*WE NOTE THAT THE FOLLOWING TRANSCRIPT WAS CREATED BY A ROBOT SO PLEASE FORGIVE ANY TYPOS.*

**[Tom Zuber]**

Hello. I’m Tom Zuber. I’m the managing partner of Zuber Lawler, and I sit on the editorial board of Dead Cat Live Cat. I’m here with Julien Camirand Lemyre. He’s with Nord Quantique, and very excited to have him here. We’re going to start off by asking you a very basic question. Julian, first of all, thank you very much for taking the time to speak with us. And from where are you speaking to us?

**[Julien Camirand Lemyre] **

Speaking with you from Sherbrooke. And also, very happy to be to be on this panel with you today.

**[Tom Zuber] **

Well, from Los Angeles, California. Thank you very much for joining us and tell us a bit first about the inspiration behind the founding of Nord Quantique.

**[Julien Camirand Lemyre] **

Yeah, so, so the founding of Nord Quantique has, like many angle to it. One, of course, is technology. So once we started the journey of not Quantic, there was, there’s one major problem that the community is facing is really having access to a quantum computing technology that has qubit that makes sufficiently low error rates. And this is a real challenge. This is a challenge that most of the companies out there is, most of the scientific people who are doing pure research to big industry players that are trying to face, trying to find ways to reduce error rates in quantum technology, to make sure that these quantum computer can have useful impact on the society later on. And the challenge that we were up to is, is taking a different angle, different shot at how we should build a quantum computer that can tackle this challenge more efficiently. And this is where we started. So having this technology, so we are a spin off from Institute on sick and we were spin off from one research lab in particular that also specialize in building qubit that can be error corrected by themselves. And it’s the mapping between these types of this type of technology, and also the potential to build a quantum computer out of it, that gets us started off the ground.

**[Tom Zuber] **

So I want to spend quite a bit time on your focus on error correction, but before we get there, just in the most basic layperson terms, are you electron focused? Are you photon focused? And also, part of our goal here with dead cat live cat is to take what seems incomprehensibly complex and they try and distill it down into simpler terms. Can you tell us as simply as possible. What’s the difference? Yes.

**[Julien Camirand Lemyre] **

So we are our platform is a super connecting qubit platform, but we do love photons a lot. So there’s a lot of similarities between the world of photonic and what we are doing. So what we’re building is super connecting circuits that can trap many photons. So we’re building these resonators in which we can store many photons for millisecond lifetime. And out of these photons, we can build quantum states that can be used as qubits. So this is a different technology from what you’ve mentioned earlier. So spin qubits. So in SPIN qubits, you would try to address the single spin of an atom or an electron, which in microelectronic circuits to build a quantum computer out of it. In our case, it’s really like these superconducting circuits that behave as a two level system. But in our case, the fact that we have many photons trapped inside these resonator is also the reason why we can do quantum error correction on these states.

**[Tom Zuber] **

So I want to talk more about precisely how that works, and we’re not going to be afraid to get a little bit nerdy here on this to get up. In other words, the first question, though, is, so what does that mean when we’re dealing with qubits comprised of electrons and focused on electron spin? That makes sense. It’s spinning this way, and it’s a one. It spins that way, and it’s a zero in terms of analogy to classical computers. What’s the what’s the analogy here that we can look to to understand what you’re doing, trapping these photons into a into a singular compartment for ability lifetime? Yeah.

**[Julien Camirand Lemyre] **

Yeah, exactly. So, so the analogy? So the analogy goes back to error correction. So I will take that detour to get you a little bit of the understanding of what is the challenge between error correction. Why do we care about this and also, like in the end, so why do we trap many photons into a resonator to solve that challenge? So this is a route that we’ll be taking. So in error correction, winner is classical or quantum, there’s winning region that can make it work. For example, if you have a bit in quantum and classical communication, and you’re trying to send that bit over a long distance, maybe in space. Maybe you have a satellite that is exploring the universe somewhere very far, and you’re sending very faint signal to that satellite. The thing is, like your bit, maybe you’re trying to send a zero, but after a certain number of time it can, it can be corrupted and flipped to a one, and then your strategy in that case is, we will be to try to add some level of redundancy to the bit string that you’re sending so that if you have q of these bits that flips, you can recover from from that so instead of sending a zero, you can send a strings of zero. Let’s see three or five or 1001 bits do. To that satellite. And if you have many of these bits that can flip, but you can, you can now recover just by doing a majority vote. So the key ingredient here is really redundancy Julian,

