IEEE Quantum Podcast Series: Episode 14


podcast14 LiscouskiA Conversation with Robert Liscouski

President, CEO and Chairman, Quantum Computing (QCI)




Podcast 14 WMcGannand 

William McGann, CTO, Quantum Computing (QCI)

Listen to Episode 14 (MP3, 33MB)



Part of the IEEE Quantum Podcast Series


Episode Transcript:


Brian Walker: Welcome to the IEEE Quantum Podcast series an IEEE Future Directions Digital Studio Production. This podcast series informs on the landscape of the quantum ecosystem and highlights projects and activities on quantum technologies. This episode features Robert Liscouski, President, CEO and Chairman Quantum Computing (QCI), and William McGann, the company's chief operating and technology officer. They share insights on the current state of the quantum landscape and the promise of quantum as the technology advances. Gentlemen, thank you for taking time to contribute to the IEEE Quantum Podcast series. To get started, can you please introduce yourselves and provide a little info on your positions?

Bill McGann: My name is Bill McGann. I'm the Chief Technology Officer and the Chief Operations Officer for Quantum Computing Inc. And the role I serve is kind of self-explanatory in that vein, but really focused on doing what the business needs to do from an operations and infrastructure perspective with a tremendous focus on technology and innovation. As a new entrant, really with some very exciting new capabilities in the quantum industry.

Bob Liscouski: And Brian, I'm Bob Liscouski, I'm the CEO of the company, one of the co-founders. And, you know, I'm just trying to facilitate the work of the great work that Bill and the team are doing here. So, my job is pretty easy. I just have to give them plenty of money to keep working.

Brian Walker: So, what's your high-level view on the current state of the quantum ecosystem?

Bob Liscouski: Yeah. Maybe I can start off from a business perspective and Bill can certainly weigh in on that on the technology side. So, you know, since we started doing this a little bit over four years ago, you know, the market then was very, very early stage. And its slightly matured today. More entrants in the marketplace, more hardware vendors, more software vendors. But I think there's been, I don't want to call it hype necessarily, although there is some of that. But I think there was this overbuilt expectation of when quantum computing was going to really provide some sort of material value. Now, people talk about quantum supremacy or quantum advantage or what have you, but in reality, I think they're looking to try to eke out some benefit out of quantum computing and understand what that means. Clearly, in the business sense, and I think so far, the market has not been able to realize that to any great degree, although there's been significant investment yet to try to get to that point. I think, you know, there's still this opportunity here for, you know, companies to really demonstrate that quantum computing is going to provide some near-term benefit. Certainly, it will in a few years. And, you know, we can talk about where we play in that space, and we will. But I think the markets still characterized by this level of anticipation in terms of what quantum computing will bring. And therefore, most entrants in the space today or most of the folks who are dabbling in quantum computing at the client level, are doing it from a curiosity standpoint, not one from which they think they're getting any kind of business value yet.

Brian Walker:  Bill, you have anything to add?

Bill McGann: Yeah, I mean to sort of punctuate a couple of things, in Bob’s, overview there. I think you know, the industry in total is sort of no longer embryonic, but it's still kind of in a nascent stage, right, where there are a lot of different implementations of hardware architectures, all of which have some promise, none of which are demonstrating enough scale scalability yet to be truly practical, to have for commercial endeavor, let's say. So, they're the people still very heads down on their own technology, very focused on demonstrating they can build a certain kind of a gate that has a certain type of, you know, decoherence, you know, characteristic or they have D-Wave has their Anila where they're trying to continually improve the performance of these of these architectures. And as of yet. You know, the industry hasn't really come into a place where you can solve real world customer problems to provide cost benefits, for example, to a supply chain problem for a financial institution or a banking institution or even, you know, optimization problems that relate to chemistry, for example. So all these things are possible and, and you know, QCI, Bob is probably going to go back to this, and maybe will play back and forth a little bit here. I mean QCI was kind of founded somewhat on the basis of we can have really good software technology capabilities to help bridge the gap between current levels of hardware performance and where the industry wants solution performance to be by using powerful machine learning algorithms, for example, to expand on the capabilities of the current hardware. And then, of course, as you know, more recently, we ourselves have become a full stack provider with our own hardware, which we're really excited about. So, the industry's changing like that in that direction where people are more and more Full-stack recognizing that we can't just have software and you can't just have hardware, you have to put it together in a meaningful way.

