Quantum Computing

The Current State and Promise of Quantum Computing

Robert Liscouski

Brian Walker, the host of the IEEE Quantum Podcast series, asked Robert Liscouski, president and CEO, and William McGann, CTO, and COO for Quantum Computing, Inc. share insights on the current state of the quantum landscape and the promise of quantum as the technology advances.

Q: What is your high-level view on the current state of the quantum ecosystem?

Liscouski: I can start from a business perspective and Bill can weigh in on the technology side. Since we started doing this a little bit over four years ago, the market then was very early stage. And it’s slightly matured today with more entrants in the marketplace, more hardware vendors, and 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 provide some sort of material value.

Now, people talk about quantum supremacy or quantum advantage, but in reality, I think they’re looking to try to eke out some benefit 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 there’s still this opportunity here for companies to demonstrate that quantum computing is going to provide some near-term benefits. Certainly, it will in a few years. And we can talk about where we play in that space, and we will. But I think the markets are 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.

McGann:  I think the industry is sort of no longer embryonic, but it’s still in a nascent stage where there are a lot of different implementations of hardware architectures, all of which have some promise, none of which are demonstrating enough scalability yet to be truly practical for a commercial endeavor, let’s say.

They’re the people still very heads down on their technology, very focused on demonstrating they can build a certain kind of gate that has a certain type of decoherence. For example, D-Wave has its annealing architecture where they’re trying to continually improve performance.

Yet, the industry hasn’t come to a place where you can solve real-world customer problems to provide cost benefits to a supply chain problem for a financial institution or optimization problems that relate to chemistry, for example.

All these things are possible and QCI was 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 to expand on the capabilities of the current hardware, for example.

And then, of course, as you know, more recently, we have become a full stack provider with our own hardware, which we’re excited about. So, the industry’s changing in that direction where companies 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 both together in a meaningful way.

Liscouski: And if I can just build on that a little bit, Bill did mention a very important point when we started the company. We started as a software platform with the intent of bringing quantum computing to end users without the burden or necessarily the quantum computing and quantum programming resources to allow a quantum computer to work.

I think your audience is probably well aware you just can’t program a quantum computer as you can a classical computer and get things to work. There’s none of that sort of software architecture to allow that to happen easily. The IBMs of the world have their toolkits and all the vendors have some form of a toolkit that requires some significant levels of programming to get them to work. And then you need to have the problem formulation and tuning the problem to the computer, etc. And you go through this iterative process that’s pretty high cost.

QCI saw the opportunity of creating a platform that would disintermediate the need for those high-level resources and allow an end user to formulate their problem to run on a QPU. We do that through AWS Bracket. We can run on any number of QPUs, and we’ve had success in doing that.

The challenge, 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 there is some promise for quantum computing downstream.

So, when we started going off in this direction, we were hoping the industry would 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 just mentioned, this recent acquisition changes the game for us.

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

Bill McGann: Yeah, I could take that one to start with Bob. So, let’s talk in real terms by example, right?

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 and they didn’t put constraints on whether it had to be a quantum computer or classical.

BMW Group was just looking for technology solutions to solve, not a huge problem, but a real-world problem in terms of the scale of meeting the equivalent of many thousands of qubits. 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 three, four, or 5,000 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, was able to provide a solution to that problem. That was a pretty good proof point to us that we had something algorithmically that could take the current set of hardware, namely in this case, a D-Wave annealer, which on a problem with this kind of mapping and density would not be able to probably consume more than about 150 independent variables. And given the nature of the problem, we were able to expand it to perform at the level of about 3800.

That was a pretty big amplification of the qubit capability and the way we did it, maybe the details are not that important, is 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 a coverage map. So, we were able to present. That was a great example of the variational algorithm for the D-Wave and the other. And we did a similar algorithm approach for a gate model system called IonQ.

