Helmut Katzgraber, Global Practice Lead at the Amazon Quantum Solutions Lab, is interviewed by Yuval Boger. Helmut and Yuval discuss the process by which the Quantum Solution Lab helps customers, successful projects with BMW and Goldman Sachs, advice for customers thinking about such projects, and much more.

Yuval Boger: Hello, Helmut. And thank you for joining me today.

Helmut Katzgraber: Hi, Yuval. Good to meet you. Nice to be here.

Yuval: Great to have you. So who are you and what do you do?

Helmut: So my name is Helmut Katzgraber. I’m Global Practice Lead for quantum. It’s an organization part of professional services, and we are the team that basically engages directly with our customers at Amazon.

Yuval: How does that work from a customer perspective? How does a customer get to you, and what can they expect to receive working with you?

Helmut: So one of the things that is very dear to me is to be relatively realistic as to what quantum technologies can and cannot do. And so when we engage with customers, they usually come because they want to learn what quantum computing can do for them in the future or they come with a problem that they seem is insurmountable, something that is very, very hard. And so there’s always a desire, well, maybe a quantum computer can solve this. And in both cases, what we like to do is we ideally want to, of course, prepare them for a quantum future, so work with them hand-in-hand and try to build a solution that will eventually run with our quantum machine at scale. But in an ideal world, we also try to find solutions that leverage today’s technologies being operations research, high-performance computing, machine learning, to actually solve the problem at scale. So it’s a little bit of a dual track approach that allows us really to be forward-looking but also bringing value to the customers today.

Yuval: Do you prefer quantum-only problems as opposed to problems that require other aspects of AWS, more of on the classical side?

Helmut: That’s a good question because I think the fair thing to say is that I prefer challenging problems. And so where I’m going with this is we are very much tool agnostic. We love working really on quantum problems because this is the core expertise of the team, but we also like to dab into other technologies. And so, to give you a couple of examples, with Goldman Sachs we did theoretical resource estimation of a quantum algorithm in finance, which is purely paper and pencil quantum work, and it was recently submitted for publication. But on the flip side, for example with BMW, we looked at robot motion planning in the production plant, showed them how you could potentially solve this in a quantum machine in the future, but then found alternate technologies that work today to solve it at scale and find an improvement of, in this case, 10% to what the robots used to take to for example, apply PVC to a seam of a car.

Yuval: How long would a typical project take from start to finish? And related to that, do you accept any customer to work with you, or do you focus on a particular size or particular type of customers?

Helmut: We have teams that focus on different types of customers. If we have a customer that needs, for example, hand-holding on how to use Braket, our fully managed service that has been in GA since 2019, then usually we pass them over to our solution architects in the worldwide sales organization, because they are the ones that really hand-on work on the service. We prefer customers that require really deep research engagements where you really have to dive down deep, and, therefore, really flex mental muscle to actually find a meaningful solution to a problem. So everybody’s a customer at Amazon and we welcome everybody to play, we just try to focus on the problems that are really, really tough to solve.

Yuval: When customers think about needing help to solve such a problem, would they normally think of AWS, or would they think of maybe a consulting organization, BCG or Deloitte, or one of these that have a lot of subject-matter experts? How do you position it relative to the consulting organization?

Helmut: I think… And I don’t want to step on everybody’s or anybody’s toes. I think that we have people that are really deep subject-matter experts, that have worked in the field for often many, many years and have really achieved a lot scientifically. And that type of depth is often not found in consulting firms. And so this is what I said for us, for example, at the end of an engagement, it’s important to publish a paper in a scientific journal and that requires a certain depth of rigor that you can only do in an organization where you have the freedom to actually work on a project. Now you asked me earlier, how long do these projects take? And what I’m going to tell you now, it will reflect exactly that, we can have projects that take three to four months, those are rare, but typically they take between give or take six months and 18 months. So this is really deep research work.

Yuval: For instance, the BMW project that you mentioned, could you tell me roughly how long it took, and what were the key stages in such pressure?

Helmut: The BMW project took about six months, from beginning to end. The way we engage is we have a first call with a potential customer, we hear what the problem is, then based on that we basically schedule a deep dive between two and four hours where we sit down with subject matters experts on their site to work with us. And that’s because, for example, we’re not experts in robotics or in finance or in clinical trials, and really hash out the details of the problem. We then go back, think about potential solution strategies and what our proposal. If the customer is interested in pursuing this, we scope out the project and then we kick it off and roll up the sleeves. Now while the project is running, again at the beginning there’s always a research and discovery phase where we really try to dive deep, and then subject-matter experts, in this case on BMW’s side and on our side, will then work hand-in-hand to actually build a solution to the problem.

Now in this particular example, we ran on two tracks. On one hand, thought how can we map this onto a quadratic and constrained binary problem which is a language that, for example, quantum annealers speak. And in parallel, we brought in expertise from our own middle-mile optimization team, Maurizio Resende, who is very well known for his work, and basically thought, “Well, how can we solve this problem at scale with an algorithm that can run on a standard computer today?” And eventually, these two tracks merged. We wrote up a nice paper that you can now read, it’s published as of last December, as far as I recall.

