Quantum Day, so that is a real day. It's been in existence since 2021. It is April 14th of every year. We're not just making this up, I promise. There is actually a world quantum day that happens every year, so mark your calendar now for 2027. Wink wink. But, Elvira, there were a lot of announcements on this particular day, and I was thinking you could maybe walk us through the import behind them.

Elvira Kuramshina

Absolutely, happy to, Chris, thank you. So, let's start with INQ, and maybe just let's take a step back for investors to appreciate the importance of this announcement. When we are discussing quantum computing, we have to highlight that the key challenges in the industry are currently geoscale. The systems from, their current noisy state. where we only have, you know, limited number of problems that can be solved through very powerful machines in the future. So when we're looking, for example, at different timelines published by various companies active in the field, you can see frequently the year 2030. as being the year where they are expecting these powerful quantum machines to arrive. This is the official timeline. Of course, it might sound quite ambitious, because it's just 4 years away from now. And, the systems have to scale, you know, in some instances, for example, from dozens of logical qubits to hundreds to then thousands. And one way how it could be possible is through networking multiple smaller quantum systems to harness them in one, basically, big, interconnected system. And, to date, before the announcement from INQ, no company really Demonstrated, that it's possible to interlink to different quantum systems. And that's why it was so exciting that INQ essentially was the first one to announce that it's possible to interlink, they demonstrated that it's possible to interlink two different quantum systems, paving essentially the way, laying this foundational stone for, scaling quantum computers in the future. Of course, there is still… a range of different challenges, engineering challenges, that has to be overcome, but this is a very important step in the right direction. And interestingly, after… just a week after INQ posted this, Cisco was another company to… basically announced the prototype, also on the networking front, and connecting different qubit modalities. Because, It's… it's a way… it's essentially another step further, where you would say that We could interconnect two different, systems, two different qubit modalities, to harness the power, and then to see, basically, in which step we can use which system to maximize our benefit from quantum computing. And, if we now shift to NVIDIA, because that was… another exciting announcement on the same day. I think… what makes me personally excited about that is that NVIDIA is one of the companies that is probably less known or less viewed to be really in quantum computing. But what we started seeing, really from last year, that they were a very important step from the company to support the quantum ecosystem. from the infrastructure perspective. And right now, to see the utility from quantum computers in the near term. The step, basically, forward is to use quantum in hybrid architecture. So, you are combining classical computers with quantum computers, and quantum computers are used in a very… small steps. So, last year, NVIDIA released in October something that is called NVQLing, and essentially that allows smooth integration of GPUs with quantum processing units, so that companies can seamlessly harness quantum computing For different problems that they're solving, where a certain step might be intractable for classical computers. And what they did further in April is essentially they released, open-source family of models called ISING, and this specific, family of models is directed at quantum error correction and quantum calibration. And quantum error correction is very important. for scaling quantum computers to bigger systems as well. Because essentially, the qubits. that is the basic, unit of information in a quantum computer. They are very fragile. So, to make sure that we have high accuracy and high reliability of the outputs from quantum computers, we have to introduce error correction. In fact, in classical system, we also have error correction. It's super reliable, so that's why we can rely, you know, like, on all the outputs that we are getting from classical computers. In quantum computers, it's much more challenging, and there are, like, different techniques to do that. So, NVIDIA, bringing the power of AI into this realm, is making an important contribution into the quantum ecosystem, because essentially it allows so many researchers around the world to get access to the state-of-the-art AI models to advance Further, error correction and error calibration, and bring about further breakthroughs on the quantum hardware front.

