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**Filing Date:** 2026-1
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## Filing Summary
**0001213900-26-005423.hdr.sgml**: 20260120

**ACCESSION NUMBER**: 0001213900-26-005423

**CONFORMED SUBMISSION TYPE**: 425

**PUBLIC DOCUMENT COUNT**: 1

**FILED AS OF DATE**: 20260120

**DATE AS OF CHANGE**: 20260120

**SUBJECT COMPANY**: 

**COMPANY DATA:**
- **COMPANY CONFORMED NAME:** dMY Squared Technology Group, Inc.
- **CENTRAL INDEX KEY:** 0001915380
- **STANDARD INDUSTRIAL CLASSIFICATION:** BLANK CHECKS [6770]
- **ORGANIZATION NAME:** 05 Real Estate & Construction
- **EIN:** 880748933
- **STATE OF INCORPORATION:** MA
- **FISCAL YEAR END:** 1231

**FILING VALUES:**
- **FORM TYPE:** 425
- **SEC ACT:** 1934 Act
- **SEC FILE NUMBER:** 001-41519
- **FILM NUMBER:** 26541345

**BUSINESS ADDRESS:**
- **STREET 1:** 1180 NORTH TOWN CENTER DRIVE SUITE 100
- **CITY:** LAS VEGAS
- **STATE:** NV
- **ZIP:** 89144
- **BUSINESS PHONE:** 408-232-2139

**MAIL ADDRESS:**
- **STREET 1:** 1180 NORTH TOWN CENTER DRIVE SUITE 100
- **CITY:** LAS VEGAS
- **STATE:** NV
- **ZIP:** 89144
**FILED BY**: 

**COMPANY DATA:**
- **COMPANY CONFORMED NAME:** Horizon Quantum Computing Pte. Ltd.
- **CENTRAL INDEX KEY:** 0002088257

**ORGANIZATION NAME:**
- **EIN:** 000000000
- **STATE OF INCORPORATION:** U0
- **FISCAL YEAR END:** 1231

**FILING VALUES:**
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**BUSINESS ADDRESS:**
- **ADDRESS IS A NON US LOCATION:** YES
- **STREET 1:** ALICE@MEDIAOPOLIS
- **STREET 2:** 29 MEDIA CIRCLE #05-22
- **CITY:** SINGAPORE
- **PROVINCE COUNTRY:** U0
- **ZIP:** 138565
- **BUSINESS PHONE:** 6581015024

**MAIL ADDRESS:**
- **ADDRESS IS A NON US LOCATION:** YES
- **STREET 1:** ALICE@MEDIAOPOLIS
- **STREET 2:** 29 MEDIA CIRCLE #05-22
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**Filed by Horizon Quantum Computing Pte. Ltd.<br> Pursuant to Rule 425 under the Securities Act of 1933, as amended, and deemed filed<br> pursuant to Rule 14a-12 under the Securities Exchange Act of 1934, as amended<br> Subject Company: dMY Squared Technology Group, Inc.<br> Commission File No.: 333-292737**

***The following is a transcript of an interview with the CEO of Horizon Quantum Computing Pte. Ltd., Joseph Fitzsimons.***

Event: Rosenblatt Securities' Quantum Computing Series 2026

Date: January 16, 2026

<>

All right, welcome to Session 2 of the day, as this wallpaper they made says I'm John McPeake, Senior Analyst here at Rosenblatt Securities. Good morning. Welcome to the conference and happy Friday. This is the second session of the day. Thank you for joining us at our inaugural Quantum Series Conference today.

Next up is Horizon Quantum. This is a company that's bridging the gap between classical and quantum computing by a hardware agnostic software stack from the qubits to the application layer. It's very intriguing to me as a software analyst historically. We're really lucky to have the CEO and Founder of Horizon with us here this morning, Dr. Joe Fitzsimons.

Before founding Horizon Quantum in 2018, Joe held a tenured position as associate professor at the Singapore University of Technology and Design, where he led the Quantum Information and Theory group. And he also spent time at Merton College, Oxford as a senior research fellow in the Materials Department where he holds a doctorate from the University of Oxford. The research focused on quantum computing architectures. He has a Bachelor of Science degree in theoretical physics from University College Dublin and a doctorate from University of Oxford. So I feel very inferior from an educational standpoint to our guest. Thank you for joining us, Joe.

