Disruptor or Disrupted with Jason Silbergleit, Head of Americas at Classiq

(Jason at 00:00:00) Am I going to be a disruptor, or am I going to be disrupted? They spend tons of money on insurance policies that somebody doesn't follow in the office. They're hesitant to spend on quantum. You can have an optimized four-cylinder engine in a subcompact car. You can also port that same engine to an 18-wheel semi. That doesn't mean it works well.

(Joel Beasley at 00:00:26) Jason, where are you calling from today? Not desert words.

(Jason at 00:00:29) Wrong number. Yeah, I'm about twenty minutes east of Poughkeepsie, New York.

(Joel Beasley at 00:00:34) Okay, cool. I'm over in Nashville, Tennessee.

(Jason at 00:00:37) That sounds cooler.

(Joel Beasley at 00:00:41) I don't know. I love America. I've been all over the country, and every place has its pros and cons, you know.

(Jason at 00:00:47) Yeah, no. We're about an hour and a half north of New York City, but we're also in the Hudson Valley, which is, you know, the diversity of big city, and you could be in the country at the same time.

(Joel Beasley at 00:00:57) Oh, my family's in Westchester. So it's probably like an hour north of the city, I think.

(Jason at 00:01:02) Right.

(Joel Beasley at 00:01:02) But I'm just curious. I've got a lot of questions for you, but I figured we'd just start at the beginning and just explain to me, for anyone that has never heard of Classiq, what does the company actually do?

(Jason at 00:01:16) So yeah, Classiq is the leading quantum pure-play software company. So we hear a lot about quantum, and the natural thought is these big machines, hardware. But Classiq is that software layer that enables that hardware to be useful for the end user community.

(Joel Beasley at 00:01:37) All right, how did you get involved with them?

(Jason at 00:01:40) So, as background, I'm an engineer by background. I spent the majority of my career at IBM in various areas, including AI, hybrid cloud, and I also had the opportunity to get very involved in quantum, leading the Quantum Global Sales Team at IBM. As part of that, it became clear to me that in order for quantum to really scale, which I believe is coming and coming sooner than later, you really need that cohesive software layer that unifies all that diversity of hardware and that innovation, and enables clients to take advantage of that at the speed at which quantum is advancing. And Classiq, being the leader in that space, was an obvious choice for an opportunity to have that impact that I was looking for.

(Joel Beasley at 00:02:30) It sounds like the Microsoft play early on. There's a bunch of different hardware. Let's put together a layer that can connect it all.

(Jason at 00:02:37) I think that's actually a really good way to look at it. If you step back, I look at—there's industries that are formed. Right? We're talking about an industry, not a single entity. And if you look at any industry, there's multiple players and multiple winners. But really, to get that scale, if you look at even the diversity of hardware, as you said, with Microsoft, you had all these different computer providers with different architectures and different software, and they were selling in the thousands. It's great, selling in the thousands of computers. But it really wasn't until you had that democratization layer of software that enabled everybody to understand that they could use all that diversity. They didn't have to pick a winner, and they could use that software layer and really take advantage of that diversity of hardware. I think you'll see the same thing in quantum. Right? You have a number of players in quantum across different modalities. You know, some use superconducting qubits, some use ion traps, some use neutral atoms, some use photonics. At the end of the day, as an end user, an enterprise user, you may want to take advantage of all of them. They may all have different advantages. Some may win. Some may not win. Some may come five years from now. But at the end of the day, for your end use, you need to be able to take advantage of that wherever the advances are on the hardware side.

(Joel Beasley at 00:03:57) For people that aren't up to speed, because we have so many things happening so fast, I've just got a couple, like, rapid-fire type questions for you. So are people today using quantum in a commercially viable production? Like, are they extracting actual business value, or is it still an experiment that they're running?

(Jason at 00:04:21) Yeah. So if you're thinking about, are they running commercially viable ROI-based applications on real quantum hardware today? The answer is no. Right? Are they running applications that take advantage of some of the classical compute capabilities as they move towards quantum? Yes. So when you think about quantum computing today, it's at a stage where people understand that that ROI-based advantage is coming, and coming sooner than later. I think some of that's the advancements in fault-tolerant quantum computing, the roadmaps that people see. But if you think about the timescale, and this is one of the reasons that I made the jump and was excited to make the jump to Classiq, is the discussion morphed from an "if" to a "when." When I started in quantum computing, there were amazing advancements I saw firsthand at IBM, and they've done an amazing job. But the question at that point was really, is this going to be a platform that provides value? Right? Still a lot of innovation, a lot of invention required. But that changed. That changed a few years ago when, you know, companies like IBM and other companies released roadmaps toward fault-tolerant using error correction. And it became not an "if," it became a "when." Right? And there's a big difference. That changes the dynamics to, you know, hard engineering. And that's something I think we do really well here in America and elsewhere, where, you know, hard engineering is difficult, but it will be solved. And it's just a matter of time. And the other thing I've seen is I've seen that timeline only accelerate. So then it becomes, okay, if you're going to start seeing those first business-relevant applications in the next small handful of years, you know, what do organizations have to do to make sure they take advantage of that? I sort of step back and I say, you know, a good test case for the community was ChatGPT. Let's use that as an example for a minute. A lot of us didn't see the impact that would have when it happened. It was a surprise to everybody, the amazing capabilities that that brought enterprises, individuals, et cetera. And I almost ask the question to say, okay, think about the disruption that that caused, you know, you as an enterprise, you as a company, you as an individual, and say, if you knew two years in advance that it was coming—it didn't happen overnight and you were reacting—you knew two years in advance that this amazing new capability was going to be available to you, what would you have done? Would you have sat still and waited for it to come and then react and be disrupted? Or would you have organized your company? Would you have put the right people in place? Would you have started experimenting? Would you have had your strategy in place? What would you have done if you knew two years in advance this kind of revolution in AI was happening? It was a test case. So now the test case has a follow-on test. It says, okay, now you know it's coming. It is coming. Whether it's two years, whether it's three years, whether it's two and a half years, who cares? Right? It's coming. What are you going to do as an organization today to prepare yourself for that? And to me, it's really a very easy question to ask. I think, you know, companies, you know, at the board level, at the individual organizational level, need to be thinking about, how am I prepared for this? Am I going to be a disruptor, or am I going to be disrupted? Am I going to have a negative implication because I'm not ready for this, or am I going to take advantage of this and build new business, build new value for my employees, my shareholders, et cetera? So to me, I think the time is right. And in fact, I would almost say, if companies have not yet even thought about quantum, it's time. It may be on the later side.

