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Podcast with Aurelie Helouis, Founder and CEO of InfinityQ Technologies

Aurelie Helouis, founder and CEO of InfinityQ Technologies, developer of an analog quantum computer is interviewed by Yuval Boger. Aurelie and Yuval talk about the types of problems that such a computer can solve, a unique application in the world of online gaming, and much more.


Yuval Boger: Hello Aurelie, and thank you very much for joining me today.

Aurélie Hélouis: Hi Yuval. How are you? And thank you so much for the invitation. I’m super happy to be here today.

Yuval: My pleasure. So who are you and what do you do?

Aurélie: I’m the CEO and co-founder of InfinityQ, a company in quantum-inspired computing. And a little bit of my background, I was a naval aviation officer in the French Navy for 16 years in France, had several responsibilities, especially on board of the aircraft carrier. And I was in charge of the maintenance of the Rafale, the jet aircraft. So, a very interesting journey, and I had the opportunity to travel all around the world. And then, I’ve obtained a degree in IT. And at some point, after becoming the CIO of the naval station, I moved to Montreal to do my MBA at McGill and started working for a big company, Pratt & Whitney. I discovered my passion for entrepreneurship, and I quit my job and became an entrepreneur, and I’m an entrepreneur since.

Yuval: Excellent. What is quantum-inspired computing? What is the product that your company is developing?

Aurélie: That’s a very good question. to get back from the early beginning, I was working at Mila, the AI ​​research center here in Montreal, the research center. And I met a professor who…rewrote a new formulation of quantum mechanics. As We were working on the new foundations of quantum computing, we wondered if there was a way to obtain the same type of results, maybe not that revolutionary, but at least enough to provide significant speed up and the ability to solve new problems, without having the burden of building quantum computers, manipulating small particles, photons or atoms, and building cryogenics, dealing with all these complex materials and equipment. So we looked at quantum-inspired technologies, and found out that developing an Ising machine was a paradigm shift in computing and a good way to solve new types of problems, these NP-hard problems. And there was a possibility to use hardware such as FPGAs that we can configure ourselves to solve the Ising model. So that’s where the idea comes from.

Yuval: Very good. So what kind of problems can the Ising machine solve? Could you give me perhaps a sense of that?

Aurélie: So the beauty of an Ising machine is that it can solve all these combinatorial optimization problems and any NP Hard problem can be translated into an Ising amazing model. Combinatorial optimization problems are everywhere, especially in logistics, everybody knows the famous traveling salesperson problems or path finding, delivery optimization, these types of problems. And right now we are also looking at gaming, as I mentioned, the path finding and the interaction between non playing characters for online games is something that is very interesting. for us, it’s a very interesting and challenging moment because we must use our technology to solve these problems and identify these problems in the real world. that’s the techno push problem. But I think it’s very interesting because we have to open our mind, to think a bit out of the box and to beyond what exists now.

Yuval: How does the performance of the Ising machine compare to classical computers or compares maybe to D-Wave machine or basically any other approach to solving the same kind of problems?

Aurélie: That’s a very good question. Actually, the performance is a bit better for these NP problems because unless you have problems such as the TSP problem, traveling sales person problem, that is very well studied and you have specific algorithms that exist. But if you don’t have the specific algorithm, the classical computers are very, very bad at it and in comparison to the others, we do our benchmark compared to the quantumannealer. the performance is better when we increase into complexity. we are still at the beginning and we will publish a white paper, by the end of the year to give more detail on that.

Yuval: Do you feel that this approach is a short-term solution to solving optimization problems? What I mean by that is in a few years, we expect quantum computers to be better, quieter, more cubits, and so on. Will there still be a need for an approach such as yours?

Aurélie: I think what is really interesting with our approach is the fact that it’s easy to code. There is no extra work to create quantum circuitry.. the simplicity is very important for customers and I think sometimes, when you love technology, you forget it. customers want something easy to integrate, easy to use, and they don’t want too much disruption, otherwise adoption won’t happen. So what we think is once customers will adopt our technology, they would require a big revolution to change technology. So I think we have some time before this happens.

Yuval: And I assume you’re selling a box, is that a big box, a small box? Is that a cloud-hosted box? Is that something that someone would put on-premise? How would you deploy it?

Aurélie: As of now, we’ve developed a local cloud, so it’s more on-premises and we are working with Amazon and Azure to provide access to the machine via their cloud. And the goal is to have our own cloud totally ready in 2023.

Yuval: In what stage is the company, how many people do you have roughly, when will a product be released, what can you tell me about that?

Aurélie: We’ve incorporated in January 2020, so it’s almost two years and a half ago . For now, We are about 10 people, it’sstill a small team, but we already have a working prototype working on POCs with different companies. So still early stage, but more in the growth phase.