Bob Liscouski: And if I can just build on that a little bit, Brian, because, you know, Bill did mention, I think, a very important point when we when we started the company, you know, we started off as a software platform with the intent of bringing quantum computing to end users without having to have the burden or the necessarily the resources, the quantum computing and quantum programing resources to allow a quantum computer to work. Right. So, it's not as we know now, and I think your audience is probably well aware you just can't program a quantum computer as you can in normal, you know, a classical computer and get things to work. You know, there's none of that sort of architecture of the software architecture to allow that to happen easily. So, you know, the IBMs of the world have their toolkits and all the all the vendors have some form of a toolkit that require some significant levels of programing to be able to get them to work. And then you need to have the problem formulation and tuning the problem to the computer, etc. And you kind of go through this iterative process that's pretty high cost. So QCI saw the opportunity of creating a platform that would actually disintermediate the need for those high-level resources and allow an end user to actually formulate their problem to run on a QPU. You know, and we do that through bracket. We can run on any number of QPUs, and you know, we've had success in doing that. The challenge is, as we both alluded to here, is that the QPUs themselves are not really providing the benefit based upon their computational capability that the users get any real benefit from. But they can dabble in it, and they can play with it, and they can show that there some promise for quantum computing downstream. So, when we started going off in this direction, we were hoping the industry would actually accelerate a little bit further, faster than it did. But we've had a modicum of success with this, nonetheless. So, for us, we think we're still pointing in the right direction. But as Bill talked just mentioned, with this recent acquisition we did, it kind of changes the game for us.

Brian Walker: So QCI recently announced a software update. How has that helped advance the quantum space?

Bill McGann: Yeah, I could take that one to start with Bob. So, let's talk in real terms by example, right? So late last year, early this year, there was a publicly announced problem by BMW to put a sensor optimization problem out there for people to demonstrate capability, right, and they didn't put constraints on it had to be a quantum computer or classical. They were just looking for technology solutions to solve, not a huge problem, but a real-world problem in terms of scale of meeting the equivalent of many thousands of qubits. Right. Which is probably a factor of 20 greater than any of the current hardware qubit counts we talk about today. So these things were operating in the regime of, you know, three, four or 5000, you know, independent variables to solve and come up with an optimum solution. And QCI using our software algorithmic approach in a combination with a D-Wave hardware set were actually able to provide a solution to that problem. And so that was a pretty good sort of proof point to us that we had something algorithmically that could take current set of hardware, namely in this case, a D-Wave Anila, which on a problem with this kind of mapping and density would not be able to probably consume more than about 150, you know, independent variables. And that nature of the problem, we were able to expand it to perform at the level of about 3800. So that was a pretty big amplification of the qubit capability and the way we did it, maybe the details are not that important, is we the algorithm allowed the D-Wave hardware to not have to do the embedded threading that it does to use most of its qubits as you know, ancillary qubits to do connectivity versus to do computing. And we got answers, we got sensor counts and coverage map. So, we're able to present. So it's kind of like it was it was that or not provide a solution. So that was a great example of the variational algorithm for the for the D-Wave and the other. And we did a similar algorithm approach for a, for a gate model system called IonQ.  But it would work for any gate model system in principle, where it does similarly uses machine learning to provide a good starting point and sort of provides the right perturbations to the system so that it converges quickly and gives the system more scale beyond to solve problems greater than the number of qubits physically in the system.

Brian Walker: You've also recently finalized an acquisition. Can you tell us a little bit about that and how it's impacted the company?