(Joel Beasley at 00:08:30) So right now, today, we're able to do things with—and I'll talk to you more about Classiq specifically and how organizations can get prepared for it—I'm just having a hard time wrapping my brain around it. So are—today, we are able to do things with quantum computing that cannot be done with classical computing? Is that correct?

(Jason at 00:08:52) No. I would say, when quantum computing is ready, we will be able to do things in quantum computing that classical cannot do today. But we are not at that point yet. As I said, there's a few different inflection points. The first inflection point is what's called quantum advantage. Right? Which is, when can quantum do something better than classical? Right? And we've hit that inflection point. There's been a few announcements of some scientific capabilities that have been shown that show that inflection point. There's always debate around whether it's shown it or not, but let's, for the sake of discussion, let's assume it's been shown. That doesn't mean it's business-relevant. It just means we've shown that scientific inflection point. Right? That's the point at which it's done something. But again, that's different than, okay, now we have a real business problem that we've been able to do better than classical. And that could be, look, we're looking at a complex optimization problem that we can get a percent better accuracy using quantum. That's one. There's other things like simulations, simulating nature, new materials, new therapeutics, new chemistries, et cetera, which, you know, using the power of quantum computing will enable you to get much more accurate discoveries of those types of materials.

(Joel Beasley at 00:10:21) Yeah, it's just interesting to me. So, like, the materials area is the one that I did a deep dive three or four years ago into quantum, and I haven't spent much time there since. But at the time, the coolest thing I remember was the simulation of, like, atoms, essentially. Right? Materials. And then also in drug design as well, like pharmaceutical modeling and design. So I'm just curious—you've got all of these people, an entire industry is moving forward. So obviously, they see it happening. But then when I'm asking a question, like, where is it commercially deployed today? There's no—and then my brain can't understand how—and we can edit anything we want of this conversation—but, like, I can't—it doesn't reconcile in my head. Why is everyone so bullish on it? Why are they spending so much money? Why are massive organizations racing towards this thing, but I can't do anything right now?

(Jason at 00:11:24) Yeah. So I think the way to think about it is, take a therapeutic. How long—I'm going to ask you a question. How long do you think it takes from discovery to actually releasing a therapeutic on the market?

(Joel Beasley at 00:11:40) Seven to ten years.

(Jason at 00:11:41) Exactly. Right? That means companies are making investments in advance of knowing whether there's going to be a monetary outcome. It could fail in Phase 1. It could fail in Phase 2. It can fail in safety. There's all different checkpoints there that can fail. But they recognize the value of a positive outcome is worth the investments that are at risk. So let's take quantum for an example. If you as a therapeutic—and I always found this fascinating, coming from an information technology space where information technology is all about freedom of action and intellectual property. Let everybody go innovate, you know, around technology. In my experience in the pharmaceutical space, where I've worked with companies, collaborating with companies in that space, it was all about the discovery. The one discovery. That one patent on a therapeutic that was discovered is worth twenty years of a patent lifespan and $20 billion. Right? So now take quantum. You've got two companies that are in that industry, whatever industry, whatever therapeutic it is—pain, you know, cardiac medicine, whatever it might be. And you say, okay, whoever finds that discovery is going to own that market. The other party might actually dissolve. Maybe it's existential. They don't have that next drug. And if that discovery is made easier by quantum, do you invest to be disrupting in that space, or do you wait for it fully to be ready while your competitor may be fully prepared? And who's going to get that first discovery? Because that discovery is actually a one or a zero. It's either a huge benefit for your company, or if you're not first, it could be disruption for your company. So to me, it's just part of the investment lifecycle for an industry. And that's what you'll see. You'll see certain industries, based on the problem, be willing to put more at risk, and certain where the benefit isn't as acute, maybe putting less. But it's still going to be beneficial. So take optimization. There are some companies where, you know, one-half a percent of optimization, you know, routing optimization, is a huge financial benefit for that company because they have thousands of variables in their supply chain. And then for some companies, they may not have as complex a supply chain, in which case, maybe that return on investment is smaller. So it really depends on what's the impact of not investing. And those are the discussions you have to have. I mean, many of those companies, you know, they'll be interested in—and it's fascinating technology. I mean, when I think of the innovation that these companies have made on quantum in general—superconducting qubits, thinking about ion traps or neutral atoms where you're manipulating single electrons to create ones and zeros—it's just mind-boggling the technology that goes in there. And, you know, I think companies that are in various businesses that will use quantum find that fascinating as well, but that doesn't move the needle for them. What moves the needle for them is, it really doesn't matter what the modality of compute is. What matters is, what does it do for my business? And does it provide an outcome that gives me value? And I think that's where, you know, coming back, that's what intrigued me on the software side, because we abstract out some of that complexity, that technology complexity that people sometimes get very focused on. Take that up a level. We'll handle that abstraction, that complexity. You focus on what you're good at. Focus on the business problem, and we'll help translate that in a much more automated way across that portfolio of technology diversity to ensure that you are in a position where you can take advantage of that for business value.