Yuval: When the project does involve quantum computers, do you find that you also engage with the vendors themselves, say Rigetti or IonQ or QuEra or other people that are hosted on Braket, or is it typically just the insight team?

Helmut: So it’s a good question. And needless to say, what the end user often gets on the machine is a downsized number of features, there’s some additional buttons that can be pushed and turned here and there. And so in specific cases, I can use the case of QuEra, which is one of our partners, we work also with researchers at QuEra to be able to tickle out the most performance for a particular problem. And so right now, we have an ongoing project in finance, I cannot disclose the customer yet, but there really we’re working hand-in-hand with QuEra to do better device calibration, to understand the problems better and see how we guess the best can embed a real-world problem onto QuEra’s hardware. And some of the things that we develop with our customers can potentially also flow back into the software or hardware roadmaps of our partners.

Yuval: I assume you’ve done a number of projects with different customers by now, and I guess, based on feedback that I’ve heard that most customers are very happy with the experience. What do you think customers are most surprised with? Is it that quantum is better than classical? Is that classical is better than quantum? What do you think if you speak with the customers, they say, “Oh, I didn’t know that was going to happen.”

Helmut: So this is a good question because it’s also a very pointed question, in that obviously customers are happy that they have a forward-looking quantum solution. But I think the biggest surprise for our customers, which is two things intertwined, is that by looking at a problem through a very different lens. Say you look at an optimization problem, traditionally, operations researcher would look at this, and we look at it through the angle of a physicist. And so you take a very, very different perspective to the actual problem, and then suddenly you unlock a solution that they had no idea existed before. And this can be quantum, this can be non-quantum. And I think this is usually where the big surprises happen, that not only do we prepare them for the quantum future, but we also bring value to them today.

Yuval: And is the team primarily physicist? I believe you have a Ph.D. in physics, and you have a lot of published articles on academics, and you’ve been a physics professor and so on, but is your background representative of the rest of the team?

Helmut: The background is very broad. We have physicists, and even as physicists, we have a spectrum, we have statistical physicists, we have mathematical physicists, theoretical physicists, people that focus, for example, on cold atomic gases. We also have operations researchers, we have machine learning experts, we have two HPC experts. And so, one really important thing for me was to bring all these different disciplines together and hire a team of people that come with very different perspectives. And this is what has allowed us to innovate at the cross-section of these different disciplines.

So I can give you a simple example, there is this important problem known as a maximum independent set problem that happens all over a combinatorial optimization. And one of my quantum physicists was working with a machine learning expert, and basically, they realized that, in principle, you can represent this problem as a physics Hamiltonian, but then you can use graph neural networks that run on standard EC2 hardware to solve the problem at scale. And so this is really the interesting mix. You see, these two communities would’ve never, ever talked about doing combinatorial optimization with this graph neural network, and now it’s a thing. And so this is very important to me and this is why I believe strongly that interdisciplinary research is key to unlock the next levels.

Yuval: When customers come to you, where are they in their quantum journey? And what I mean by that is the successful projects that you completed, do some of them move into production, or are they more about understanding the capabilities and extrapolating and saying, “Well, in 18 months or 24 months, we’ll have a computer that’s strong enough to run it.”

Helmut: All of the above. We have customers that are very early in their quantum journey. We have customers that have invested heavily in quantum computing that have in-house teams. And we have customers that are really happy when some of the things we built not only give them an indication when a quantum machine will have impact for them at scale, but also that we are providing a solution that is now running in production and we have a handful of those that I think is a big source of pride for the team.

Yuval: I assume there may have been a couple of projects that did not live up to expectations, what did you learn from those? Is that the customer’s fault? The customers not being ready, or defining the problem well enough? Is that just an unsolvable problem? What can you teach us from the failures?

Helmut: I hate to say this, but we have yet to have a failure. And I know this sounds pretty incredible, but so far, we have aced every single thing that was handed to us, so I don’t know what it feels. But usually, from my experience, where slight hiccups can happen is when we don’t have subject-matter experts from the customer’s side on the project, that’s usually where… Whether it’s challenging, we have to guess about technologies that we’re not experts in, and then sometimes there is a bit of mismatch, but usually, this can be rectified easily, they bring in the right person, and then it keeps going smoothly.

Yuval: Would that be the key advice to the customer for a successful project, make sure you have the SMEs on your side?

Yuval: Or are there other things you need a customer to prepare before they approach you?

Helmut: No, it’s absolutely important for the customers, first of all to have a clear vision of what they want to do. It’s not often the case. We can help you in some cases, but I think the most important thing is for the customer to come with the right people in the mix. They can be pulled straight off production into these conversations because then we know that we’re solving a problem that is relevant and not an academic problem that potentially might have no real impact on the business.

Yuval: Is there a particular vertical or a particular type of problem you see more often than others?

