National Labs: Turning Basic Research into Industry Solutions
Overview
What do you do with 450 scientists in 15 labs across the country? You take on the country’s biggest energy challenges and bolster national security in the process. This leadership role is one of Bert de Jong’s jobs at the Berkeley Lab. He leads the Quantum Systems Accelerator which pairs advanced quantum prototypes with algorithms as part of the National Quantum Initiative. Bert is also the Department Head for Computational Science. He discusses the vital role of America’s national labs in translating basic research into industry solutions on the latest episode of The Quantum Spin.
00:00 Introduction to Quantum Spin Season Four
00:29 Interview with Bert De Young: Quantum Systems Accelerator
02:28 Bert De Young’s Research Journey
03:42 Quantum Computing and Energy Efficiency
06:48 The Role of Quantum Computing in Scientific Research
10:58 Open Source Software in Quantum Computing
12:22 Berkeley National Lab: Mission and Impact
18:53 Advice for Aspiring Physicists and STEM Students
20:40 Future of Quantum Computing and Current Projects
29:00 Conclusion and Closing Remarks
Bert de Jong is a senior scientist at Berkeley Lab, and currently serves as the Department Head for Computational Sciences, and the interim lead for the Applied Computing for Scientific Discovery Group. De Jong is the Director of the $25M/year National Quantum Initiative Quantum Systems Accelerator that involves 15 institutions and over 450 scientists. He is also the team director of the new large multi-institutional, 5 year MACH-Q project funded under the DOE ASCR Accelerated Research in Quantum Computing (ARQC) program, focused on advancing quantum algorithms and software integrated development environments. He is a co-PI on two science grants with quantum focused projects in DOE Basic Energy Sciences and DOE High-energy Physics. In addition to developing HPC applications in chemical sciences and his work in quantum computing, de Jong and his team are developing machine learning approaches for chemical sciences. He has a background in general chemistry, chemical engineering, high performance computing, quantum computing, AI, and quantum chemistry.
Transcript
[00:00:00] Veronica Combs: Hello, I’m Veronica Combs, and this is the Quantum Spin by HKA. For season four, we decided to do something a little different. In March, we attended the APS Global Physics Summit in Anaheim, California. We took advantage of this amazing event to talk to the leaders in academia, industry,
[00:00:18] as well as the creative folks who helped make the event such a compelling experience.
[00:00:23] I hope you enjoy these conversations that really reflect what’s happening in the industry right now. Today I am talking with Bert de Jong, who is the director of the Quantum Systems Accelerator. That is a program that is led by the Lawrence Berkeley National Lab and Sandia National Laboratories is the lead partner of the center.
[00:00:43] The team is dedicated to a mission of pairing advanced quantum prototypes with algorithms. So that sounds like a really big job, Bert, you must be a very busy person.
[00:00:53] Bert de Jong: Yeah, since I took that on a year ago my life definitely has changed around the team that is
[00:00:58] two national labs, one National Security lab, or defense lab actually, and 12 universities. They are about 450 people. So trying to understand all the research that they’re doing and funneling that in a way that gives us a cohesive story as a center to drive quantum information sciences forward as a field has been a pretty big task in the last year.
[00:01:22] Veronica Combs: So are your teams organized by modality or use case or how do you put 450 scientists into coherent groups?
[00:01:30] Bert de Jong: Yeah, so the center originally was centered around building fundamental building blocks, so fundamental qubits, scaling qubits, and then looking at algorithms and applications. But you quickly get to people thinking about that, not in the verticals, but in the horizontal and saying, we want to develop these kind of modalities.
[00:01:50] So we literally have research teams that are focused around atomic systems, focused around ionic systems, and around superconducting qubit systems, and they are then having a very big cross cut team that is really a co-design team focused on algorithms and applications. Our ultimate goal is to find a way to deliver scientific advantage or scientific capabilities to the Department of Energy to solve the real, major science problems that, that they are trying to solve as an organization, I would say.
[00:02:24] Veronica Combs: And what is your research specialty that brought you to this point in your career?
[00:02:28] Bert de Jong: Oh, my research history is a little checkered. I started as a chemical engineer, didn’t like the rules of thumb and went to get a chemical physics degree. Early on I got really an appreciation for computational chemistry and so I got into the HPC world.