Bob Liscouski: Well, we weren't looking to do an acquisition when that came along. Quite honestly, we were just heads down focusing on, as Bill's pointed out here, you know, really focused on software and amplifying the amplification of the software and pursuing that. They approached us very smart as a small little startup out of Stephens Tech, headed up by the CEO Dr. Yuping Huang, brilliant physicist, has done some interesting work there and wanted to commercialize, has already started a company and wanted to get into the commercial space and actually get into the public market. So he initially approached us and when we began the conversations. The thing that was, you know, was really apparent in the conversations was that we were very much aligned both in the business philosophical point of view as well as a, you know, a really a business goal as well. Right. So philosophically, you know, he as well as we wanted to bring quantum computing to the business environment. Right. We didn't want this to be an elitist approach where only a select few companies that could afford to get into quantum computing with the resources necessarily could do that. We believed and still do believe that the more users there are in the quantum computing space, the more that the industry is going to benefit not just from because of the marketability of it, but most like every other technology. The more users are involved, the more the applications get developed. They demand more from the technology. It really, really makes it you know; it makes the industry have to be responsive to that. And we believe that that's the case. So, we're philosophically aligned on there. And then technologically, he and Bill is much better at describing this than I, but I just tell you from a business perspective, the research that is done and the IP that we are developing with them now under QCI’s banner because it uses photonics, has so many business advantages, not just because it actually has a computational capability that as we’ll demonstrate, can actually deliver business value today for a quantum computer. But the infrastructure, because it's photonic based room temperature, doesn't require any special infrastructure and it's desktop size, meaning that it's a very scalable, very portable type of machine that, you know, we believe is going to be and I and I hesitate to use some big language here, but we do believe it's going to be a game changing type of approach. We didn't know that in the beginning. You know, we like the idea of where it was going to go. The more we got involved in the transaction, doing our due diligence, and we did this over several months and we got to know Dr. Huang, it became very apparent that he had technology that is indeed going to change the way people view quantum computing. So, it made the transaction for us all the more sensible in terms of the value we were going to be able to provide to the shareholders, because we have become a full stack quantum computer and it's interesting software company buying a hardware company. But more important than that, because we become a full stack company and a quantum computing company, we can deliver the promise of quantum computing to end users through the software platform, right through the computational output and the results. And we can do it in a way that's not just cost effective, but obviously computationally very effective. And so it made all the sense to us in the world. And I think Bill can probably give you a better description than I.

Brian Walker: So Bill, how do you see this helping advance quantum and in what particular vertical markets are you focused?

Bill McGann: So, you know, just to thread the needle from what Bob was saying, it became very apparent very quickly to us that the partnership was strong between the QPhoton hardware in our software approach. And so, we endeavored to do a merger and successfully closed one. And the partnership is stronger every day between us. The hardware itself is incredibly impactful. I don't want to make bold claims either, but I'll tell you what, it's factual that I have physically done with the machine myself and observed being done with some of my colleagues so that the BMW challenge I just mentioned that we did last year with a D-Wave machine being supported by our machine learning software. Well, this year we're presenting data actually on Monday, this coming Monday to BMW with the summary report. And I'll be doing a presentation, a PowerPoint, you know, 20-minute presentation with questions and answers to the same problem, 3854 variables, I believe, is the count roughly. And we solve that problem with no software, just a direct input of the problem into our new entropy quantum computer, which operates at temperature and got very good results given that this is a brand new platform for hardware computing and I think it gives us a seat at the table of moving the industry forward in a in a direction that takes perhaps some of the estimates of where quantum computing might be by 2025. I think we're there much sooner based on that one example alone. And we have other people now, potential customers, but certainly people interested in partnering with us in financial services as well as in computational fluid mechanics, people are doing wind farm kind of mapping where the, the mathematics is really complex and the interactions are quite complex and, and the variable diversity can be quite large. To solve these problems with a good level of granularity and we're solving those problems today with, with our new hardware.