We have kind of a rough 15, 15, 15 structure today. We're not going to hold that exactly 15 minute presentation, 15 minute fireside chat. Then we'll open it up for questions via the system. Please don't be shy. So, Joe, go right ahead and tell us about Horizon Quantum.

<>

Great, thanks very much, John. So I'm sure you're all familiar with the customary disclaimers, but let me start off by telling you what our focus is at Horizon. We're really focused on getting to a point where quantum computers, where applications running on quantum computers are delivering real value for the end users of those applications. And our focus is really on building the software infrastructure to enable this. And I would love to say that that's already the case today, but the reality of quantum computing is that it's not quite at the point where applications running on quantum computers are regularly solving hard problems. And that's really a major focus of the field. How do we get to that point as quickly as possible? How do we make quantum computing as useful as possible for tackling really difficult computational problems?

But at least from my side, this is a two-sided problem. There is both a software side to it and a hardware side. And on the hardware side, things are looking pretty interesting, at least from my perspective I feel that quantum computing has reached something of an inflection point. And there's a couple of different reasons for this. One of the reasons I would say a real major milestone in the field in my view is the fact that we have seen multi-round error correction achieved for the first time in late 2024, where error correction was actually improving the performance of qubits. So, the lifetime of the quantum information was being extended.

Now, does that mean we've managed to squeeze out all of the errors yet? And by we I mean the community, not Horizon. No. Quantum computers are still swamped by errors, but there is really interesting kind of threshold behavior in quantum computing where if you're above the error correction threshold, if your operations are not quite precise enough, then when you try to do error correction, you make things worse. And now, although we've known how to do error correction for 30 years, it's only really in the last year or so that it has become – started to become possible and now things are starting to get better.

And as you start to improve, if you get say a twofold reduction in your error rate, but your error encoding can correct up to nine errors, meaning that you need 10 errors before you get an actual error in the encoded information, then each factor of two improvement in the physical error rate is a factor of 1,000 improvement in the error rate on the encoded information. So once things start going in the right direction, they can go pretty quickly. And it's also the case that the overhead you need to do this kind of error correction has come down substantially. In the last four years, there has been a major change in our understanding of quantum error correction codes.

There has been the discovery of asymptotically good codes, which have just much better properties that – than was previously taught. So for a long time it was thought that you would need maybe 1,000 or even 10,000 physical qubits for every one error protected qubit. And that number has fallen off a cliff with the introduction of quantum LDPC codes that really have much better parameters than the standard surface code that had been explored before.

So now instead of thinking of 1,000 or 10,000 to 1, the community is starting to look at 20 to 1, 10 to 1, 5 to 1. And that just means the complexity of the overall quantum computer you need to do, you need to build to get to error free computation or at least massively reduced error rates is a lot less complex than we thought it was five years ago. And we're at the point now where several different frontier systems are convincingly hard to simulate. So it doesn't really matter how many GPUs you can scrape together, you can't really simulate what's going on, on some of the best systems that are kind of currently being demonstrated.

And then the last thing that's happening is that there are multiple different platforms emerging and maturing at the same time. So for a long time you had maybe superconducting qubits and trapped ions that were right at the front and I'd say probably in the commercial world, they're still the largest or most advanced systems that are available. But you're also seeing photonics and neutral atoms maturing rapidly as well. So there are multiple different paths emerging to get to large scale quantum computers. And all are kind of making simultaneous progress, which to me at least is very exciting.

But at least from my perspective, the hardware alone is not enough. These things without software to run on them, without the software infrastructure to develop applications to take advantage of them, they're exquisite physics experiments, but they're not yet really computers, they're not yet programmable systems that you can really turn to tackle hard problems. Of course, there are programming frameworks for them, but if we really want to make use of these systems, we need to get to higher levels of abstraction, we need to make it much easier to develop applications for them.

Where we sit as Horizon, I mean, it's somewhat self-aggrandizing, but I like to think we sit at the heart of the quantum computing ecosystem in that the software infrastructure we build connects developers to end users and to hardware providers in the sense that we are building the programming languages and compilers for developers to develop complex applications to take advantage of quantum computers. We're building the queues in the runtime environment to be able to execute those programs on hardware and we're focused on supporting as wide a range of hardware as we possibly can. And we're building the deployment infrastructure so that when you finish – when the developer has finished creating an application, they are able to wrap it up as a web API, to easily integrate into any user-facing application, into any user-facing interface, so that their end users can make easy use of this without them having to manage the deployment infrastructure themselves, without them having to stand up servers, and so on.