(Joel Beasley at 00:15:42) Okay. So you guys are sitting there and you're at the level of, it's happening. We don't have to convince people to do this. It's happening. This is occurring, and people are grabbing, trying to piecemeal all this stuff together, and there's an optimization area where we could say, hey, if we build this layer and make it easier for them to build the widgets and run the experiments that they're trying to run for whatever reason they're trying to run them, then now we have a profitable business model that's bringing value to them and allowing them to get up to speed faster.

(Jason at 00:16:14) Exactly.

(Joel Beasley at 00:16:14) Okay, got it. All right, I get it. So your business is good. They know what they're doing inside of their niches, and they're exploring it for their incentives.

(Jason at 00:16:22) And by the way, we need them to be successful. The software will only be successful if you have the hardware getting to that point where you provide business value. So we are highly motivated and highly interested in the success across the whole ecosystem. But if you just have success on the hardware but not the software layer, you don't have success. If you have success just on the software layer, not the hardware layer, you don't have success.

(Jason at 00:16:44) That's why this is a whole symbiotic ecosystem. But, you know, there's been so much focus on the hardware side that we're now at that inflection point that if we don't focus on the software, we'll have great hardware. We don't have the scalability to provide business value. So I think the time is right to now do the investments and build out that scalable enterprise-grade software layer that gives those industries confidence that they can take advantage of quantum computing.

(Joel Beasley at 00:17:09) So if you had a megaphone and you were talking to the marketplace at large, like everyone from credit card payment processors to science material manufacturers, like everyone, where are the areas that you would say, "Hey, guys, I'm tapping you on the shoulder. You might not know this, but other people in your space are pushing towards this. You should take a look at this."

(Jason at 00:17:14) Mhmm.

(Joel Beasley at 00:17:36) This will sound a little cliche, but I think it's everywhere. I think it's that pervasive. You know, name a company that doesn't have complex supply chains. I mean, primarily today, it's probably focused a little bit more on the bigger, larger companies that are gonna get the most benefit because they've got such complex and so much value associated with those optimizations. But, you know, as it gets more mature, it'll further work with basically every enterprise will take advantage of it.

(Jason at 00:18:08) But I think across industries, it's really an industry agnostic, even though there's verticals that are very specifically targeted for certain algorithms. You know, if you have supply chain, if you have optimizations, if you have routings, if you have discovery, if you are simulating nature, these are all the kinds of things that if you're doing that in your business, it will be, it could be impacted by quantum. And I'll just say I have had a hard time finding companies that I cannot find a use case that would resonate with quantum. The only difference is how big is the problem they have and, you know, how quickly should they move on it because of the value it can provide. It's not about whether or not there's an opportunity for them to engage.

(Joel Beasley at 00:19:05) Similar to, back to your AI reference earlier, your large language model, you can apply it to like any business, but how people choose to apply it. Okay.

(Jason at 00:19:14) Well, think about this. I remember, you know, you go back to classical computing in the early days of classical computing. I think they came out and said, "Hey, this is great stuff. This is amazing. We got these computers. We're gonna sell dozens of them. These computer things." Right? And we have like 10 applications. It's amazing. We got 10 applications we can work on this computer. You put those computers in the hands of the public, you have millions of applications. You know, once you give people a tool, they figure out how to use it. That's where quantum is. I mean, we have applications. We have algorithms, but we're just at the tip of the iceberg. Once you get this in the hands of these enterprises and communities, the number of applications, the value proposition, it's just gonna explode.

(Joel Beasley at 00:19:57) It's just how humans work. Anyone who has a kid and who has ever handed them an iPad knows you don't teach them. You're like, "Here's the glowing screen," and then they come back to you and show you stuff you didn't know it could do.

(Jason at 00:20:08) Exactly.

(Joel Beasley at 00:20:09) Yeah. Okay. So this is interesting because before this conversation, I'd say my last dive into the quantum world was mostly a qubit race, if I believe that's correct. They were trying to, "I've got this many qubits. I got this many," and they were all racing after that. But now it seems like it's advanced and there's new levels unlocked here. Is that correct?

(Jason at 00:20:33) Yeah. I mean, I think people have continued to learn, you know, what is the state of the art that's needed to continue to advance these computers. I think qubits are still important, but it's really about the number of usable qubits that can scale for those applications. I mean, you know, you can make a lot of qubits, but are they quality qubits? Are they corrected qubits? And so a lot of the focus now is ensuring that there's enough usable qubits for the scaling of those applications.