Brian Walker: So how do you view the role of the IEEE Quantum Initiative in helping advance quantum technology?

Bob Liscouski: So great question. I think. You know, when we start talking about inclusivity and the ability for us to be able to, you know, widely scale quantum computing, you know, I think the notion of standards clearly is, you know, is an important one. We've all worked in industries before whereas we've seen earlier on and, you know, in the classical industry, you know, computing standards and, you know, software standards are all difficult to be able to kind of enforce because it's a race to the finish line. And there are many different ways to do that. I think the quantum computing industry is probably no different. As Bill earlier mentioned, you know, you've got gate model, you've got annealing, you've got these different approaches to ostensibly achieve the same goal. But that's a pretty costly approach for the average user community to be able to adopt. And I think I'm not suggesting there's going to necessarily be standards here that are going to be widespread yet because there's no clear winner in the quantum computing industry. But I do believe that, you know, a more standardized approach and, you know, helps the academic institutions once we get out of the research phase of, you know, trying to really commercialize quantum computing. And once things become more, steady state in terms of the application space, you know, I think that's where the lessons of the past are going to be very applicable to what the future can be. You know, and I think the IEEE has been a leadership has been leading clearly leading in this space for quite a long time. So, you know, I think the efforts there are really well founded and well positioned for quantum computing.

Brian Walker: Okay. Bill, did you want to add anything?

Bill McGann: You know, I think Bob said it well. I mean, it is definitely too early to pick a winner. And I'm not convinced at this point that that's actually a good thing anyway, because I think the diversity of problems in the world that I'm aware of it is, you know, probably not going to be resolved or addressed by one particular type of quantum system, for example. ANJILA really good at finding the optimum point in a sort of a diverse, you know, topological map or find the ground state with a whole bunch of constraints placed upon it. You know, gate model machines are more like a general purpose, you know, machine that those calculations. Right. They actually are quite different now. They can be kind of forced into each other’s domain space because people that build them designed these machines are capable of thinking how to reconfigure their architecture. But the way these things have naturally come about is that kind of have natural, sweet spots, right. I guess is maybe a good way to say it. And I think that's probably going to be around for at least a very long time, if not for the foreseeable future, a long time, and that's okay. We want to have a highly scalable gate model, you know, circuit-based systems to support our entropy. Quantum computer, for example, is not a great model. It's more like a D-Wave Anila. And now it doesn't use, doesn't rely on the same physics, but it ends up with a similar sort of result when you put a problem and it's got a sweet spot for finding, you know, highly constrained, high variable count, you know, cost objective kind of problems that you find the best answer. It's beautifully built for that and we're demonstrating that as we speak. So, I guess, you know, there's a diversity of problems is going to require a diversity of technologies. And we just want to have a seat at the table and, you know, pick the right partners, and pick the right verticals for us to go after.

Brian Walker: Thank you again both for taking time to speak with us today. In closing, do you have any final thoughts you'd like to share?

Bob Liscouski: Well, I think, you know, from our standpoint, I'll speak to you as the CEO of the company is that you know Bill and Yuping and the team but really led by Bill and Yuping have a tremendous vision of where we think we can bring quantum computing today. And I would tell you, as a CEO of the company, we try to do this in a very realistic and very pragmatic way. We don't try to hype it up. We try to be very evidence based, very fact based and if we say we can do 3000 variables, we can do 3000 variables. If we say we have a problem that we can solve, it's got a business relevance to it we can do that. And we're very willing to demonstrate that kind of proof because, you know, we're a small little company. We're not an IBM, we're not Google. We don't have big names next to us. So, people aren't going to believe a little company like ours can necessarily do it. But I will tell you, we can prove it. And I always believe that the best innovation comes from small companies. And then with Bill and Yuping and the team, they're going to prove it.

Brian Walker: Thank you for listening to our interview with Bob Liscouski and Bill McGann. To learn more about the IEEE Quantum Initiative, please visit our web portal at