So as I see it, there are basically three barriers to getting to really useful applications on quantum computers from the software side. Of course, there is also a hardware angle to this where we need to build more accurate and more capable systems. We need to increase qubit count, we need to reduce the error rates in the hardware.

But on the software side, there are real challenges. The first, I would say, is that if you want to take advantage of quantum computing to solve a hard problem, you really need to take advantage of quantum interference. If you are not taking advantage of this effect from quantum physics in your program, then your program could have been run on a conventional computer, it could have been run more reliably and more cheaply on a conventional computer.

So you need to take advantage of this, this kind of quantum physical effect. But this isn't something that humans have an actual intuition for. In particular, we just don't interact with quantum phenomena in our daily lives, we don't experience quantum interference. So the way we think about approaching problems is much more like how we do calculations on pen and paper, things like this, which pretty closely maps how we approach tackling problems with conventional computers. Just as with a pen and paper, if you are doing a calculation, you maybe look down at the page, you read some information into your brain, make a simple manipulation, and then record the result. The CPU and a conventional computer is doing much the same reading in a bit of information from memory, doing some simple manipulation on it, and then storing the result back in memory.

So how are we going to take advantage of these quantum effects in order to more efficiently process information? I would say the only proven way to do this is essentially to bang your head against the wall for 10 years, trying dozens of different ways to solve a problem, maybe failing 99 times, and succeeding only once. And that maybe gets you to a quantum algorithm, but it's slow, right? It takes a lot of time to build up the expertise to do this, it takes a lot of time to get good at doing it. The number of people that have been involved in more than two quantum algorithms is only a few hundred people worldwide.

The next problem you have is that the hardware is extremely diverse. So you have different kinds of platforms based on different kinds of underlying physics. In some cases, the instruction sets are very different, there are different constraints in terms of how programs need to be constructed. And in some cases, even the underlying physical model is different. Sorry, I should say the underlying computational model is different.

So, for example, in photonic quantum computing, you really need to do all of the computation via measurements, because photons just don't directly interact with each other. And that's a major difference from how you would approach it with a superconducting system or a trapped-ion system, for example. And then the third challenge you have is that the programming languages really lack abstraction. So by that, what I mean is that many of the existing frameworks for programming quantum computers are ultimately piecing together a circuit, one logic gate at a time.

And maybe they're leveraging the infrastructure of Python to do that in a programmatic way. But it's still like trying to design an application or to build an application by designing an integrated circuit to implement it. If you had to put together your applications one NAND gate at a time, it would be very challenging to build anything that looks like modern computer software. So at least in my view, to get us to the point where quantum computers are solving hard problems that are really creating value for end users, hardware's only half the – is only half the solution. You also need software to get there. You need to build the software infrastructure to enable developers to tackle these problems more efficiently.

So what we have been focused on at Horizon, essentially since inception, is the question of can we make programming quantum computers more like programming conventional computers. And the real reason we want to do this is because as I've said, there's only a few hundred people that have shown any kind of success in constructing quantum algorithms. But if you look at the number of people out there in the world that write code regularly, it's a very large number. So, if you were to look at the number of active GitHub accounts or something like that, you'd see this as many tens of millions of people.

So in that situation, you think how are we going to approach this problem of building quantum applications for the domains that really depend on them. So – or that can really stand to benefit from quantum computing. So domains like pharma, for example, domains like AI and machine learning, but also domains like finance and the energy sector, because the people that are domain experts in those fields are not experts in quantum computing, and the people who are experts in quantum computing are not experts in those fields.

So if you think about the way of trying to address this, currently there's maybe three different approaches. You can try to view it as an education approach where you try to teach people more about quantum computing and hope they become able to develop their own applications. You can view it as a libraries approach where you kind of pre-program some quantum applications and make them accessible through a Python library or something like that, so that it becomes easier to access them, easier to apply them. But this maybe isn't so flexible in terms of allowing people, allowing developers to tackle new problems in quantum computing that maybe haven't previously been explored.