[00:02:46] That’s how I got into the US as a postdoc at Pacific Northwest National Lab. I spent there almost 14 and a half years leading HPC teams, developing computational chemistry codes. Then when I moved to Berkeley Lab, one of the big interesting things that I started to think about is, okay, HPC, we’re now at exascale.
[00:03:06] So Oakridge and Argonne have now exascale machines, but how far can we push that? And if you really look at most of the computational science, or the computer science problems that we want to solve, they scale in an order that we need bigger computers every time. We still need to reduce the problems that we want to solve.
[00:03:26] We can never do it on the classical computers that they’re going to deliver in the near future. I started to look at alternative technologies. I started to work a lot about 10 years ago in quantum computing for that reason because that was a very promising field and there’s a lot of promising opportunities there.
[00:03:42] Veronica Combs: Because the power consumption of a quantum computer doesn’t scale like the power consumption of a classical computer. Is that the right way to think about it or no?
[00:03:51] Bert de Jong: It’s one angle to take. One interesting angle, of course, is power. Supercomputers, they take 30, 40 megawatts. Just imagine where you have to go with the next one. You have to have your own nuclear power plant effectively. Quantum computers have the potential to be more energy efficient, but it’s not just a question of energy efficiency.
[00:04:09] Quantum computers actually have an ability to store and harness information. That is in an exponential version for, so you go store bits on a classic computer. It’s a zero or one quantum computer. It’s anything between a zero and a one. And if you entangle them now you can store a lot more information.
[00:04:31] And so the amount of information you can store effectively for a qubit, which is two-level system, you can store two to the n number of qubits of information. And if you do that math very quickly, if you go to a hundred, which would be a hundred qubits or 500 qubits, 500 qubits, you probably get more particles than you have in the universe.
[00:04:52] So you just get an amount of information you can store a quantum computer that is small that you could never store on a classical computer. And as an example, I would say the big supercomputers that are out there right now, the two exascale machines, they can probably store the information of about 40 qubits in memory.
[00:05:12] That’s about what they can store, but we need to solve problems that require 500, a thousand of those qubits, so we could never even store that information. And that’s storing information, but the way we can operate on it is also a very different way on quantum computers that has the potential to be a lot more efficient.
[00:05:31] As often is talked about the exponential found advantage of quantum computers.
[00:05:35] Veronica Combs: Right. So you said you started in general chemistry and then you moved on to quantum chemistry. I keep having to remind myself of the difference between those two fields. What am I missing about the difference between those two?
[00:05:47] Bert de Jong: So I started as a chemical engineer, which meant I actually still have some roots in building plants, building factories, building pipelines, building reactors. But when you do that, there is a lot of rule of thumb. So there are assumptions that are based on the smaller scale, effectively, the molecular scale, the atom scale, or the collective atom scale.
[00:06:10] And I did not like the rule of thumb. I wanted to know why the rule of thumbs were invented. Okay, so I started to go into physical chemistry. Physical chemistry is looking at those kind of rules of thumb, what the roots are. And when I started to solve these problems and look at these problems, I quickly figured out I was not an experimentalist.
[00:06:33] I was more of a person that would be behind a computer. So I moved into the computational chemistry side, which effectively is trying to do virtual chemistry experiments. And that’s the goal with that field is trying to get to virtual experiments.
[00:06:48] Veronica Combs: And so simulation is always named as one of the earliest use cases for quantum computers.
[00:06:53] But I think that I’d like to get a little more detail into what that would that mean to be able to simulate an experiment or a chemical interaction better than we can now.
[00:07:02] Bert de Jong: So I can give you a couple of examples that are relevant also to our funding agency, the Department of Energy.
[00:07:09] What’s the mission of the Department of Energy is make our nation energy secure. One way to do that is to build better batteries, build better photovoltaics, so better solar cells effectively. But also a lot of energy is produced and used by the heavy industry that does a lot of chemical processes.
[00:07:32] So being able to figure out how to make these processes more energy efficient, allows us to gain energy. For example, look at a solar cell, okay, light is bouncing on a solar cell, it gets absorbed somehow, then it gets transported to where it needs to go. Understanding that process is very complicated, and we can use computational models to model those, but we can only approximate it because to get it accurately, the computational models become, again, exponentially hard and they are not solvable on a classical computer anymore.
[00:08:10] So that’s one example. As I said, a good example that people like to talk about a lot is one of the processes that is used in industry a lot is how to convert nitrogen and hydrogen into ammonia. Now that takes about couple of percent of the world’s energy right now.