Christopher Gannatti

Absolutely, and something we can do to kind of bring everybody to the baseline in our discussion today, and again, in 30 minutes, this is a big topic, so I don't expect that we're gonna hit every single detail in 30 minutes, but something that we can do is we can sort of think through, when Elvira's talking about modalities, for instance, what does that mean? Well, one of the leading modalities from a standpoint of being around the longest, if anyone has seen a quantum computing article where you have that picture of a usually gold, upside-down chandelier of some sort hanging from the ceiling. Why does it look like that? Well, typically there are specific isotopes of helium that are being used To run through those very thin pipes, very fine gold, architectures. that essentially cool a very, very small chip to a temperature that is colder than what you would experience if you were in one of the SpaceX rockets that had just gone to orbit. So obviously, we know space is… very cold, but there are no molecules, there's no air, there's no gas, there's nothing really out there besides radiation. And in those systems, and you can Google, you can… ChatGPT, you can use whatever model, and you can say, show me what an IBM deployed system… and IBM has a lot of these deployed systems, not a lot like hundreds, but certainly tens. They're deployed in places all over the world. And the reason that the system is the size that it is, and the reason it looks the way it does. is you basically need to separate this very tiny chip, and these very tiny, neobium, the specific metal is called neobium that they're using. They're cooling it down to such a temperature that it becomes a superconductor, and now you have what's referred to as superconducting qubits. Google has their version of this, Rigetti, if you've heard of that company, has their version of this. IBM has their version. It's notable, Elira and I were talking before the session. IBM has been running a connected system. It's not a super powerful system yet. I think in the early days it was maybe 5 qubits, but those early days were 2016. So, there is a history where IBM has been running a working machine. That, obviously, you can't, you know, run Shore's algorithm and break all encryption on such a machine, but it was a running, working machine for 10 years plus now. They just crossed their 10-year anniversary of having this machine connected to the internet. So that's superconducting. If you've heard the those words used before. Quantinum, the IPO from yesterday, the company that we'll be adding to our strategy here in the U.S. next week. That company is doing the trapped ion. modality. IonQ, as well, does the trapped ion modality. What you hear with the trapped ion modality is the highest qubit fidelity. It has a great… Control of the qubits, a great resistance to the errors sort of creeping in based on how they do it, but the trade-off for that control in that modality is when you're running a complicated algorithm, there's a word that you'll frequently see in articles. It'll be called gates. And the gate speed of trapped ion is among the slowest of all the systems. Why is that important? Well, that's important if you need to run a billion gates, because that means you need to keep the qubits in superposition, which is not easy to do, at least not yet, and you need to keep them in that superposition for a longer and longer time to be able to run through all of the gates. The superconducting modality is very, very fast. roughly a thousand to a million times faster, depending on how you measure it. The issue is, the errors creep in a lot more easily. So, in one case, you can run the logical operations much more quickly, but the errors are always about to converge, and in the other system, it's a lot slower, but you're more error-resistant. You'll see, in the world of quantum computing today, there's no perfect system. There's no system that has solved everything to the ideal degree. There's always a trade-off. The photonic, if you're looking at Xanadu, which is in the strategy, photonic qubits, the benefit of photonic qubits is you can do it at room temperature. So the cooling that is ultimately required, the separation from the environment that is ultimately required in a lot of these other systems. Is not necessarily required to the same degree with photonics, but the issue with photonics is the, photons don't easily interact with each other. Why is that important? Well, when you're trying to use qubits to store information and perform logical operations, which is the basis of computing, the propensity to interact at least somewhat is important. a company like Xanadu, or if you've heard of a company, S… PSI Quantum, excuse me, that is another photonic qubit company. Those are some of the challenges that they're facing. And then if you've heard of the neutral atom modality, that's a newer modality, a very interesting modality, the company Inflection, which is in the portfolio, or if you've heard of Q-Era, which is a private company, not in the portfolio, these companies do the neutral atom approach, where it's kind of cool, it's called an optical lattice, where they're using lasers to manipulate specific Atoms of different… elements. Utterbium is a word I never thought I'd use on an office hours, but that's an example of one of these elements that are actually used. In these systems. So again, the way to think of this is not any one system is perfect, not any one system has been fully built, to scale. All systems are making interesting. progress, and to Elvira's point as well earlier, the exciting thing about what IonQ did was they networked to commercial quantum computers. Frequently, what you find is people will write a paper, the paper might be about an error correction, the paper might be about a new system, the paper might be about a new potential. The paper is frequently the first step, and it's a very valuable step, but there's a difference between the paper, the experiment in the lab, which is frequently the next step, and then the actual proving that it works on a commercial system. A commercial system… may not be as perfect in terms of the underlying conditions as the lab might be. And so, of course, we're trying to get to commercial quantum readiness, but as you're following the space, as you're seeing headlines and announcements from companies. A model that I have found useful is trying to think and break it down step by step. Is this a paper? Is this an experiment? Or is this a commercial demonstration? You're seeing variations of all three of those. Almost every day these days, which makes it exciting, makes it interesting, but also makes it important that we have to keep our, you know, optimism tuned to the right degree, not get overly excited, not get overly pessimistic, and realize we're ultimately on a journey here.