And the third approach you have is kind of a professional services approach where it becomes a consulting problem. You get together a room full of experts in quantum computing and then you approach the potential end users of quantum computing and look to solve that problem for them. And that can work fine, it can make a lot of sense for high value problems. But the reality is it's not very scalable, it's not how most modern software is developed.

So from my perspective, if we can make this much more like programming conventional computers, then we get to a point where we're really able to do a lot more. We're able to open up quantum computing to a much wider audience. So what we've been trying to do is to build a pathway from programs written for conventional computers all the way down to an accelerated implementation running on quantum computers.

That means we've had to put together the technology, we've had to develop the techniques to automatically construct quantum algorithms from code written for conventional computers. And we've had some success with that. We've had some standalone demos, we've some patents granted on this process, but we've also had to build the compiler stack and the languages to go from a very high level description of a quantum program all the way down to an concrete implementation on hardware. And that has meant overcoming many of the limitations of existing systems. Many quantum computers that are available today are not capable of doing things that you would take for granted on even the simplest conventional computer. They're often not able to do loops, for example, and when it gets to things like dynamic memory allocation, that's completely non-existent in quantum computing.

So this process of going from a very high level description of a quantum program down to implementation on hardware is what's available today in Triple Alpha that we work with mostly hardware partners with the systems in early access.

So our users are really mostly from the hardware companies and we're working with them closely to see what we can do to better support their hardware with our tools. Because our focus is really on getting to a real concrete advantage or we're solving hard computational problems as soon as we possibly can.

As I say, what we're trying to do with our system, with our programming languages and with our runtime environment is to extend the capability of hardware systems. So if you look at many of the systems that are available today, current generation systems are often not able to at least in their commercial offerings are not able to do things like mid-circuit measurement or control flow, or at least those features may be poorly supported or not uniformly supported across hardware providers.

And once you get down to things like dynamic memory allocation, which is usually something that the operating system takes care of for you, finding the space to store a variable for your program that's not implemented at all. That has to happen at compile time in quantum computing. And that only works when you don't have while loops. So it only works because we don't have very capable quantum computers today.

But when you access these same systems through our tools, you're able to express programs that make use of all of these features in our languages and you're able to execute those programs on hardware today using our runtime environment. We've also become, I believe, the first quantum computing company – sorry, the first software company, I should say, to start operating its own quantum computers. So we have a hardware testbed in Singapore where we've been working to integrate our software stack very tightly with the control systems, so that we can get as close as tight as efficient an implementation of quantum programs as possible. So that we're minimizing the overhead to be able to do things like these loops, recursive function calls, to be able to implement things like dynamic memory allocation in real time on the system.

Where we stand as a company, our business model, what we're trying to do is essentially focused on building not just the infrastructure for developing programs, but also for distributing and executing them. So applications essentially run through our infrastructure and that puts us in a position where we can charge in a kind of AWS like way, where we're charging based on the usage of the resource that the program is running on and the value of the resource you're accessing, which is I would say a very familiar model to anyone that builds web applications today.

And it's kind of aligned with value creation for the developer in the sense that if you build an application and you use it only once or twice, there's no significant cost to that. But if you have an enormous success, you build an application that is creating real value for the end users, it's getting used all the time, then hopefully we've created a lot of value for you as the developer, and it's fair to charge accordingly.

So I will leave it there. But just to recap, what we are focused on as a company is really trying to become the default software layer through which quantum applications are developed and deployed. And we're trying to do this in a way that insulates the developer from the risk of changes in hardware, so that they do not have to place a bet on which hardware is going to win the race to becoming large scale, low noise quantum computers.

Thanks very much. I'll leave it there and happy to move on to the questions.

<>

Thanks, Joe. That was great. I am asking every participant in the conference today, I'm going to ask every participant in the conference today the same question and see what your answer is with two time horizons 2030 and 2035, if one, obviously we'll talk about it in terms of relative to 100%. How likely do you think by 2030 and then by 2035? And if you have another date, you want to tell me, that's fine too. How likely do you think it is that we'll have quantum computers that are doing something that is actually commercially productive by 2030 and then by 2035? And again, if you think it's longer than that, let me know.