[00:08:28] If we can harness that in a different way and be more energy efficient, then we should be able to do that and save that energy, which brings back energy security in a lot of ways. So that’s one example that we can think of. And there are biological processes that can do this without any energy or almost no energy.
[00:08:46] So we are trying to mimic that and understand it, and nobody has been able to really do that currently.
[00:08:51] Veronica Combs: My neighbor has solar panels, and so if we understood that conversion process better than maybe her solar panels could run twice as long as they do now, or is that the way to think about what the impact would be at that level?
[00:09:03] No,
[00:09:03] Bert de Jong: A solar cell is a good example. So about 20% of the light that hits the solar cell gets converted into electricity. 20% is not much. Right. Right. If you can do something that stores that, we can convert 80-90% of solar cell. Now, it’s not the question of you have to have more solar cells.
[00:09:26] You can have fewer solar cells because you can get the same amount of energy production. But on an industrial scale, just imagine all the solar fields that you see around the US nowadays. It allows you to shrink those or you can get more energy out of a smaller or a similar size field.
[00:09:41] Veronica Combs: Or a battery in an EV would last longer with different battery chemistry.
[00:09:45] Bert de Jong: Yes. So batteries is a very complicated chemical process that is not very fully understood. And that’s why the batteries right now have a limited range, but they also don’t last forever because the chemical processes that happen during the energy conversion in a battery actually degrade the battery.
[00:10:04] It’s never going back to where it was. Being able to model and understand these chemical processes, it’s extremely complicated and extremely hard on a current HPC kind of platform. We can take pieces, but then we have to figure out ways to connect all those pieces of information and figure out what’s going on.
[00:10:23] If we can use quantum computers to our advantage, we will be able to potentially model these systems at a proper scale and really fundamentally figure out what the better way is to operate a battery.
[00:10:35] Veronica Combs: Yes, I was reading about batteries for a client of ours and, really getting into the details of the chemistry, and I thought, wow, it is so much more complicated than anyone really appreciates, or at least the average user who just, buys their battery and, or, plugs their phone in and uses it.
[00:10:50] So that really sort of opened a window for me into the deep work that research groups like yours are doing too, to make them better. Yes. Do you have a point of view about open software, open source software, and how is that a good fit for the quantum industry? Or is it like classical software?
[00:11:05] Some things can be open source, some things shouldn’t.
[00:11:08] Bert de Jong: So I would say even in a classical world, a lot of the software is now becoming more and more open source. Sure, Sure. We have companies that are developing specific products that are tuned towards certain industries, but if you look, especially in the national lab system, in the academic system, a lot of software that’s being developed is open source.
[00:11:28] And this works in a classical world, honestly, the biggest operating system that is used nowadays is Linux, which is an open source software platform. So in quantum I would say open source is the way to go. And most companies have realized this too, like IBM, but their Qiskit is a good example of early on starting to develop software that is open source because they see where their proprietary angle is, and that is the hardware.
[00:11:53] And you see a lot of other companies have adopted that too. We have been developing open source software too, not under the Quantum System Accelerator as much, but I have other programs within the Department of Energy that are focused purely on developing things like compilers like error correction methodologies for quantum computing so that we can actually solve bigger problems faster.
[00:12:15] And that software BQSKit is the one that comes out of Berkeley Lab, is open source.
[00:12:20] Veronica Combs: Oh, BQSKit. That’s a good name. Yes. So tell me a little bit more about the Berkeley National Lab. It’s part of the National Lab System. What does that mean to be a national lab?
[00:12:30] Bert de Jong: Berkeley Lab is one of the 17 Department of Energy National Labs.
[00:12:34] There’s two sets of labs. Within that 17, there is a couple of them that are focused purely on national security. They’re called the National Security or NSA Labs. Those are Argonne, Livermore, Sandia, I forget one.
[00:12:49] Veronica Combs: Los Alamos.
[00:12:50] Bert de Jong: Yeah. Los Alamos. That’s the one I was thinking of.
[00:12:52] Yes. And the other ones are Office of Science Labs, and they have a very different mission. They’re really focused on more the fundamental science, basic energy sciences. The labs came out of the Manhattan Project so many decades ago. All of them were formed around that time.
[00:13:10] But their mission have changed because the Manhattan Project ended. So the labs were reformed to really build capabilities and user facilities to deliver large scale resources that could be used by the scientific community at large. So Berkeley Lab has one of those cornerstones that’s called the Advanced Light Source.