But it would work for any gate model system in principle, where it similarly uses machine learning to provide a good starting point and provides the right perturbations to the system so that it converges quickly and gives the system more scale to solve problems greater than the number of qubits physically in the system.

Q: 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 head’s down focusing on, as Bill’s pointed out, 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, a brilliant physicist, who has done some interesting work there and wanted to commercialize. He had already started a company and wanted to get into the commercial space and into the public market.

Dr. Huang initially approached us and when we began the conversations what was really apparent in the conversations was that we were very much aligned both in the business philosophical point of view as well as business goals. Philosophically, he as well as we wanted to bring quantum computing to the business environment.

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 could do that. We believed and still do believe that the more users there are in the quantum computing space, the more the industry will benefit. Not just because of the marketability of quantum, but—like every other technology—the more users are involved, the more the applications get developed. They demand more from the technology. It really makes the industry responsive to that.

And then technologically (Bill is much better at describing this than I) I’ll just tell you from a business perspective, the research we’re doing 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 has a computational capability that can deliver business value today for a quantum computer. But because the infrastructure is 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 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 as 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.

It’s interesting, a software quantum computing 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. So, the acquisition made all the sense to us in the world. And I think Bill can give you a better description than I.

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

Bill McGann: 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 and our software approach. And so, we endeavored to do a merger and successfully closed one.

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.

This year we’re presenting data on Monday, this coming Monday to BMW Group with the summary report. And I’ll be doing a presentation, a 20-minute presentation with questions and answers to the same problem, 3,854 variables. And we solve that problem with no software, just a direct input of the problem into our new entropy quantum computer, which operates at room temperature and got very good results given that this is a brand-new platform for hardware in quantum computing.

I think it gives us a seat at the table of moving the industry forward 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.

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 mathematics is really complex and the interactions are quite complex, and the variable diversity can be quite large to solve these problems with a good level of granularity. We’re solving those problems today with our new hardware.

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

Bob Liscouski: So great question. When we start talking about inclusivity and the ability for us to be able to widely scale quantum computing, I think the notion of standards clearly is an important one. We’ve all worked in industries before where, as we’ve seen earlier on in the classical industry, computing standards and software standards are all difficult to enforce because it’s a race to the finish line.

There are many different ways to do that. I think the quantum computing industry is probably no different. As Bill earlier mentioned, you’ve got the gate model, you’ve got annealing, and 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.

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 a more standardized approach helps the academic institutions once we get out of the research phase of trying to commercialize quantum computing. And once things become more steady state in terms of the application space, I think that’s where the lessons of the past are going to be very applicable to what the future can be.

I think the IEEE has been clearly leading in this space for quite a long time. I think the efforts there are really well founded and well positioned for quantum computing.

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

Bill McGann:  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 a good thing anyway, because I think the diversity of problems in the world that I’m aware of it is probably not going to be resolved or addressed by one type of quantum computing system, for example.

Annealers are really good at finding the optimum point in a sort of diverse, you know, topological map or finding the ground state with a whole bunch of constraints placed upon it. Gate-model machines are more like general-purpose machines for those calculations. They are quite different now. They can be kind of forced into each other’s domain space because the people who build them are capable of thinking about how to reconfigure their architecture.

But the way these things have naturally come about is that they kind of have natural, sweet spots. I guess that’s maybe a good way to say it. And I think that’s probably going to be around, 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 annealer. And now it 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, cost objective kind of problems so 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 that are going to require a diversity of technologies. And we just want to have a seat at the table and pick the right partners and pick the right verticals for us to go after.

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

Bob Liscouski: Well, from our standpoint, I’ll speak to you as the CEO of the company. 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 the 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 3,000 variables, we can do 3,000 variables. If we say we have a problem that we can solve, and it’s got a business relevance to it, we can do that. And we’re very willing to demonstrate that kind of proof because 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.

I always believe that the best innovation comes from small companies. And 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 Quantum.IEEE.org

Listen to the podcast: IEEE Quantum Podcast Series: Episode 14