(Joel Beasley at 00:21:05) But there are a lot of things that you can do with a quantum computer that you can also do, right? There are some, is that correct?

(Jason at 00:21:15) Yeah. But I would almost say, you know, I view quantum computing as really a new modality of compute. It's not gonna replace classical computing. There's gonna be reasons for classical computing for, well, as far as I can see. Right? I mean, you're gonna do big data transaction processing. That's gonna be on classical computing. There's no real advantage to doing that on quantum computing. So you're really opening up new spaces of value creation. So I don't even view this as a which one will win. I think it's a how can quantum plus classical open up new opportunities for value creation for enterprises, new job creation, new industries can be created because you're opening up spaces that just were not even feasible in the classical era.

(Joel Beasley at 00:22:10) Yeah. I just remember a few years ago, I talked with Bob Sutor. I think you know him. But they had some sandbox where you could do some very basic stuff, but at least you were running. And it was actually running on quantum, so you could put your hands on it as a little quantum playground.

(Jason at 00:22:27) Yeah. Yeah. So we have, I mean, so we just released in beta a benchmarking tool in Classiq. And it really does the same thing at a probably a much more advanced level now where you can run an algorithm across whether it's simulated on GPUs or running across various modalities of actual hardware and actually benchmark, you know, across a specific algorithm, the comparative nature of the various hardware modalities.

(Joel Beasley at 00:23:01) That's interesting. So there is some use going on for it today then.

(Jason at 00:23:05) Absolutely. I mean, it's only increasing. I've been incredibly, even since coming over to Classiq, the adoption of quantum, the adoption of our platform, the ability to find value out of coming into a, I'll call it a lower friction environment to start with has been incredible. I've been very pleased with the engagements we've had.

(Joel Beasley at 00:23:34) What's the coolest thing that you have ever seen quantum do?

(Jason at 00:23:40) I guess I would just say, as I said before, when you say what has quantum done, again, because it's not at the point of providing capability that's better than classical in a business relevant mode, but intends to be. I think I would just switch the question around to what's the coolest thing that you are expecting quantum to do? Can I change it around for you?

(Joel Beasley at 00:24:10) But quantum can do something, though. Like, it can do something.

(Jason at 00:24:14) It can do stuff. It could experiment with things. It can do also, it can do. But in terms of things that it can that has provided better than classical, to me, the ability to actually simulate molecules is amazing. It's amazing. I mean, just I look at, you know, I spend a lot of time at the intersection of information technology and health care life sciences, pharma. And, you know, there's so much need for discovery, for the benefit of society on a therapeutic side. There's rare diseases. There's things that people are suffering and, you know, the ability to actually address those because the expense, the ability, how much time you need is just not there. I think quantum is going to open up and democratize the ability to go after, you know, really important things, whether it's to a small number of people or a large number of people. I think that's gonna be really, really transformational. It's gonna open up a lot of hope for people that in the past probably didn't have hope for, you know, what they were interested in having discovered.

(Joel Beasley at 00:25:22) It can do some math. Right? Like, that's the initial, the original. I feel like we're, it can't?

(Jason at 00:25:28) Yeah. I mean, so these are all the algorithms. It's no different than that. And, again, I'm differentiating between what it can do now from an advantageous perspective versus what it can do. Can it show capability? Yes. Is that capability as good as classical? Not yet. Is it coming to be as good as classical? Very, very quickly. Is it going to be better than classical? Yes. And it's in certain areas like we talked about optimizations and discovery, and it's coming soon. That's the key message is, you know, where it was two years ago, we're light years ahead of where it was two years ago, and it's coming very, very quickly. If you look at the road maps of many of the people in the space, it shows fault tolerant, error corrected, you know, fault tolerant quantum computers by the end of this decade, 2028, 2029. It's already showing that technical inflection point I talked about of an advantage in 2026. So between 2026 and 2028, 2029, you're gonna start seeing, you know, multiple business relevant advantageous use cases.

(Joel Beasley at 00:26:40) Okay. So this is, let's use your original analogy of the LLMs. We just, we had a breakthrough with transformers.

(Jason at 00:26:48) Exactly.

(Joel Beasley at 00:26:48) But if we ask you to generate a poem five times out of 17, it runs off and just starts spitting out gibberish and a hallucination. But we're like, "It's the future, man." And people are like, "Yeah. It can't even..."

(Jason at 00:27:01) That's a great analogy.

(Joel Beasley at 00:27:03) Okay.

(Jason at 00:27:03) So take for example, when LLMs first came out, everyone's talking about, "Oh, look. I ran this LLM," just like you said. And it came out with all these hallucinations. It was really bad. It was interesting. It maybe said something or something, but it really didn't do anything. And if I look at where we are today, it's incredibly, incredible. It's incredible with code and with, you know, language, et cetera. And so if you think about that, that's the journey you're gonna be on quantum. It's gonna do some things in the next small handful of years, and then it's gonna rapidly continue to advance just like we've started to see. I don't know if it's exactly on the same time scale of advancement, you know, whether it happens every, you know, a new model released every ten seconds or it's gonna take a little bit longer, but that's the analogy that people should have in their head.