<>

This is a great question. And actually I usually prefer not to give my own answers on this because I think anyone person is going to be off, I mean, some people might get it right, but there's a lot of unknowns in this. There's a really – in my view, there's a report that's very useful that's written each year for the Global Risk Institute by two professors in the area, Michaela Mosca and Marco Piani, that tries to anticipate the timeline to quantum computers. The report's mostly focused on when do we expect to see quantum computers that can break cryptography in particular RSA 2048. And so what they do is they go out and they ask about 50 different experts in the field, they send them survey and it kind of asks you to give your confidence interval for seeing a quantum computer that could break RSA2048 in 24 hours in different time bins.

<>

Yes.

<>

And so they collect that information. I find that really useful. And you see the crossover there. If you look at that, the majority opinion goes from thinking it's less likely than not to more likely than not in the 10 to 15-year window. And if you stare at it really closely, it looks like maybe it's around 12. At least that's how it appears to me. But what I would say is that's kind of last year's report, they also asked the question of when are we likely to see commercial applications of quantum computing? And there the crossovers in the three to five-year window. So that will be ahead of 2030 for first commercial applications.

And as I say, that's last year's report, so two to four years. And I would say I'm pretty aligned with that, probably a little bit on the optimistic side. So I would hope to see things maybe a little bit sooner than that. The other thing that has happened since then is that in the year since the report, we've seen maybe four different claims to have achieved a meaningful application of quantum computing. And of course, whenever a claim like this comes out, it takes some amount of time for the community to come to a consensus as to whether that has actually been achieved or not. And at least in my view, this is a little bit like if you look at computers playing chess or playing Go, that you have this gray period where it's not clear whether the computers are better or worse than humans, right?

So there's a long period which humans could always beat computers at chess, could always beat them at Go, and then there's a gray area, Deep Blue beats Kasparov or AlphaGo beats Lee Sedol, where you get into a point where there's argument, right? There's no clear consensus. People are saying, well, just on the day, and maybe if it was a different player, it would have been a different outcome and so on. But then you get, you fast forward a year or two and it becomes unambiguous. It becomes very clear that – I mean, it's very clear today that computers are better at both of these games than humans are.

<>

Right.

<>

I mean, unbeatably so. I think we will see that with quantum computing as well. I think we're entering that gray area where there will be some back and forth and discussion as to whether it has really been achieved or not. And I don't think there will necessarily be a very clear example, but we will move on pretty quickly to a point where it is very clear that quantum computers are beating conventional computers for specific tasks.

<>

So if I parse your response there. So 2030, you're optimistic that by 2030 we'll see a commercially advantageous application somewhere?

<>

Yes, I think so. And I would probably be more optimistic than that, but there's a lot of variance on that, right? There's a lot of uncertainty because you're making predictions about things that are not yet known.

<>

Well, remember, I'm a financial analyst and I make predictions every day. I'm forced to operate in an environment where I'm lucky if I can get to 52% and then I still have to make decisions or make recommendations. And that's just unfortunately the world we live in. So I appreciate your – and would that be most likely probably in chemistry, because I don't think breaking security is necessarily constructive. It could be commercially viable and advantageous for a three letter agency or a sovereign or something that, yeah.

<>

Chemistry is significantly easier than cryptography, that's right. So if you think about Shor's algorithm, really you're talking about billions of logic gates to be able to implement it.

<>

Right.

<>

So it is – you need quite a mature quantum computer to be able to do that. There are chemistry applications that come in at fewer cubit counts and at fewer gates. So certainly you would expect to see maybe chemistry applications beforehand. Of course, there are other areas that people are interested in, condensed matter physics and particle physics. I mean, I would be shocked if we didn't see a pretty clear advantage for some of the calculations that we in quantum computing do all the time to figure out how to build better quantum computers and better software for quantum computing. I mean, I know from my own experience building software for this that there are a lot of places where we could harness quantum computing within our own software stack that would make it more efficient.

It's a little way off. We're not – the hardware is not quite at the point of being able to realize that yet. But I think this is a unique position we have as a software company that we can start to see where in our own tool chain we will be able to make use of that. So I think you'll see early applications, chemistry, things like that, things that are close to quantum mechanics are most likely to be the first ones, I would say.

<>

And you mentioned the circuit depth on Shor's. I know that's a kind of a moving target depending on the parallelism and the algorithm and that type of thing. I'm just curious in the chemistry realm, how useful depths, what are they like? I don't recall.