**[Tom Zuber] **

that makes sense, and I think that that’s quite digestible, too. So what we’re saying here, what you’ve just said, if I understand correctly, is that, as opposed to sending one qubit, you’re sending a number of them, and so you can identify the error as an anomaly amongst a larger swath the duplicity of redundancy is that what I just heard so you may have, let’s speak, just be silly. In terms of simplicity, let’s say you have 10 qubits. You’re sending 10 qubits that are supposed to indicate the same precise data point, but you’re doing it 10 times over. So that way, if over time, one or two of these become errors, those will be recognized as anomalies. If you have enough redundancy is that you just made

**[Julien Camirand Lemyre] **

it, this is correct, so Exactly. So in quantum computing, the analogy goes, so if we have qubits that now are faulty, we can do the tricks that you just explained. So instead of using one, we can use an arrays of many of these qubits and run an algorithms of these qubits that can detect and correct the these anomalies, so these errors as they occur in the chips. So this is the driving principle beyond quantum error correction. The challenge, though, is that, as you just described it, this approach needs many qubits, so you will need, like many of these qubits, to encode what we call this logical qubit that can now be error corrected. And many in the industry can be, can be very large. So to target the error rates that are useful for applications, let’s say the chemistry or pharmaceutical sector, we will need 1000s to 10 1000s of these physical qubit for every logical qubit that will be running into the algorithm. So the overhead is massive. So what we are trying to do at Nord is something a bit different. We’re trying to embed this redundancy into a single physical object and try to play this error correction game onto that single physical object, so that we lower the cost in terms of number of quantum objects that we need to operate to get to these error corrected qubits. And the way to do it is so we cannot move around redundancy. We still need this element, but what we can do is, like, localize it in space. So this is how what we do. So we build these resonators in which we can store many microwave photons, and then we encode these photon into a state that can be error corrected. And the fact that we have many photons is also linked to the to do, to our capability to do quantum error correction on these states.

**[Tom Zuber] **

So you’re saying you convert the photon to a state in which there can be error correction. Can you elaborate on on that? What does, what? What do you mean by that? In more, yeah,

**[Julien Camirand Lemyre] **

exactly. So, for example, in these resonators, a common error that that occurs is like we’re losing a photon. So a photon leaves the resonator. So, so this would, this would be an error. So we encode these states, these made of many photons, in such a way that if a photon leave we can, we can detect it and then correct it just as we just as what you were describing earlier, with arrays of qubits. So if one is faulty, we can detect it and correct it. Here, we can do the same with our photon states, but now it’s all within a single physical object without needing to repeat that object a certain number of time.

**[Tom Zuber] **

So what we’re saying then is part of me, and this may be a little bit dense to you, meaning, it may take me a bit to get this, but I’d like to go through it, because it’s the heart of what we’re talking about here. How are you achieving that? So what we had just talked about in the first instance, in the instance of how the rest of the industry is doing it, is that they’ll get a large number of qubits, and they’ll basically be redundancy. And so you recognize the anomaly amongst a large two, large number of two qubits here. And what you’re doing is you’re compartmentalizing photons into a singular space here, and you’re using that to eliminate the need to achieve such redundancy, to identify errors. And the way that that happens is you’re identifying when a photon leaves the environment. So a couple of questions here. One, exactly, how does that you’ve identified Now, if you’ve got a bunch of photons here, do you still have redundancy inside that compartment with with multiple photons for each data point that you’re trying to each each point of data that you’re trying to memorialize here into and to manipulate and to incorporate into a solution, or does it mean something else here? How is that happening here? And furthermore, if a photon leaves the environment, it doesn’t tell you the data point that the photon had, right? So, how do you know if you’ve got a plurality, if you’ve got a large number of photons in the compartment here. How do we know which data point corresponds to the photon that left, right? I mean, that’s, that’s, that’s something else as well. Because what we’re talking about here is a disturbance of a data set correct