(Joel Beasley at 00:27:51) I think it's gonna have an outsized impact on molecules, like our physical world. Like, our ability to create hyper personalized drugs, edit our DNA, to remove things that will hurt us. I think it's going to improve our life to a degree in which it's hard to imagine. It almost would seem like a weird fantasy. But at the same time, if I brought you back 10 years ago and I told you about where Gemini would be at today or Grok would be at today, you would say that's insane. That's not even...

(Jason at 00:28:22) I totally agree. And I think it's beyond even therapeutics. So think about hyper personalized materials. I need a material because I wanna do, I wanna have renewable energy capabilities or fusion technologies or all sorts of things that are transformational for environment, for therapeutics. A lot of these are materials based problems. Right?

(Joel Beasley at 00:28:47) If we can have an LLM for materials that could simulate it in a near real world, then we'll have advancements overnight. Like, we'll have new gravity related advancements.

(Jason at 00:29:02) That's why it's important. And so, you know, I want the audience should understand the reality of where quantum is today, but also understand how fast that reality is changing. Right? That's the difference. It's no different, and I spent a lot of time in AI before the LLMs really before the ChatGPT moment. The reality of AI pre-ChatGPT was very different than the reality of AI after that. The reality of quantum today, that's why I use that analogy early on. The reality of quantum today is gonna be very different than the reality of quantum in two years. And if organizations who were disrupted, many were disrupted as we all see. If they are not planning for the future reality of quantum, they're gonna get left behind. And, you know, one way I always think about it, a different way to think about it is all the companies, they take out insurance policies. I was just thinking about this the other night. They take out insurance. They spend tons of money on insurance policies that somebody doesn't fall in the office. Right? Yet they're hesitant to spend on quantum. And quantum could be the biggest disruptor of value for those companies in the next few years. The worst case scenario is you get involved in quantum, understand the industry, start spending a little bit and making sure you're prepared, you understand it, you see what's disrupting, so that you're hedging and you're ready when it comes out. You could spend a lot, that's great. You spend a little, but if you're spending nothing, but you're spending to see if, you know, God forbid somebody falls, I think that the priorities, you gotta think of it in different ways as well. Because this could be that impactful to these companies.

(Joel Beasley at 00:30:50) Ask your LLM about it. Be like, "Find the way that quantum could impact my industry," you know? Or you call it classical. Can they reach out to you guys? Do you guys do that at all? Do you do consulting or do you just build the OS layer?

(Jason at 00:31:06) No. No. We do. So let me, if I take a minute just to explain, beside it's more than an OS. Right? Obviously, it does orchestration and operates. But the way the software operates, and I think this is another way to think about how to start that journey in a risk mitigated low friction way. Because I think to date, it's been a higher risk, higher friction way to engage because you're engaging at, you know, a much larger level. So I almost look at it as, you know, today, there's many different players that are all at the hardware level that are reaching out to customers saying, "Hey, work with me. I've got the best hardware. I'm gonna get you there. Spend a lot of money and we'll get you started." And they'll do that across different modalities, and all the modalities will say they're the best. And there's some amazing innovations across. But, you know, I think many of the enterprise companies, you know, when everybody's saying that they're the best or they've got the right road map, et cetera, to some extent, it leaves the end user confused on, well, if I'm gonna spend a lot of money, I need to make the right choice. If I don't make the right choice, I've actually wasted my money. So I'm just gonna wait. I'm gonna wait till it's clear, and I'm gonna keep waiting and waiting and waiting. And the way you can think about Classiq, it's a hardware agnostic hardware aware platform.

(Jason at 00:32:26) And let me just explain the difference. So hardware agnostic means you can use it across all those modalities. Hardware aware means what we do is when we take an algorithm, it's actually abstracted, so it's an abstraction layer. So it makes it a simpler way to engage in typical language like Python, etcetera.

(Jason at 00:32:46) And we have the largest library of code and curated applications and algorithms. And you could use it on simulators, but then you could actually, if you decide, okay, tomorrow I want to go run it on this superconducting platform, it compiles it to that platform aware of the architecture. And then if you say, hey, listen, I want to now compile it on an ion trap platform or neutral atom, it will then take that same algorithm and recompile it and optimize it to that architecture.

(Jason at 00:33:16) So the end circuits will look different because they're optimized versus if you're just doing an agnostic layer. I kind of use the analogy: you can have an optimized four cylinder engine in a subcompact car. You can also port that same engine to an 18 wheel semi. That doesn't mean it works well, right?

(Joel Beasley at 00:33:35) So the

(Jason at 00:33:35) The ability to have it hardware aware and optimized really takes the decision point out of the end users so that they can say, listen, I know I want to start that journey. I want to focus on my business problems. The Classiq software stack allows me in an automated fashion to start that journey wherever I want to start it. I want to start on GPUs to simulate it? Great. Oh, tomorrow I want to start it on this platform of choice in quantum? Fine. I can go move it there. Oh, five years from now there's a whole new platform none of us ever thought would exist? I can port it there. So you're never actually losing that journey. You're actually investing in the industry and in that journey, not in an individual player. And that's where I think we can kind of break down those fears of starting and ensure that they can start that journey, be confident that that journey doesn't end because one player goes in a different direction, falls apart, whatever it might be, and allows them to really be a part of the industry.

(Jason at 00:34:30) Because, again, as I go back to part of the premise of my excitement for Classiq was, I'm convinced that the quantum industry is here and it's coming fast. And so how do you invest both as an end user, but also as an employee? How do you invest in the industry? And the software allows me to invest in the industry, not a single modality or hardware player.