<>

It's different. There's a whole range of different chemistry algorithms. There's also this separation between types of problems. So we know that you can efficiently simulate like molecular dynamics, any kind of dynamics efficiently with quantum computers. Computing spectra, on the other hand, like working out lowest energy states and things like this is not something that you're guaranteed to be able to do efficiently on a quantum computer.

And indeed they don't always get populated in nature. So you can make certain kinds of molecules, certain kinds of materials that don't cool efficiently, so they get trapped in these long lived metastable states and they just don't reach their ground state. And so although you can efficiently simulate whatever you could see in the lab, it's much harder to make predictions about progress on spectra in general, for example.

<>

Right. Okay. So let's talk a little bit about your company versus generalities. I apologize there, but I'm just asking all the panelists today because I want to see what people are thinking about the time horizons. How do you choose who to work with? I mean, you have limited resources and you have a scarce resource, which is software talent in quantum, triple-alpha. I guess, you have early access requests from more than 40 major corps, 80 universities, 10 quantum software companies, national labs. How do you prioritize and how do you make sure you get leverage, right? Because you don't want to have 16 instantiations of work that doesn't scale over time and has to be supported.

<>

Yes. So at the moment our focus is on trying to get to a real advantage as soon as we possibly can. And that mostly means working with the hardware companies directly. You also want to make sure we're in a situation where we're able to serve the broadest range of customers as possible. And that's also kind of aligned with working with the hardware companies first because ultimately it's the combination of hardware and software that gets you to a real solution to these problems. So we certainly want to make sure that we are enabling the hardware companies to succeed as much as possible.

And in some sense their success helps us as well. So that's where our main focus is at the moment in terms of how we prioritize things. We're really interested in what paths there are to large scale quantum computers in a reasonable timeframe and to low noise systems. Our focus is really on the kind of error corrected side of quantum computing, the low noise regime rather than on variational techniques. So that's where our kind of alignment is.

<>

I got you. And people can look at – look to Bill Gates and say, this is someone that made a fortune on software. And the hardware companies could certainly look to Bill Gates and say he made a fortune on software. While the hardware companies, particularly in the PC space, didn't make a lot of money. Really their margins are much lower and they've been commoditized on the hardware side of the x86 market at least, I'm going somewhere with this. And when I'm old enough to remember back when many computers had – everyone had their own operating system. It could be a Prime computer, Wang computer or whatever it might be. And even some of the workstation companies, Sun had their own OS, although it was a flavor of Unix. So the hardware companies, I would think see software as maybe where the value could get created. How do you convince them that it's a good path to let you and Horizon Quantum kind of own that for them and get them to market faster? I'm curious what those conversations are like.

<>

Well, look, I would say, if you look at what happened historically in PCs, for example, as you made that analogy. It's not just Microsoft that did well. So out of the Wintel alliance, both Intel and Microsoft did extremely well. So there you have both the hardware companies and the software companies in a mutually beneficial relationship where the software is driving adoption of the hardware, and the hardware is what enables the software. So it's a very synergistic relationship in that sense, I would say. Certainly hardware companies have software efforts too. I mean, in some sense they'd be crazy not to. But it's very hard to do two things well and to be focused on two different parts of the stack. At the same time both when you're in this enormous struggle to get to full tolerant computing to get to these large scale, low error rate systems where your focus really has to be on driving the performance of the physical systems.

And on pushing that side forward, and also be focused on, hey, what does the future of quantum computing software look like? So I would say, what you see with a lot of hardware companies is that they definitely have focus on the hardware where, of course, their focus should be, but are also interested in how do you enable program of that hardware and how do you get to applications running on top of that. And I think ultimately, we all recognize the importance of getting of software and of getting to a real quantum advantage as soon as possible. And I think, if you're given the option of getting to a real quantum advantage sooner, that's a winning proposition in most cases.

