Podcast with Dr. Bob Sutor, VP of Corporate Development at ColdQuanta

Transcript

Yuval Boger: Hello, Dr. Bob, and thanks for joining me today.

Dr. Bob Sutor: Pleased to be here. I’m glad to be back.

Yuval: So who are you, and what do you do?

Bob: Well, I’m Bob Sutor. I’m somebody who’s been in the computer industry for about 40 years, as it turns out. I guess I must have started very, very young. I work in quantum computing. For a long time, I worked for IBM, in particular, IBM research. And then over the last, well, let’s say half-decade, in IBM Quantum. But I left. I retired actually from IBM, at the end of February. And one minute later, I started working for ColdQuanta, which is a quantum company in Boulder, Colorado, as well as other sites in Chicago, Madison, Wisconsin, and the UK. And I’m the VP of corporate Development there.

Yuval: So IBM employs over 200,000 people, and I think ColdQuanta, maybe around 200. That’s about a thousand-to-one ratio. How do you feel about the move, and what advice do you have to people who make this move from large companies to smaller ones?

Bob: It’s been a tremendous amount of fun. Let me say that through the years, as I’ve talked to people who maybe tried to attract me to smaller companies, they seem to have this assumption that at very large companies like IBM, you have all these minions running around doing everything for you. They say, “Well, in a startup, you have to climb under the desk, and you have to do what’s necessary,” and things like this. Well, guess what? You’ve got to do that in large companies today as well. This certainly is an opportunity, though, as you said, this scaling of a thousand, to get to know pretty much everybody that’s in the company, but really always feel you’re at the center of what’s going on.

Now, IBM Quantum was like that. I mean, it was a few hundred, let’s say, but not all of IBM. I did know probably a few thousand people by the time I was done. But what I’m especially really enjoying is living at this core of the startup as it is devising its strategy. It’s looking the best way to use the technology. I’ve been working with investors, for example, which is not something I did at IBM. That’s a very different type of world. It’s been very invigorating, in many ways, including intellectually.

Yuval: At IBM, these were superconducting qubits and at ColdQuanta, cold atoms, I think, and then ColdQuanta does other things. How does corporate development relate to all these various things that ColdQuanta is doing?

Bob: Well, corporate development’s an interesting title and we devised it, it’s a little bit, I wouldn’t say catchall, but kind of an umbrella type of title. That is, yes, ColdQuanta is small, but ColdQuanta doesn’t want to stay small. ColdQuanta wants to grow. ColdQuanta wants to be successful. So part of the reason why I’m there is to bring a big company perspective to this company as it grows. When you are a large company, you do some things very well. And frankly, you do some things not so well. There could be extra bureaucracy. There could be slow decision making. I mean, any number of things. It’s a human condition.

So how can ColdQuanta grow successfully in this area of quantum, not just quantum computing, but things like quantum sensing and so forth, while avoiding the pitfalls that some large companies have found themselves in? ColdQuanta is very much part of the quantum ecosystem, and I’m here to encourage that, to grow that, and to help them produce great products.

Yuval: As you’ve mentioned, I’ve interviewed you before. I think it was about a year ago, so welcome back. Thank you for doing this again. What have you learned about the quantum market in this past year since we last officially spoke?

Bob: Well, there’s a life cycle to it. Let me describe that. When we started seriously talking about quantum computing and future commercialization, we were very clear that this was going to take many years. Years and years and years. And there were going to be little steps, and there would be different breakthroughs, but it would take a while to get to what some people call quantum advantage or practical quantum advantage or practical business quantum advantage. And I think people understood that. They spent a lot of time learning about quantum. But I sense that we’ve hit this period where people keep asking, “Are we there yet?” It’s almost like if you have children and you’ve ever taken them on a car trip, you say, “Okay, look, this is going to take three hours. We can’t get there any quicker. I’ve given you what you need in the backseat of the car,” and then after 15 minutes, they say, “Are we there yet?” We say, “No. We said it was going to take three hours to get there.” And then they say, “But I want to be there yet.” What people want, understandably, is quantum advantage.