[00:13:33] So that’s a synchrotron light source. They have X-rays, soft x-rays so they can actually probe materials, biological systems, and chemical systems. But we also have some other capabilities like Molecular Foundry, which is fundamental material sciences. And of course, we do have a supercomputing capability, which is called NERSC.
[00:13:54] NERSC is very unique, relative to Oakridge and Argonne in that it’s not a leadership class system, but rather a mass production system for the Department of Energy scientists that have projects that are funded by the Department of Energy.
[00:14:09] So what is Berkeley Lab? So their mission is to bring science solutions to the world.
[00:14:15] And I would say that’s what resonates through that lab too: solving real-world scientific problems. We are very strongly built around team science. This is something that comes back from the Manhattan Project. We have anything that we do team sciences first, very little in individualism.
[00:14:37] That’s because we need to solve large problems and for that matter, quantum is also a large-scale problem. We have a lot of research focused in high energy physics and nuclear physics. We historically have a strong base there. We do a lot of work in chemical and material sciences from fundamental material science to catalysis to small atomic systems even.
[00:15:02] And then we have a large effort in biological sciences where a lot of focus is, how can we use the biological world to generate products that we need, that industry would need, or you would need in everyday life. Right. So those are the major areas that we have other efforts too that are more on the applied side.
[00:15:22] So the energy technology areas focus more on things like the grid. There is a lot of research and batteries there too. Water, kind of the key largest-scale problems. So I, if you really look at it, we have very fundamental research at the smaller scales, understanding the atom and the universe to looking at materials, chemistry and biology to actually starting to integrate all of that into layers that directly can be impacting society as a whole.
[00:15:50] Veronica Combs: What do the labs mean to the country?
[00:15:52] Bert de Jong: The labs have a very unique role within the scientific and engineering ecosystem. But I’ve always looked at it as you have academic researchers that do very fundamental research, so to speak that have very long time horizons, decades, multiple decades. The national labs are directly connecting to the universities and then doing research.
[00:16:17] That is basic research, but they’re translating that basic research into technologies that can be adopted by industry. And so if you look at it from a pie, the bottom layer, academia. Then you have the national labs, and then on top of that you have the industry that takes over the capabilities that are there.
[00:16:36] So we are really focused from basic to applied with a goal of translating fundamental scientific discoveries into capabilities and solutions that help us advance uh, the life of society in general on many levels.
[00:16:53] Veronica Combs: levels, not many levels, just academia, or not just industry, but the whole, the whole pie.
[00:16:57] Bert de Jong: They have a very large resource of effectively very smart and very capable researchers. So it’s not just the capabilities that they provide. So most of the labs have light sources, neutron sources and so on. Supercomputing capabilities and other facilities that are also a resource to the academic community, but they are working very closely with the academic community,
[00:17:22] and then find ways to translate it into real applications that can be taken, adopted and integrated into industry applications.
[00:17:31] Veronica Combs: So the federal government support is the infrastructure for the team science that you mentioned. Is that a good way to think about it?
[00:17:38] Bert de Jong: Well, I wouldn’t say that. So the instruments, the capabilities are that infrastructure. Okay. But we have a large amount of human expertise that is critical to actually help that translation from fundamental sciences to applied. And that to me is just as big a resource as the capabilities that are being provided
[00:18:02] by the national labs to the scientific community at large. Let me give you one example of how that actually played out. So during the pandemic there was a big need to figure out what the virus was, what it looked like, how we could find better ways to, to combat it.
[00:18:18] And so the national labs actually played a significant role in trying to understand the structure of the virus that was in front of us. And so how? Because we had the expertise to solve some of these problems together. The teams were formed with experts. We used unique capabilities, we used computing capabilities, we used the light sources, and we were able to give them a picture of what we’re actually looking for, and what we’re looking at when it comes to this virus.
[00:18:48] And that helped accelerate, I think how we got to vaccines.
[00:18:53] Veronica Combs: So we were talking during the APS Global Physics Summit, and there’s a lot of students here. There’s some poster sessions and mentoring. What advice would you give to young people thinking about studying physics in college
[00:19:04] Bert de Jong: in general, if you are interested in solving real problems, it’s not even just physics, chemistry, physics, biology. STEM research is what’s needed right now. I know it’s not the job where you can make millions of dollars, but it’s a very rewarding career. And what we see in quantum, for example, is there is an enormous shortage right now in the workforce.