<>

That makes a lot of sense. And along those lines, how much can you accelerate, like I'm sure you go, I would imagine, I would imagine you go into some quantum hardware company that calls themselves full-stack and they may have a software team there that's somewhat overlapping with what you do. How much can you accelerate the time to market versus an internal team? I mean, it's hard to make generalizations, I bet, but…

<>

These kind of things are often collaborative. So what I would say, is normally we're focused on the stack. Although with our test-bed system, for example, we go all the way down to the control systems and to the shape of the pulses that are getting sent to the processor. We extend up in the stack far beyond pretty much any other effort in terms of building up higher levels of abstraction. So we've just introduced Beryllium, which is an object oriented programming language. And most quantum programming approaches up to now have really been at the level of circuits, at the level of manipulating quantum circuits, even if there are adaptive circuits where there's some amount of executing, some operations conditioned on a measurement.

By pushing up this way, by enabling general control flow, by getting to an object oriented programming language, we get to unlock a kind of network effect around quantum programming. Because you can start to build libraries. Because with an object oriented programming language, you have the idea of objects and classes. You can start to define objects, you can start to define classes that represent specific kinds of information built up of lower level objects, built up of qubits and numbers and things like this.

And then you can build new classes on top of those that are using the classes you've previously defined. So you get to kind of leverage up to get to higher and higher levels of abstraction so that you move away from talking about, how do I represent information in qubits and manipulate it and shuffle them around the chip, to moving up to expressing your program much more in terms of how am I manipulating different types of abstract information?

And that that's getting to a level far beyond where most hardware companies are focused today. But it gets to a point where it becomes much easier to build complex applications, and where you do have this network effect where the developer maybe does not have to make a bet on which hardware is going to win and they're able to build complex applications and execute them on a wide range of hardware.

<>

I love the approach, actually.

<>

Oh, thank you.

<>

Got you. So the business model at scale, I would think looks like a enterprise software model, infrastructure software model with fairly high gross margins and fairly high EBIT margins. Is that the way I should think about it?

<>

So what I would say is that there are – this depends to some extent on the way in which the quantum computers are accessed. So there's – I would say at the moment most quantum computers that are used commercially are cloud based.

<>

Yeah.

<>

So people are accessing them over the internet. And it makes a lot of sense to operate in an AWS like model. There not necessarily where you're operating the hardware yourself, but where you're charging based on the usage of your APIs for the deployed program. Then you're able to charge kind of proportionate to the value of the resource that's being accessed and the amount of usage of that resource.

<>

Sure.

<>

As you move over to on-prem deployments, I would say there's a pretty well-trodden path there with products like Windows Server and VMware where you're charging kind of annual recurring licenses, but you're doing it in a way that is again tied to the capabilities of hardware, in the sense that you're charging based on the number of cores, for example. So I would say there's pretty well established software pricing models in each of those categories.

<>

And in terms of the cost to provide the service though, it's a software like model, right? I mean your COGS, if you will, the cost to provide revenues…

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Yeah, absolutely.

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Yeah.

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Absolutely. I mean, yeah, when it's third-party hardware, absolutely, it's a – we're providing software, we're providing the server. Well, I mean we're running it on servers, but yes, we're just providing a software there. We operate our own testbed system as I've said in Singapore, we anticipate making that also available through our tools. But even with that, our CapEx on that, the components were about $2.3 million, something like that. Sorry, that's a pretty rough number. But to piece together that system which is very different from the kind of CapEx you have if you're a hardware company and you're designing your own chips, for example.

<>

And in this type of model you really want to get developers at an early stage and get them used to your tools. Are you trying to get into educational institutions and have you considered open sourcing any components of this sort of the way IBM did with Qiskit, curious?

<>

So I mean certainly, educational institutions are important to us. We have not yet added academic institutions to early access, but that is something that we would anticipate doing in the future. We're participants in a number of networks including for example the Trinity Quantum Alliance in Dublin where we're – where Trinity College in Dublin has been running a master's programming in quantum technologies. And…

<>

Yeah.

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So we're definitely engaging with academia, I would say on that side. We haven't quite added them to early access yet, but it is certainly something we would anticipate there.

<>

And is it something where you want the product to be more mature. Before you do that, what are you thinking about there?

<>

So initially our focus has been on working with the hardware companies because that's where we are actually able to overcome technical problems to make those systems more useful. And that has been the first step for us anyway.

<>

And that they pay you to do that, is that the way I should think about that?

<>

With the hardware companies, no, that's not our intention at all. We want to ensure that we are the best software platform available to…

<>

Okay.

<>

…be able to program any quantum computer.

<>

Yeah.