So in this, what I’ll call the middle period, the beginning period was, let’s say, the first five or six years, and now we’re in this middle period. There’s an expansion of the understanding of really the rate and pace at which quantum computing will become available and will become useful. There’s an expansion of the ecosystem, the education. There’s maybe a little bit of a resetting of expectations of when it will be here. Now, those expectations may have been set originally unreasonably, but people are getting a more concrete sense of where are we, what will these technologies be used for? And they’re also starting to ask other questions.

I think a lot of people view quantum computers as future supercomputers. Now, if you look around at the various supercomputers around the world, these are big. They take up a lot of room, they take up a lot of energy. And yes, great for solving very hard problems. But we don’t use supercomputers in all of our daily lives. I mean, one of the recurring facts about the computer industry is that hardware keeps getting smaller and smaller and more powerful. This was Moore’s Law. So, there’s no reason not to think as long as we’re worrying about the future in use cases, that quantum computers will get smaller and they will spread out, and become more ubiquitous. So we can ask more serious questions, not just saying, “Well, what is quantum computing in a data center?” But what does quantum computing mean at the edge? Well, now we start talking about, well, what are those applications? What are those use cases? How are they different from the supercomputer ones? Do they involve machine learning, for example?

So in this middle period, what I’ve learned, to answer your question, is, while progress is steady, the depth of understanding and the depth of thought is increasing around what quantum computing could be as we develop all this technology.

Yuval: And it seems that we’ve shifted from whether it will ever work to when will it be good enough? So what’s your answer on “when”?

Bob: Well, I used to describe it this way, and I think it’s still valid. Let’s talk about this notion of quantum advantage and that’s related to the when. And so what I’m going to term quantum advantage is when this combination of classical computers and quantum computers can do better than classical systems alone. Some people might throw in the word significantly better, but I’m okay with just saying better to start with. I think in the first phase, what we will see are some really arcane gorpy examples in the next two or three years of where quantum computers plus classical computers can do some interesting things, maybe in chemistry, things that we just can’t. Now, people might say, “Well, how does this affect my everyday life?” And the answer is, “It doesn’t,” but it’s good progress along the way and improves certain points.

The second phase, which I think will be in the, let’s say, the five years after that. So I started with two or three years, and now I’m saying five years and maybe five and a little bit more, is when we start seeing, for very specific industry verticals, applications of quantum computing. And I’m emphasizing the word computing, I’m emphasizing also the word calculation because quantum computers still will not be big enough probably to deal with large data sets in that second phase. In the third phase, we’ll start to see error correction, fault tolerance, we’ll start to see quantum memory, and we’ll start to see far more widespread use. Now that will dribble out over, I guess, what am I up to? Eight or nine years and so beyond that point, 10 years or so, those are the types of things we’ll see.

Yuval: So if we pick up on one of the industries you mentioned is early adopters, or some advantage will come for chemical. So let’s say I’m CIO CTO, CEO of a chemical or pharmaceutical company. When should I get started with quantum, and related to that, what kind of people should I hire? Should I hire quantum information science PhDs? Should I upskill my existing people? Should I get McKinsey in or Deloitte? When and how is the best way to go about it, in your opinion?

Bob: First of all, I think, we’re speaking very generically here. You need to answer the question, “Will quantum computing be relevant to me?” If I’m a food manufacturer, just a straight-up food manufacturer, maybe not so much. If I am actually though devising new chemical processes for certain things, then I could say yes, quantum computing. I might say, “Do I use high-performance computing right now? What are the bottlenecks there? Can quantum computing help?” So before you do anything, and once you get past the allure of this word quantum, you have to say, “Is it going to ever be relevant to me?” And if so, where. And now you can start asking the when questions and how and the who and things like this.