[00:19:29] And by the way, it’s not just physicists anymore. Right now quantum is really quantum computing, quantum sensing, quantum networking is really moving away from needing physicists, just physicists or PhD physicists, I should say. They’re looking for people with bachelor’s, master’s degrees.
[00:19:49] But they’re also starting to look at people that are just associates degrees, because they’re getting to a level of technology
[00:19:57] that they now have to build many of them, build them at scale, build them, ship ’em to customers, provide software support, software infrastructures. That’s a completely different skill set than a physicist. So computer scientists and applied mathematicians, they are critical pieces now in the quantum computing and quantum sensing infrastructure.
[00:20:19] This is why early on I’ve been pulling in a lot of computer scientists and applied mathematicians and taught them enough of what it meant to do quantum so that they could actually take off and say, yeah, I have some ideas that come out of our community to actually move the field forward.
[00:20:37] Veronica Combs: Yeah. That collaboration is yeah irreplaceable. So I know there’s always the ongoing debate about when are we going to get to quantum advantage or when are we gonna have a real quote unquote breakthrough. But leaping ahead of all of that, what problem would you take on with a fully fault tolerant quantum computer if one were available at your fingertips today?
[00:20:55] Bert de Jong: So I’ll say that the type of problems that I would like to solve, I might not need a fully fault tolerant quantum computer. Oh, and that’s I think a key part to keep in mind when people talk about timelines. Sure. A fully fault tolerant quantum computer is going to take a long time. But when we start to talk about solving the DUE style scientific problems, the grand challenges that they have,
[00:21:20] we will not need a fully fault tolerant quantum computer. We need a quantum computer that is accurate enough to give us the answers that we need. Because even on the classical world, we do not need 12 digits. We need a couple of digits. That’s all we need for our scientific problems. If you do the math on that, we can handle some noise in a quantum compute.
[00:21:42] And actually we could potentially even take advantage of some of the noise and use that as part of our simulations to, to understand the world. And why would that be a way to think about it? It’s like nature by nature is noisy.
[00:21:56] Veronica Combs: Oh right, that’s true. Rain and rot and predators. Yeah. Everything is noisy.
[00:22:00] Bert de Jong: Yeah. Right. At the larger scale, at the smaller scale, everything is noisy. There is no perfect world that you try to simulate and that’s what a fully fault tolerant quantum computer would do. So what kind of problems could we solve? I would be really interested in fully simulating, for example, absorption of solar light by a biological system and see it transport that electron that it formed out of that solar light through its system to where it needs to go. Nobody has been able to do that, and those are very dynamical complex processes. Not static processes, not we need a number, but rather see how these systems evolve over time.
[00:22:46] And this is what quantum computers can be really good at. The other thing is, again, we don’t need fully fault-tolerant; we can handle quantum computers that are not digital. For example, we can deal with analog. Mm-hmm. because nature is also analog. The window I would say is a lot shorter even than with digital quantum computers.
[00:23:05] Veronica Combs: So in your example, to see how, like, how that solar energy, how a biologic system used that to, to sprout or to grow leaves or to do something like that, is that what you’re thinking of?
[00:23:19] Bert de Jong: I’m at the molecular scale, but yes. Ultimately, right when the energy goes into the biological system, it’s going to do something with it.
[00:23:27] Veronica Combs: Oh, you mean just that, that initial contact, ?That initial contact
[00:23:32] Bert de Jong: is already a very hard problem to understand, right? Huh? But those kind of problems sound like they’re a small bite at a very big apple, but they’re critical processes because if we understand how a biological system does this, we as humans have a tendency to try and replicate that in an industrial form.
[00:23:53] So if we can do it, nature can do it de efficiently and we figured out how they do it, we can do it de efficiently and maybe you can do it more,
[00:24:01] Veronica Combs: I think, is that called biomimicry or something like that? Yeah. You,
[00:24:05] Bert de Jong: you’re trying to mimic the biological function. Yeah. And this is really what a lot of processes are about because.
[00:24:13] Hey, everything chemistry is, has a close tie to biology so, right,
[00:24:17] Veronica Combs: right. And so when you say we can deal with some noise, are you thinking about hybrid systems? That’s like artificial intelligence, quantum computing, and high performance computing? Is that,
[00:24:26] Bert de Jong: oh, they’re going to go and get together one way or another?