<>

And we think we'll win on the merits on that.

<>

So that's like R&D basically to attach to those systems.

<>

Absolutely. So what I would say is that the most important thing in quantum computing right now is getting to a real quantum advantage.

<>

And is it your view that we'll have multiple modalities in the end? Or will it be suddenly PsiQuantum has a million gate computer – a million qubit – million perfect qubits in 2030 and everyone will be like, we have to use that and everything else will shut down. I'm curious, these are questions I get covering the space.

<>

I think what? Well, it's always hard making predictions, particularly about the future, but what I find very exciting about the current time is that you have multiple modalities that are all maturing in parallel. So the fact that you have a viable path for photonic systems, for trapped-ion systems, for superconducting systems, and for neutral atom systems to scale – to scale up significantly and get to lower noise is really somewhat unique.

I think if you look to the far future and say, well, is one modality going to win? I would imagine we would go through a period with hybrid systems before then. If you look at computers, for example, they have, over the history of computing, different modalities have been used for storing information over different timescales. So the technology you use to build a processor has been very different from the – for the technology you use to build long-term storage like a hard drive or a tape or whatever it happens to be.

And I don't really see a reason for quantum computing not to go that direction other than the fact that quantum computing is difficult to connect components in this way. It's difficult to make trapped-ions talk to superconducting qubits, for example. But were that ever to be overcome, then it would make a lot of sense to see hybrid systems. But there's challenges.

<>

Sure, I could talk to you all day, I have so many questions. But do you think that the developers. Is it your view that developers, smart software algorithm writers will overcome or make do with limited hardware faster than the hardware can deliver the qubits that are required to solve hard problems? Kind of like, the Gidney Shore implementation, that type of thing. Do you see more of that happening?

<>

Yeah, I do. I think as the systems become real, particularly if the error rate comes down, then you'll see a lot of – you'll see a lot of work trying to make programs fit within existing systems. And you've seen this, this is like a proud tradition in the history of computing, right? Where there are all sorts of tricks, like there's this very famous – this very famous trick in graphics that there's – there's this peculiar constant that appears in a bunch of code and it gives a shortcut to division.

And so you see a lot of these tricks emerging. And Craig has definitely been at the forefront. I mean, if I was to think of anyone in quantum computing that is coming up with tricks, it's definitely Craig. So, yeah, I think that's definitely the case. But interestingly, quantum computing has hit a kind of funny milestone in that now the largest quantum computers have just eked past the number of bits in an Atari 2600. So it gives you some kind of sense of where things are at. It's less reliable, but about the right number of bits.

<>

Yeah, and he's a researcher. Wait till they have your tools in the market more broadly available to all of the minds that are on planet Earth. I mean, I think these new technologies have been doing this since the 90s. They always – they always have unintended consequence. There's things that happen that I don't think about. I never thought the Internet would allow, I don't know, food delivery or whatever.

I thought about it as a cheap pipe because I wasn't imaginative enough. So anyway, I would like – I didn't realize this, but there was some access difficulties in the beginning of the session. So I just want to let everybody know that the Meat Max platform that we use is nice in the sense that you can just go back and listen to the replay fairly easily. So if anyone had problems getting on this session, you can go back and it has a DVR like function. You can just listen to what you missed.

So, Joe, thank you very much for joining.

<>

Thanks, John. I appreciate it.

<>

Yeah, very good.

***About Horizon Quantum***

Horizon Quantum's mission is to unlock broad quantum advantage by building the software infrastructure that empowers developers to use quantum computing to solve the world's toughest computational problems.

Founded in 2018 by Dr Joe Fitzsimons, a leading researcher and former professor with more than two decades of experience in quantum computing, the company seeks to bridge the gap between today's hardware and tomorrow's applications through the creation of advanced quantum software development tools. Its integrated development environment, Triple Alpha, enables developers to write sophisticated, hardware-agnostic quantum programs at different levels of abstraction.

***About dMY Squared***

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Past performance by Horizon's or dMY Squared's management teams and their respective affiliates is not a guarantee of future performance. Therefore, you should not place undue reliance on the historical record of the performance of Horizon's or dMY Squared's management teams or businesses associated with them as indicative of future performance of an investment or the returns that Horizon or dMY Squared will, or are likely to, generate going forward.

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