Christopher Gannatti

So, something that we can do, and there were some questions coming in, and I wanted to… address this, directly? Because to me, again, it's one of the most useful Things… is… If you're seeing the 2026, 2028, and 2030, This is an example, so this is the real website from IBM, because people frequently ask, like, what is the progress? How do we know what the companies are seeking to do? How do we know if the companies are ultimately… delivering any sort of success? How do we measure if the success that they're telling us about has any relation to what they were trying to do? Like, did they fail 10 things and they're just telling us about, you know, the 11th thing that they tried that year, or are they actually, you know, performing to plan? The nice thing that IBM does, and you see what they're trying to do in 2026, what they're trying to do by 2028, what they're trying to do by 2030. And Elvira's exactly right in noting that 2030 is kind of this… key line in the sand, at least based on where we are today. And many companies are converging to a belief, and specifically what the belief is, and what all these details really mean. Is there are certain physics that, in many cases, the companies believe have been figured out. Now, the next step is, what do you do to take those physics into sort of an engineering reality? And when you're seeing the different amounts of gates, so we mentioned gates earlier, that's another term for logical operations, qubits. That's a term, Elvira introduced the term. It's IBM, so it's superconducting qubits, that's what we're referencing here. In the superconducting space, the most important thing that you want to be digging into, if you're basically saying, what's going to be the biggest challenge. From what we know today in superconducting, it's gonna come down to error correction. How are they doing with coming up with more efficient ways so that they can get more information at a higher fidelity, and they don't need a billion qubits to get the job done? Maybe… and this is what a lot of the papers in the space tend to say. They tend to say, oh. we thought we would have needed a billion qubits to do this, now we only need 20 million qubits to do this, now we only need a million qubits to do this, now maybe we need 500,000 qubits to do this, so there are real papers that are basically addressing, and the most popular topic that gets addressed is encryption. They basically say, if you're gonna break RSA 2048 encryption, which is an important standard, how many qubits would you need? This is something that has… there is no experiment, there is no… proof of running Shore's algorithm and actually doing this. This is an example of a lot of papers have been written that have led to a lot of headlines in popular media, but does such a system actually exist today? Not yet. The earliest possible time, Elvira's exactly right, that maybe we'll see some initial tests of Shure's algorithm breaking something. Maybe that'll be 2029, 2030. That's a reasonable estimate as we sit here today. No one's estimating 2026. So as people are thinking of a reasonable investment horizon, it's interesting to think of the potential announcements that you might see as we go through the rest of 2026 and into 2027, but we have to also calibrate our expectations and say, okay, none of the companies are saying, we're gonna have everything figured out by 2027. Now, the way Google operates, they tend to be more a, surprise, look at the breakthrough, whereas the way IBM operates is they put a timeline, directly on the page. This is the same idea, only with, IONQ's version, so they're basically telling us roughly calendar year 29. Physical qubits are just the overall qubits in the system. Logical qubits, if you ever see that expression, it basically just means qubits that are holding specific information that could then be used for worthwhile calculations. The way to think of information in a qubit sense, and Peter Shore figured this out 30 years ago, is to encode, as Elvira was alluding to, the appropriate error correction. You basically have your logical information, and instead of doing one qubit to one unit of information, you might say. A logical qubit is represented by 9… physical qubits. So, a ratio might look something like 10 to 1, or 9 to 1, or something like that. And the whole name of the game for a lot of these systems is how do they get that ratio closer and closer. Yesterday, one of the published metrics for the Helio system, which is Quantinuin's system, is they believe they're getting closer to 2 to 1, meaning you would have two physical qubits to one logical qubit. The benefit of that is it's easier to control, and it's easier to run a system with a lower number of… you want the lowest possible number of physical qubits that can still do the job. So it's a delicate tightrope, a delicate balancing act. To ultimately, walk. Now, Elvira and I were talking, and there were some questions that came in. Power consumption, because that's the big discussion in, AI. So, Elvira, I know you had some interesting ideas and perspectives on the idea of, power consumption.