Helmut: Yes, absolutely. I think finance has given us a lot of signals. We have engaged with several companies in the financial industry, we worked in the past with Fidelity for example, we worked with Goldman Sachs and a couple of others that I can’t mention right now. We also have seen a lot of signals in healthcare and life sciences where there’s, of course, interest. For example, we once did a rather unusual project with a pharma company on the optimization of clinical trials, and it’s not a standard quantum project you would expect, but the interesting bit is that the clinical trials are actually the biggest cost of developing a drug. So for me, it’s a mystery. Why are we talking about the chemistry if we can basically attack a problem that is much, much larger in scope?

So those two, I think, are the strongest signals. We’re also seeing more and more manufacturing and scheduling problems, et cetera, et cetera. And I’m lumping air travel and all these others a little bit into manufacturing, logistics. We have signals in that area too, but I think finance has really been the front-runner of the companies that are investing in quantum computing.

Yuval: And from your perspective, how far is the quantum industry from being truly useful say to the finance area? When do we move from exploration into widespread production?

Helmut: For the finance area, it’s hard to predict. If I may pivot slightly, the first valuable application of quantum computers that don’t require thousands and thousands of error-corrected qubits, will be in the area of chemistry, material science. For example, with a QuEra device, we can do already some material simulation type workloads. And so I would say in the next, give or take… Don’t hold me responsible for this number. In the next five years or so, we’re going to see the first meaningful results, probably in the chemistry area. For finance, hard to predict. Very hard to predict.

Yuval: If you were controlling the work plan of the industry, and I’m sure Amazon has certainly influence, but if you were in control, what would you have vendors focus on that they’re not focusing on today?

Helmut: Focus on error correction to build high-quality fault-tolerant qubits. I think this is the one area where everybody has to focus, and this is also what we are focusing on at Amazon in our center for quantum computing. We are not just building hardware, we’re superconducting qubits. Why? Because of course, they’re well understood and we know how the fabrication works, but we’re also focusing very strongly on error correction very early on. And these two efforts are going hand-in-hand so that we can build a fault-tolerant machine. I think that’s really, really key that we do this.

Yuval: Last question on the technology, when you think about hybrid applications that use classical and quantum computers, do you run into latency issues on the circle or roundabout time between quantum and classical and back?

Helmut: Well, as of today, there are no real full-fledged data centers that house both types of hardware. And this is going to remain a challenge for the foreseeable future, especially because we are in a very nascent part of the technology. We haven’t even decided yet which qubits are going to be the ones that eventually will scale to the sizes needed, it could be ion traps, it could be superconducting, who knows? And so because of that, these machines are usually at a distance housed in other facilities, and that can cause latency problems, which is absolutely normal. But in the case of AWS, great efforts have gone into architecting our service to prevent these latency issues, especially now that we have launched these so-called managed jobs, hybrid managed jobs that allow you really to do this. For example, variational workloads in a much more efficient way than it was previously possible.

Yuval: And if I could ask you a hypothetical question, if you could have dinner with one of the quantum greats, dead or alive, who would that person be?

Helmut: Dead or alive? Huh, that’s a tough one. And I’m going to give you a very odd answer. I would love to have dinner with Paul Dirac. The main reason being, I love his books, they’re very concise, to the point, and there’s a certain elegance in the math that he did. The other one potentially would be Heisenberg, just because… I have to say, I was an undergrad at ETH in Zurich, where Wolfgang Pauli was, and there’s a fun story. We moved to a different building and I found an old wooden chair in the dumpster, they’re like, “Oh, this is the perfect chair.” So I pulled it out of the dumpster, put in my office, fast forward two months, and then suddenly an email goes out, “Has anybody seen this chair of Pauli?” It turns out that by accident, pulled it out of the dumpster and was sitting on his chair. So this is as close I’ve gotten to one of those big fellows.

Yuval: And thank you for the story. And I did hear Dirac as an answer before with one of my previous guests, and the caveat was that that person said that Dirac was not a great conversationalist. So it might be a difficult dinner, an awkward dinner, but maybe very valuable.

Helmut: So, in full disclosure, I love food, I love eating, I go to restaurants, write a lot of reviews online. And when I like to eat dinner, I like to focus also on the food. So he would be a perfect partner, just if you want to enjoy the meal and then maybe cross a few words.

Yuval: That’s perfect. Helmut, how can people learn about your work, maybe case studies or papers that you think could contribute to everyone’s knowledge?

Helmut: You can reach out directly to me or on AWS’s website, the quantum Solutions lab has its landing page. And if you’re interested in engaging in problems related to optimization, chemistry, and now the newest thing we have, the center for quantum networking, so we’re also engaging with customers on building quantum connections between devices. Just reach out, go to the website, send us a message, and we’d be happy to work with you.

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

Helmut: Thank you. It was a pleasure.

Yuval Boger is an executive working at the intersection of quantum technology and business. Known as the “Superposition Guy” as well as the original “Qubit Guy,” he can be reached on LinkedIn or at this email.