**[Julien Camirand Lemyre] **

exactly so. So let’s say in these resonators the way we see it, so the photon state that is being created inside this resonator will be a logical Q. It Okay, so qubit that is encoded, so in that cube, in that qubit, if a photon loss is lost, is one to the to the to elsewhere in the chat, for as elsewhere in the universe, we can apply a quantum error correction protocols that will detect that error. So this is what the protocol will do, and then re inject energy if needed, once, once the error is detected. So we can recover from that error. So we we detect the error, then we understand that something bad happened, and then we can correct this error with the protocol. So this is just a series of gate, but this is one qubit. So if we, if we, if we need many of them, we then just duplicate, or replicate this. These, this arrays of resonators that are coupled together through coppers, and then this is how we build, build architectures

**[Tom Zuber] **

understood, okay. And on this front, how many others are looking at this problem of error correction the same way as newer Quantique? How many is this widespread? Is are you singular? Is it somewhere in between? So

**[Julien Camirand Lemyre] **

I wouldn’t space this widespread at all. So this, this technology, has been coined as bosonic codes. So we’re leveraging the bosonic nature of photons to encode, to encode these logical qubits. So there’s few actors working on different piece of technology. Amazon, for example, has been public about like, trying to explore these, these, these approaches, but on this specific way we are doing it, and just in the industry. So we’re basically the only one really looking at this. There’s also some academic research that are looking at these coasts, trying to find, like, what’s the best performance, how we should we build these, these systems? And we do have a research chair, for example, with the researchers at the Institute and cherbrooke, also looking at this approach and trying to figure out, what are the best path toward error correction that has vanishing error rates with these systems,

**[Tom Zuber] **

very good. What is your technology roadmap will look like? What does your IP roadmap look like? In other words, what are the milestones that you’re looking forward to over the next year, the next two years, that you see as the key milestones for you to really hit your target as a company,

**[Julien Camirand Lemyre] **

definitely. So going back in the past a little bit, so our first milestone as a company was really to demonstrate the this capability of this technology that we built to correct and detect error so playing this quantum error correction game. And this is, this is what we did in 2023 showing a logical qubit at that longer lifetime than that, then the non corrected qubits, basically showing this quantum error correction properties. And now, of course, we don’t want a computer with a single qubit. So so we are working hard on improved quantum error correction on one side, so do us as good as we can on the system, but also couple them together, and this is the next milestone that’s coming up with this year for our company in the in the coming years, what we’re targeting is really two things. One is really higher performance quantum error correction, moving toward with like improving the protocols, improving the hardware as well, moving toward this very high fidelity qubit, and on the other hand, is scaling the technology to handful of qubits and then a larger chip size in two

**[Tom Zuber] **

years. Fascinating on that notion of larger chip size within two years. Let’s talk about scalability, right one? How is Nord Quantique looking at scalability, and what breakthroughs do you see for quantum computers in terms of scalability on the horizon in the next year or two or three, not just for nordpoint teak, but for the entire industry here, where are we as an industry in terms of scalability? It

**[Julien Camirand Lemyre] **

depends what you mean by scalability, and we have a very we have a bit of different notions of what scalable means. But let’s just talk about where the industry is right now and give you our viewpoint on this afterwards. So I think the industry has progressed tremendously in the last three to five years. Really impressive to see all these different technologies, whether it’s superconducting, photonic ion traps and others, scaling different architectures toward larger and larger machines. And what we’ve seen with these is progress, not only on the chip, but progress on access. So it’s more and more well known how we can access these quantum computer, how we should run algorithms on these so both the hardware and software stack has been developing and trying to build the industry as a whole at the same time, I think what we’ve learned in the past year is that it is hard to scale very large, large one on computer without being hit by the fact that if you have cubist that has certain error rates, and right now, the error rates are about one ever, one error every 1000s or 10 1000s operation or so. You’re also limited in the size of a chip you can build. So there’s no. Point in just like brutally scaling this technology, because you you don’t access the property of larger chip if you don’t have better error rates. And this is, this is why we also think of scaling, also about like we think of error correction as a way to scale chips. So the best error correction you have, the best way you can reduce error rates on single logical qubits as a form of scaling the technology, because you lower also the overheads you need building a fault tolerant quantum computer. And I think, like the industry is also picking this tangent, developing various approaches toward quantum error correction and fault tolerance. And it will be interesting to see also how the different modalities can leverage different quantum error correction codes as the field progress toward forward.