(Joel Beasley at 00:34:54) That's really interesting. That does derisk it a lot getting involved. And you said you don't have to commit to buying the physical quantum machines. You can run it as simulations?

(Jason at 00:35:05) Yeah. We can run simulations. I mean, the amount of capability on the quantum hardware is increasing every day. And our platform really allows people to use that hardware as well in a low friction environment because they don't have to go to each of these players. We can manage that. So it makes it a much easier way for them to either use existing resources they might have in classical compute to simulate, and then some have existing capabilities on quantum. We help them get to whatever capabilities they want on quantum, make it a single platform to engage. You know, think of it as how you engage classical computing today. You're not really focused on all the underlying complexities. You're just working with that software to get your applications running.

(Joel Beasley at 00:35:51) So yeah. I mean, quantum can do a lot. Do you guys have the largest quantum algorithm library on GitHub? They can do all of these different things.

(Joel Beasley at 00:36:02) Okay.

(Jason at 00:36:03) And as you asked before, given the fact that some entities and some enterprises are pretty advanced in quantum, they may decide, you know, they want to understand our platform, use the platform, and here, go use the platform. You can do all that yourself. You've got all the libraries, you got all the capabilities, all the tools, etcetera. Go enjoy. We'll be thrilled to do that. We'll give you some training on how to use the platform and you're off to the races. Others may say, listen, I don't have a lot of depth in quantum. Can you hold my hand and show me how to do it? Get me educated so that, you know, teach the teacher. I'm going to teach you how to go do this. I'm going to do the first use cases, the second use cases, then you're able to do it. We can do that as well. And we do that quite often with our customers.

(Joel Beasley at 00:36:51) Yeah. You guys have tons of examples in here. Finance, physical systems, cybersecurity, optimization, logistics, chemistry. So okay. Earlier, I was getting lost. I was like, what can it do today? But I think you're so used to having the business ROI positive conversation. Like, well, we're not fully, but it can do a lot. It can do a lot.

(Jason at 00:37:09) That is the hole we sometimes get in because a lot of companies are so, as they should be, ROI focused, the discussion about ROI. We have to always be careful to ensure that they understand the value they're going to get out of it. I mean, any of these relationships are based on trust and they are trusting of when they're going to see that business value.

(Joel Beasley at 00:37:35) Okay. Yeah. No. That makes complete sense. Alright. I feel a lot better.

(Jason at 00:37:39) Now, now I will tell you, the other thing that I think is really incredible about the platform is we've also recently released AI agents at the front end. So you can imagine this platform has got a tremendous capability. I mean, our compiler is like a thousand times faster than anything else out there. We do large circuits. A lot of people are working on smaller circuits when you think about scaling, all that stuff. But we then have these agents that are context aware and understand all the documentation, all of that language that we use, all that capability, but then create agents on the front end. So think about quantum yesterday. You know, companies were figuring out if they wanted to hire a ton of quantum physicists, which, there's always going to be that need. But if you're starting, do you need quantum physicists to start? Now what we've created these agents at the front end that essentially enables your data scientists and your business users to actually have quantum assistance to be able to enable them to engage these systems, to make it very, very easy and user friendly to take advantage of these capabilities for that business layer, which is a big change and a big improvement in terms of democratization and scaling of quantum.

(Joel Beasley at 00:38:55) One of the things that's running through my mind right now is the ChatGPT story. So I talked to Zach who is the head of marketing when they grew from like 2 million to 2 billion. And he had told me he said they were having a rough time selling this because they were selling it to engineers and the government and all these different people for these very specific use cases before. And then they said, well, as sort of like a superlative or as a way to get this out there, we're going to create a model that's specifically tuned to conversation and then put it out there just as a way for people to get to experience and interact with it. And that's what made it the fastest adopted product in history. And so for me, I thought it was really interesting that they're sitting there with this amazing technology. They can all see the future. They can all see the potential, but they're still having trouble selling this one adaptation for like, hey, let's just get it into the hands of people, change the entire game, and then every other player in the industry had to jump on. So I'm wondering, what is that thing going to be for the quantum world?

(Jason at 00:40:05) I think that's a good question. I think this is less going to be about, you know, something that, I believe you're not going to see that strong consumer necessarily use case that people are doing that in their house versus, like, with ChatGPT, it was amazing. You were just creating poems and putting in the style of, you know, this person's voice and all sorts of things. It became an amazing tool that got that adoption so quickly. I think it's going to be, showing that once we show that advantage, business advantage in optimization or in discovery, I think the adoption of the enterprise is going to be a hockey stick adoption. It's going to be, I think it'll be

(Joel Beasley at 00:40:46) A show. Yeah. I think it's going to be a new material or something. I think it's going to be like a scientific breakthrough.

(Jason at 00:40:54) I think that would definitely be the headline that grabs the most attention.

(Joel Beasley at 00:41:02) Yeah, right? Because it's like, oh, we can have wings now as humans. What? Why? Quantum computing figured this out. I think that'd be cool or some new type of material that allows us to have like gravity boots or something. I don't know.

(Jason at 00:41:17) Yeah. That could be. I'd like it to be one of these amazing discoveries on a therapeutic, something that, you know, not only shows that innovation, but it fundamentally changes people's lives and hopes because that's when you start getting a lot of people focused on things when they see they can really make an impact.