I think there’s a lot of latent talent out there. I know in my previous life, when we looked around for quantum talent, well, that wasn’t necessarily people’s first thing on their resume. But then, if you go back a little bit, there are an awful lot of physics PhDs out there who may be doing other things and doing other things in business already. I remember meeting a CFO of a company who was a physics Ph.D. Now, I think he enjoyed being a CFO, but this latent talent that people you may already have in your organization. So if you don’t have the talent inside to guide you on quantum, yes, by all means, use the consultancies, use some of the organizations that you mentioned to come up to speed, to understand where and possibly when. And then you’ve got your decision about skills development and so forth.

The educational system itself is shifting. So three or four years ago, most of the people who I would say were in quantum computing were physics PhDs. And I used to advise people, young people, when they would say, “Well, what should I major in?” Well, back then, I said, “Major in physics, minor in computer science.” I think we’re starting to switch that. Major in computer science, minor in physics, as we say, in most technologies. So yes, begin by understanding your core strategy and then worry about the milestones and the people.

Yuval: And you touched on education, your former company, IBM, does a whole lot of education. I think it’s summer schools and challenges and so on and so on. Do you see that as the primary path that companies should do to educate the market? Or should there be more of quantum for optimization experts or quantum for chemistry experts, or as you mentioned, your major is in something else that’s not quantum. And now we need to bring you up to some quantum knowledge, not necessarily at the gate level to say, how can I solve these problems using quantum computers?

Bob: I think if you are serious, let’s say, optimization practitioner. Yes, you certainly should be understanding quantum computing. Now let me say, it’s not a foregone conclusion that all these optimization techniques that people are speaking about with quantum, either using variational techniques today or even eventual fault-tolerant methods, will be better. Because remember, quantum computing is only going to really be a serious contender for problems that are too hard classically. I mean, if it’s a relatively easy problem, just put it the way you’re doing it. If it’s being done in time. So the problems we will be looking at will be very hard, maybe very hard problems.

So at this point, the setup, if you will, just to do the problem on a quantum computer may dominate the time it’s going to take. And what is the trade-off there? So if you are an optimization expert, yes, track that. And in the same way, if you are into the computational aspects of logistics, which is a form of optimization in many ways. Yes, you would want to do that. I think quantum chemists are already there, honestly. I think they are probably the closest to quantum computing and how they’re being used. The descriptions of the chemical algorithms are completely in their toolkits of what they do already. So yes, learn what these things are.

Now, there’s one other one that you didn’t mention, which is AI and machine learning. That’s a tricky one because there’s a lot that’s been written. There are a lot of things that people are doing, but really we have to admit that no one is changing the world of machine learning with 10 qubits or 20 qubits, or 30 qubits. We’re going to need an awful lot of qubits. We’re going to need a serious machine to actually be able to do much better than we can now. And for some parts of machine learning, we will, again, need things like quantum memory to deal with large amounts of data. We’re going to need much better coherence times, we may need fault tolerance. So don’t judge how close we are to commercialization by the sheer number of papers on the topic. Look at the scaling factor. Are the systems big enough to solve the problems that of interest to you? And that’s a very different question.

Yuval: As we get close to the end of our conversation today, I wanted to ask you a couple of questions about ColdQuanta. ColdQuanta is doing both computing and sensing. Is that just a byproduct of the same core technology, or do you see an overlap between the ability to do high-performance quantum sensors and high-performance quantum computers?

Bob: Very much an overlap. And so ColdQuanta started in 2007. One of the founders, professor Dana Anderson, the University of Colorado, Boulder, is still part of the company. He is the CTO. And the idea is that we use natural cold atoms for our qubits or within our sensing apparatus, our sensing devices. Turns out you can do a whole lot with atoms and lasers. Our CEO likes to say, “Well, we shoot lasers at small things. And those small things happen to be atoms.” If you put those atoms together using lasers into an array, well, now we think of an array of qubits, and you start using things like Rydberg atoms, Rydberg effects, to do two-qubit gates, things like this. So all of this language translates over to very natural properties of atoms.