**[Tom Zuber] **

So 1001 where do we need to be? In your view, like, where are you trying to get to as a reasonable within the next few years? As far as narrow correction rate, where do we need to be, and where do you intend to take us? Nordwatik,

**[Julien Camirand Lemyre] **

so yeah. So. So for Nord, the objective is to to reduce error rate as much as possible. So if you look at applications for to run application, let’s say, in the material sector, so the earliest application you could run on such a computer, you’ll, you’ll need error rates around 10 to the minus six. And this is, this is really the target that we are aiming to achieve, but trying to aim to that target also like keeping in check the amount of redundancy we need to the physical like the number of building blocks we need per logical qubit in check because, like this, is also being a scalable technology. Don’t want to be hit by these overheads every time you you want to have a larger system,

**[Tom Zuber] **

very good. How long do you think it’ll take to get to 10 to the minus six rate of error correction of error?

**[Julien Camirand Lemyre] **

I think we’ll be there in the next few years. How? How long? Exactly, is still hard to protect. So there’s a long whatever the technology, there’s a lot of bottlenecks, whether, whether it just on pure hardware performances, but also on the types of codes that can be run on the system. In our case, we want to be there before 2028 this is this is our objective. Very

**[Tom Zuber] **

good. Let’s talk a bit about the industry in general and quantum computing in general, and the applicability of it to global commerce in general. How far away do you think we are from the commercialization of quantum computers to material degree where you have everyday businesses that will start to tap the power of quantum computers, not necessarily daily, but on a regular basis. How far away do you think we are from from that type of world, and what industries do you see leading the way in terms of the usage, not the quantum computer industry? I’m not talking about that. I’m talking about other industries that are actually tapping the quantum computing industry for quantum computing power. Where are we on that front in your view?

**[Julien Camirand Lemyre] **

Yeah, I think again. So the field has been developing a lot at that interface so between industry and also the quantum computing hardware providers. So this is something that is developing, I think, in the coming years, with evolving quantum computing capability from the Divine providers, also having offers that are more and more accessible to end users, will accelerate the development. However, it’s like we’re not yet ready to access like in day to day workflow, in daily workflow. So we don’t see quantum computer being in use right now. And part of the issue is because we don’t have machines that have the performances that will drive the businesses right now to migrate their current workflows to the quantum computing workflows. And this is, this is coming, and what needs, what we need in terms of development is both algorithmic development and also hardware development. And a good example of like, what types of industry are interested in this is what the work that we are doing with people like oti lumionix. So oti lumionix is a Canadian startup. We’re developing OLEDs for giant industry like LG Electronics and these sort of players that are interested in having access to all that technology, and they are interested in improving the their r&d pipelines, and they’re looking into the quantum computing space and looking to providers that will enable them to simulate their molecules better. And they’re pairing with narquantic In that case, because of the unique capabilities that our platform will bring to their value change, basically. But at this stage, what we’re developing is both the algorithm on the oti side to try to figure out where our technology will be impactful for their for their use cases. There are many, many endpoints where we can connect our technology to do better simulation. And this is what we need to explore, where exactly the disruption will come. And, yeah, and the part, of course, this is, this is the job of the providers like us, is just to big build and provide better hardware to these companies so that they can also try to send their own algorithm and see, like, what the performance, what’s the benefit they can get from this technology? Yeah. Yeah,

**[Tom Zuber] **

fantastic. I’ve got one last question for you. I wish I had time for more Julian, and thank you very much for a great conversation so far. Just in a few words, besides error correction, what’s the biggest challenge besides error correction and scalability? What’s the biggest challenge facing the quantum computing industry today in terms of getting to a place of mass adoption, and I mean as precise as possible.

**[Julien Camirand Lemyre] **

Yeah, I think, I think the major challenge is just the one that we described. So like solving also the algorithm inside of things like finding new use cases, new algorithm that can run on different types of machines, that can have an impact for workflows. And there’s needs, there needs to be involvement from the community. There needs to be involvement from the end user themselves. Invest in these, invest in this early development, so that once the machines are ready and once quantum error correction is there, we can, we can, we can tie these together and have an impact in the industry.

**[Tom Zuber] **

Julian, that was a wonderful conversation, conversation from my perspective, and I learned a lot, and it’s been a great pleasure having you here. Thank you so much, Julian. I’m rooting for you and rooting for North Quantique, and I hope that we’ll get an update in a year or two to see how you’re doing and how far along the business plan is.

**[Julien Camirand Lemyre]**

Thank you so much Tom for having me here. It was a pleasure.

**[Tom Zuber]**

Thanks so much, Julian, bye, bye.