Yuval: When you listen to quantum computing vendors, the TSP traveling salesperson problem comes up very often, but I must admit that I haven’t heard too many people speak about gaming. How did that application come about for your company?

Aurélie: At first, we were looking at different industries where technology can be a good fit. So we did the SWAT analysis with opportunities and we discovered that gaming can be a good fit, especially as I mentioned with online games. it becomes more and more interesting, and I would say in the future, especially after COVID, a lot of people became gamers and with metaverse, it opens a lot of opportunities. And I think what is great with gaming, is that they really need solutions. they won’t buy a consulting agreement to explain what can be done in five years, for example. They really want a working solution to help them solve their problem today. So you can’t really cheat with them. It’s like either you provide something concrete, or they ask you to come back with the solution. And another good thing is that Montreal, is a great city for gaming. We have a lot of studios, a lot of big companies in gaming. for us, it’s also a good way to find talent and potential customers.

Yuval: Let’s see if I understood the application, so if you and I are gamers, then we have a character and that character travels through a virtual world, but then there are other non-playing characters, crowd or other villains or some of those and they also need to move through the space. And today it sounds like their movements are not very realistic. And so you are saying that your machine would help figure out what’s the best path for a non-playing character, is about right?

Aurélie: Exactly this and also not only optimization of the path, but also the optimization of their interactions, to make these interactions smarter. that’s what we are working on right now.

Yuval: And that sounds like almost a real-time activity, right? I’m not going to wait five minutes to figure out how a character moves through a virtual universe. So is the machine built to provide very fast answers?

Aurélie: Yeah, that’s our goal. And what we see also, is that for the future with metaverse, we will need more and more real-time environments, so that’s kind of the mindset. We are right now working on .. Improving interaction and at some point, we will be able to apply that to the metaverse.

Yuval: You spoke earlier about the cloud and on-premise deployment. Is there any specific requirements for the machine? Does it need any special cooling or lots of electricity? Anything in particular?

Aurélie: Oh, not at all. It’s very easy right now, we’ve developed a solution on FPGAs. the beauty with FPGA is that the hardware is on the shelf, but you configure everything yourself. So you can customize your own hardware and for us it’s a great opportunity, cheaper than building your own chip. But at some point, we aim to build our own chip. So right now, we develop FPGA solutions, and then at some point, we will be able to have a chip. that’s our timeline and our roadmap.

Yuval: And going back to TSP, how many cities roughly can you solve today and what do you expect to be able to solve in the near future?

Aurélie: that’s a good question. We have a new approach with a digital approach. We’ve shifted to a digital approach at the beginning of the year to ensure better scalability. So right now I think we can solve the TSP for around a hundred cities, Andve as I’ mentioned, we’re working on bigger FPGAs to be able to scale more rapidly.

Yuval: Tell me a little bit about the composition of your team. You said about ten people, I assume they don’t all need to be quantum scientists or maybe even none of them are quantum scientists. How is the team made up?

Aurélie: we have a professor from Polytechnic – it’s a university in Montreal. he is a professor in computing science. We also have a couple of interns with a background in software development, a cloud expert and, a hardware expert with FPGA experience, , software developers and mathematicians. So actually, yeah, no quantum expert per se. We have a consultant, an expert working with NASA for that matter, but the core of the team itself is really more computer scientists.

Yuval: And when you go to customers, do you basically say, “look, you have these problems, classical computers are going to have a hard time solving them. You don’t want to wait five years for quantum computers, here’s a solution that you could deploy in the near future”. Would that roughly be the sales pitch?

Aurélie: Exactly. But as I’ve mentioned, the good thing is we can explain what we can do, listen to our customers’ problems, and then fill the gap. We explain that the solution can solve part of their problem or all the problems, and then we work together to create the solution. that’s the approach we use right now to work on POCs. And also it helps to educate people because they are not very familiar with these combinatorial optimization problems as they know there are problems in the real world, but they don’t usually know what the mathematical model is underneath. So we are there to help them.

Yuval: Are you able to share anything about pricing? How is it per machine? Is it per shot or per thousands of shots? How does pricing work?

Aurélie: right now for the pricing, we sell services. So it’s more project based pricing, we aim to sell computing time too, and at some point, once we develop our own cloud, to sell the licensing to the software. That’s kind of the three approaches we have in mind.

Yuval: Excellent. So how can people get in touch with you to learn more about your work?

Aurélie: please contact me, send me an email over at InfinityQ.Tech or contact me on Linkedin and I will be happy to contact you back and we can discuss with the rest of the team.

Yuval: Excellent. Aurélie, thank you so much for joining me today.

Aurélie: Thank you for the conversation. It was very interesting and I hope you enjoyed the chat.

Yuval Boger is a quantum computing executive. Known as the “Superposition Guy” as well as the original “Qubit Guy,” he most recently served as Chief Marketing Officer for Classiq. He can be reached on LinkedIn or at this email.

October 19, 2022