Now, atoms are good as qubits because we don’t have to manufacture them. There are no manufacturing defects in the qubits themselves, but we still have to control them. We still have to have this laser technology. And this is going to be the same whether we are using these atoms for sensors, inertial sensors of some type, we could use them for atomic clocks. We could even use them as RF antennas, very sensitive RF antennas. So there’s this duality between quantum computing with atoms, where you want a very pristine environment, as you always do with quantum computing. You don’t want other quantum effects to disturb your computation. But sensing you want to let the outside world in. You want to tell how fast am I going. You want to be able to compute things for time, ultimately positioning and things like this. So it’s the same technology at its core. Different applications may use different atoms.

Now, one thing I do want to mention about lasers is that the good thing is lasers aren’t just used for cold atoms or indeed things like ion trap technology or photonic technology. This an extraordinary industry built around lasers. LiDAR for cars, barcode readers. If you remember DVD or Blu-ray or any of these things like this. Lasers are pervasive throughout many, many different technologies. So this idea of producing less and less expensive and smaller and smaller lasers and integrated photonic technologies that will be valuable in many industries, will feed completely into the use of cold atoms for computing or sensing. So we are benefiting from the millions of dollars of investment around the world in these other technologies. And it’s this, it’s the attractiveness of these, if you will, natural qubits, I sometimes joke, I call them organic qubits, these natural qubits, or using the atoms as sensors coupled with this cost down, scale down work that’s being done on lasers that I think will ultimately make this technology successful. And I think it’s extremely versatile. And frankly, I think it’s much more versatile than some of the other cubic technologies that are out there.

Yuval: Most of the people I speak with, and perhaps that’s because I come from the computing side, think that ultimately the market potential for quantum computers is going to be much larger than for quantum sensing, in terms of total addressable market and the dollar value. Do you agree, and do you also see this in the short term? I mean, you mentioned, we spoke about the timeline for quantum advantage, will it be the case that in the next couple of years, actually, there’s going to be much more business around quantum sensors, and then ultimately quantum computers will take that over? How do you see that?

Bob: The numbers I’ve seen is that if you look at the total market for what we’ll call sensors and here, remember there are a lot of things that we’re throwing into here. So we’re throwing in atomic clocks, we’re throwing in various types of inertial sensors. We’re throwing in potential RF applications. That total market will develop to be roughly the same size as the quantum computing market. It’s just when you pick a part and say, well, this bit over here and that bit over here. You have to add it all up. And in the same way with quantum computing, you have to ask fundamentally, what is the business model? Am I just selling time on a cloud? Am I selling devices, big systems, or am I going to shrink those down and sell smaller systems and maintain them or different things like this? So really, I would say even what the business model is for quantum computing and how people will get paid is evolving. And the final sweet spot is not a foregone conclusion by any means.

So I think that they are both quite valuable, but I think we will see quantum sensors become much more prevalent and in production, much faster than quantum computing. They’re smaller. You don’t need 100,000 atoms in a sensor. You can make do with far fewer than that. Whereas you do, in fact, ultimately will need thousands, tens of thousands, hundreds of thousands of qubits in a quantum computer that can do something better. So quantum, as it relates to sensors, will be far more immediate. I do think though, that the market, if you look at it, is likely to go through governments first. I think, I mean, who uses sensors? Where do we need sensors? It’ll go through government and then a later step of commercialization. Although you will see some industries, I think like automotive picking up quantum sensors soon. So I think it’s easy to think of quantum computing as one thing, but quantum sensing, it’s lots of things, and you have to look at it in total.

Yuval: Excellent. So Bob, thank you so much for joining me today. But before we go, how can people get in touch with you to learn more about your work?

Bob: I think the best way is on LinkedIn. I’ve got a great network of people interested in quantum and all sorts of other things. And I’d love to meet more people who are part of the ecosystem. Thank you again for having me.

Yuval: Thank you for this second time. And I look forward to the third.

Bob: Very good.

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

September 22, 2022