(Joel Beasley at 00:41:44) I think that just happened. It's the GLP-1s.

(Jason at 00:41:47) That's addictive.

(Joel Beasley at 00:41:50) Oh, no. Maybe maybe

(Jason at 00:41:51) Maybe you need to find GLP-1s other than the couple that you're stabbing yourself with a, you know?

(Joel Beasley at 00:41:57) Not doing it. Not doing it. That's right. Let's get one that's just like a pill. Hey, you've said a couple of things. You've said this a couple of times and I made a note of it and I want to circle back to it. When you're talking about the progress, you kept mentioning this milestone of fault tolerant.

(Jason at 00:42:13) Yeah.

(Joel Beasley at 00:42:14) What is that milestone? What does it mean? Like explain it to a kindergartner because I'm not that smart. But like, where is it? How close are we? What does it mean when we achieve it?

(Jason at 00:42:25) So I think I mentioned earlier, today, you know, people are working mostly in these things called logical qubits. What really needs to happen is how many of these physical, these actual material based qubits do you need that are running correctly? Right? I said that there's a lot of errors in these physical qubits. Right? These qubits. So you need to get to the point where those qubits are operating, and they're quality qubits, right?

(Jason at 00:42:56) And so there's been a lot of work on software that error corrects those qubits and enables you to essentially have the number of logical qubits you need, and you need to be able to correct those qubits that you've built. And so when you think about fault tolerant, it means you've gotten to the point where essentially you don't have the errors that you have existing in today's computers. And there's a lot of work being done on the software side to bridge that gap between today's quantum computers and fault tolerant. And that's the road map that you're hearing about, which is when are we going to be at that point? And that's where you're seeing a lot of these roadmaps converge in that 2028, 2029 period of time. And there's a lot of work. And like I said, our platform as well is able to do predictive prediction of the number of physical qubits you're going to need, which is good because you map that to application. You can map that to a hardware roadmap and, you know, works on things like how to route those logical operations, what's the fidelity, what's the cycle time, all the kind of things that are needed to essentially take our abstraction platform and use the automation in the fault tolerant era. So, you know, we're preparing even our platform to assume capability to do all that abstraction, all that compilation, all that automation in the fault tolerant era, which as soon as it happens, you're dealing with large circuits. And without that automation, it's just going to be incredibly difficult, impossible to manually map those qubits.

(Joel Beasley at 00:44:39) For the leaders that are listening and they're like, I'm into this. I want to know what's the first concrete step I can take. What would that be?

(Jason at 00:44:48) I would say go to www.classiq.io and make an inquiry because we would be glad to respond incredibly quickly and have that conversation. I would just tell you, you know, I think folks think, and I've seen it could take a tremendous amount of time, an outsized investment to get started, and I would just say, let's have a conversation. I think, like I said earlier, there is low friction, rapid ways to get involved, and we'd be happy to have that conversation. And if it results in an engagement, fantastic. If it doesn't and it results in more knowledge for you on when you need to be involved, I'm happy.

(Joel Beasley at 00:45:36) Nice. You guys have been doing pretty well. You're growing. You've raised some money. Any public talking points you want to get out there?

(Jason at 00:45:46) Look. I think, as I mentioned earlier, I think there's been a lot of talk. There's a tremendous amount of public information out there on all the hardware, the investments that people are making in hardware, the types of modalities, the innovations. I think the time is right to have that discussion about software and how software can really be the bridge to scaling and adopting quantum for these enterprises. I think the most important thing is quantum is, it's not an if. It's a when. The when is compressing. It will be disruptive. And there's nothing that prevents any company from engaging now. And, you know, through a software layer and a low friction environment, I don't see any gates to starting that journey.

(Joel Beasley at 00:46:43) Excellent. And the website is, could you tell me one more time?

(Jason at 00:46:47) Sure. www.classiq.io.

(Joel Beasley at 00:46:54) Perfect. Now you as a leader, I've got some leadership questions because

(Jason at 00:46:58) Sure.

(Joel Beasley at 00:46:59) While our audience, they want to know about the new technology and all that, they're also leading teams, managing people, project through disruption, all of that. One of my favorite things to ask people I speak with is for a piece of leadership advice that you've gotten from someone else that you then implemented it within your life, and it stuck with you for a long time.

(Jason at 00:47:22) Listen. You know, I have an engineering background, and engineers by nature like to solve problems, right? And so by liking to solve problems, we also like to talk and be and show that we can solve problems. And sometimes the best thing to do is just listen, not talk. And I found that that's important. And, you know, when I talk to clients, you know, I can sit there and talk about quantum till I'm blue in the face. But it doesn't matter. What really matters is what is the client thinking? What are they thinking? What's on their mind? What are their problems? You know, is there a problem that they can't find resources? How do I help them because they can't find the right resources? Is the problem that, you know, they don't know how to explain quantum computing to the leadership because the leadership is so focused on another priority? Let me help you with that. Is the problem that they've invested in the past and it's had problems? So how do I risk mitigate that? So I think just actually listening to what people need, I am convinced once you know what somebody needs, that's when the fun starts. I mean, no different than engineering.

(Jason at 00:48:42) Creativity and how you engage with people is something that I've really admired from leaders that I've worked from in the past and ones that I've really taken forward.