Elvira Kuramshina

Sure, Chris. So, I think first, I mean, you introduced already that there are different qubit modalities, right? And that also impacts How much energy each of those systems can consume. There is definitely a diversity in energy needs, but what we have to think about, what is the major promise of quantum computing, right? to deliver exponential speedups for certain problems, or to solve problems which are currently intractable for classical computers. So, if we're thinking from the perspective of delivering speedups, it already means that you will be solving problems much faster than what classical computers would be doing. And that already implies that your energy needs should be lower. Of course, currently, it's not the case that we have such powerful systems, and it might be the case that, in its current state. quantum computers might be consuming more energy just because they are not solving, yet, problems as efficiently. But I also would like to highlight the potential impact that quantum computers can bring in terms of solving different energy problems. So if we think about more efficient battery design, next-generation batteries, or more sustainable materials that are being, you know, that can be discovered using quantum computers. I think the impact on power consumption, on energy in general, on potentially climate change. Could be, quite profound when we have powerful quantum computers.

Christopher Gannatti

And something I know our team is putting together to, again, help people see, for instance, using such a strategy. within a portfolio context, again, I alluded to how we announced today that next week we will certainly execute some trades and have Quintinum essentially be the most recent addition to the strategy. Again, the idea is we want to be early into The key pure-play quantum companies, and… the reason that that's important is it could be a long time before a company like Quintinuum, to use that as an example, is in the NASDAQ 100 or in, you know, other major benchmarks like the S&P. And so, there is sort of that core satellite mentality, as well as sort of the risk sizing appropriateness in the sense that this is an earlier stage, type of technology, say, relative to something like artificial intelligence, where you think, for instance, the government announcement from a few weeks ago that they were announcing a $2 billion overall investment. I think IBM announced something on the order of $10 billion over a number of years, and that's not nothing, but at the same time, we're watching the AI world, where next year it might be that 4 or 5 companies that… combined to spend a trillion dollars in a single year, and we know that this year, it's gonna be around $730 billion, roughly speaking. Last year, it was $500 plus billion. The year before that, it was maybe $300 or so billion. So we've seen the exponential speedup of spending in the artificial intelligence world, and if you compare, sort of, the known quantum numbers to those numbers. There is no comparison, at least not yet. But at the same time, what Elvira is alluding to is exactly right, which is… We're talking about a certain class of problems here that, even if in some cases you ran the NVIDIA system for the next thousand years, you would still not get an answer. Think about that. Running the Vera Rubin system without stopping for the next thousand years, and you still don't have enough computing power to reasonably get an answer to certain types of questions, certain types of problems. Certain types of chemical simulations. So we're unlocking the ability in certain areas, chemistry is one of those areas, material science, batteries, these areas, we could see very, very interesting ultimate developments, and Elvira turned me on to another area that is important, you think of AI, like, we're basically using all the data of the internet to train a lot of these models today, but you think of, if you can run these systems effectively and efficiently, you could use them to create interesting, specific, and precise data sets for example, in chemistry, that can then feed back into training specific and precise AI models that are then able to get better and better results, because the quantum computer was able to simulate and to get at data that you couldn't even get, running, things in the old way, and data that didn't even exist. on the internet. So there are feedbacks in terms of how AI and quantum computer could essentially effectively improve each other. But I do recognize that it is… 11.31, so I apologize, I'm one minute over, Irene. I know we could go all day on this topic, but we don't have, you know, any more time, so I guess, we'll have to follow up with anyone who asks a question, and we appreciate people taking 30 minutes, with us today.

WisdomTree Events

Yeah, we'll definitely have to get you both on again, and thank you again.

Elvira Kuramshina

Thank you.

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Christopher Gannatti is a registered representative of Foreside Fund Services, LLC.

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