(Joel Beasley at 00:48:53) And then what's the most common mistake that you see technology leaders making when they're evaluating any emerging technologies, not just this, but in general?

(Jason at 00:49:04) Human nature. When you have a theory, you want to prove that it's right. And I think people get stuck in their own idea, and sometimes you have to make sure you step back and truly evaluate whether that approach is the best approach. Is it another approach? You know, being a little bit open and flexible to the data that you're seeing.

(Joel Beasley at 00:49:33) Nice. Well, we made a podcast, Jason. How do you feel?

(Jason at 00:49:37) I feel pretty good. The only part I don't feel good about, I feel like I didn't convince you that quantum was valuable. I was being very, I was trying to be a little bit conservative so your users don't think I came on here to just tell them quantum is here now because that's a dangerous thing as well. So I had to try to balance it, but I don't know if I balanced it really well for you though.

(Joel Beasley at 00:49:56) No, I think we got it. I think we should leave this part of the conversation in because, yeah, when I went into it, you know, I'm coming in from the outside and you're in it all day. So one of my jobs as an interviewer is, okay, if I'm trying to get an answer pinned down and it's not getting pinned down, there's another reason why it's not.

(Joel Beasley at 00:50:18) And that's because, you know, maybe they're in their world. And so the words that we're using might be different or whatever it is. So I was just trying to get, like, can quantum do anything? Like, can it do?

(Jason at 00:50:32) It can. Like, yeah, it can do a lot of stuff, and you've seen that. I think you'll find you will find hype cycles in technology. And I think many of your listeners might have, in the past, I would say, let's say five years, heard quantum's coming, quantum's, oh, you should be. And they may have gotten burnt that it wasn't yet as far along as it is. I'm trying to manage, you know, maybe some prior concerns or experiences they've had to where we are today and manage that because, you know, in many technologies, hype cycles, they go up and they go down.

(Jason at 00:51:15) And I think I'm convinced again. Like I said, I made the move because it's not an if, it's a when. It's coming. It is coming. Yeah.

(Jason at 00:51:25) You know, I—

(Joel Beasley at 00:51:25) You've seen the transformer.

(Jason at 00:51:28) Like, I mean, you know, moving from a storied company to a highly regarded software startup is something you do when you see that transformation coming.

(Joel Beasley at 00:51:43) Honestly, I've been around for a while now interviewing all these people that are at the top of their field like you. And every time that happens, I pay attention to that startup. I'm like, there's no way you're working at the top of your game for twenty, thirty years and then you go join this thing. You know, last time I saw it—

(Jason at 00:52:00) Yeah.

(Joel Beasley at 00:52:00) Oh, what was it? It was when they were storing data inside of DNA. Oh, if you haven't looked into that one.

(Jason at 00:52:07) Oh, no, no, we, yeah, it's amazing. Yeah.

(Jason at 00:52:09) I mean, pay pay attention—

(Joel Beasley at 00:52:10) Classic area where quantum might be useful, you know?

(Jason at 00:52:12) Yeah. Yeah. No, like I said, for your users, for yourself, pay attention to Classiq. It will be a name that people hear for a long time.

(Joel Beasley at 00:52:20) That's right. I'm excited about it. It's just so cool. I want those wings, Jason.

(Jason at 00:52:26) Listen, listen, as part of this podcast, I'll discover wings on a quantum computer for you. How's that sound?

(Jason at 00:52:35) You want that better than the gravity boots or, sort of, you know, Star Trek-like transportation?

(Joel Beasley at 00:52:41) Oh, if I get the transportation, because I'm going to just destroy the transportation industry.

(Jason at 00:52:46) I like that too. I'm sort of towards that. And there's also cloning. I mean, who knows? You know, we could look at all those examples.

(Joel Beasley at 00:52:53) I don't even need to teleport a human. If I can just teleport basic material, I'm in. That's, uh, I'm selling it to Bezos.

(Jason at 00:53:02) There we go. We'll go work on that. And the next time you have a podcast, I'll bring the wings and the gravity boots.

(Joel Beasley at 00:53:10) Yes. Okay. That sounds like a plan. Jason, this is fun, man. This is great.

(Joel Beasley at 00:53:14) Thank you for bringing this knowledge. I love the idea of quantum. I've followed it from my personal life to the actual technological happenings every couple years. I love it in sci-fi. It's got this massive potential to change our lives in such a big way that it's just very exciting.

(Jason at 00:53:34) Stay tuned. I would even say, you know, as you look at future podcasts, you'll have the chance to look back and say, okay, what was said a year ago? What was said now? What was said a year from now? And compare and see the progress because it's going to blow your mind.

(Joel Beasley at 00:53:52) Oh my gosh. You know what? This is very similar. I saw somebody came to me in, like, 2012 with this API that you could put in a page, and it could tell you something about the page that was not on the page exactly. And it was this very crude large language model type of situation, this learning.

(Joel Beasley at 00:54:14) And I thought, wow, it's going to revolutionize search. It's going to be crazy. And then you fast forward, you know, it wasn't until they had the additional breakthroughs that it became even more usable and could do even more things. And now it's like, how many times a day do I talk to the LLM? It's ridiculous.

(Jason at 00:54:31) You know, it's used, it's just part of everybody's daily life now.

(Joel Beasley at 00:54:36) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you would like to hear discussed on the podcast, either add me on LinkedIn or send me an email, [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.