[00:24:29] Let’s say we want to go to a quantum computer that is error corrected. Decoding what the errors were so that we can correct them can become very computationally expensive. So what we already see is people integrating smaller scale HPC resources into a quantum computer to solve some of these problems.
[00:24:50] But we’re dealing with very small quantum computers right now. Yes. Just imagine if we go to where people see things go, which is hundreds of thousands, millions, maybe tens of millions of qubits. Being able to actually decode the information at that point in time might require resources that are going to be larger.
[00:25:10] And it’s not just that has to be larger resources to decode. They have to do it fast. Right. Because qubits don’t live forever. Right. In real time, basically. You need to effectively do it in real time. Yeah. And that’s where an HBC comes in. What you see is actually that people are now starting to integrate AI for that same reason because again, classical computing can be very expensive.
[00:25:32] So could we use AI to accelerate this? And I think one of the last um, big papers that has come out of Google, for example, where they introduced a new Willow chip and actually did error correction and error detection and analysis of where the errors were. They were using AI. Right. So what I think we are seeing is a merging of all these technologies to really better the focus of we need to solve these kind of
[00:26:01] scientific engineering or industrial problems. We take the pieces of the technologies that we have and put ’em together in the best way to get to a solution.
[00:26:11] Veronica Combs: Are there any projects you’re keeping close track on at the Quantum Systems Accelerator? Anything you expect to announce this year or papers to be finished or anything we should keep an eye out for?
[00:26:20] Bert de Jong: We’ll have many more papers coming out. Our center, so far in the four and a half years, produced a little bit over 500 papers and we have a lot of papers in high-end journals like Nature Science. Mm-hmm. But I think there is a lot of interesting new stuff coming down the pike in all three fields, all fields actually.
[00:26:40] There’s some very novel things that are coming down the line when it comes to atomic systems. We have systems that are now capable of scaling. We are starting to look at error correction in those kinds of systems. We built a 200 ion trap, which is the first one in the world of that scale.
[00:26:58] That is now being put in use at Cornell and at Duke. So they’re loading in with ions right now. Wow. So that we can start to actually do experiments at that scale. That hasn’t been done before. So that would be a unique kind of capability that we expect to get some publications out in the near future too.
[00:27:16] We have now some interesting work on the superconducting qubit on arXiv coming actually out of Berkeley Lab. Mm-hmm. Where they build the first passive, what they call passive topologically protected or error internally corrected qubit. Yeah.
[00:27:32] They have done this in an active form where they have to make corrections. This one by nature is topologically protected. And there’s a lot of other efforts around that to actually make superconducting qubits better. Wow. And less noisy. ’cause that’s the game right now is really we’ve gotten to a level where we need to start finding the next level of uh, reducing noise.
[00:27:53] A lot of programs in Quantum System Accelerator actually have reduced noise in the systems by an order of magnitude and probably scaled the systems by one to two orders of magnitude. Wow. So we have made a lot of progress and we then have used those systems to actually do real science. We’ve done a lot of fundamental, many body physics, which means understanding some of the fundamental behaviors of materials.
[00:28:17] There’s a lot of work actually in high energy physics trying to see if we can actually model high-end physics processes with quantum computers. So there’s a lot of different angles that people are taking. And it’s just a question of can we get another order of magnitude less noise?
[00:28:33] Can we maybe get another magnitude of scaling and then solve even bigger problems that are going to be relevant to the Department of Energy.
[00:28:41] Veronica Combs: Right. Wow. Well You’re keeping those 450 scientists very busy, I can tell. Oh, they keep
[00:28:46] Bert de Jong: themselves busy. That’s the only good part. I don’t have to keep them busy.
[00:28:51] They keep me busy. Yes. Figuring out all the things that they’re doing and being able to communicate that to the community and also to our sponsors.
[00:29:00] Veronica Combs: Yes. Thank you so much for your time today. It’s great to hear about your work and see what’s coming down the pike and understand all the good work going on.
[00:29:06] So thank you so much for talking with us today.
[00:29:08] Bert de Jong: You’re welcome.
Host Veronica Combs is a quantum tech editor, writer and PR professional. She manages public relations for quantum computing and tech clients as an account manager with HKA Marketing Communications, the #1 agency in quantum tech PR. You can find them on X, formerly known as Twitter, @HKA_PR. Veronica joined HKA from TechRepublic, where she was a senior writer. She has covered technology, healthcare and business strategy for more than 10 years. If you’d like to be on the podcast yourself, you can reach her on LinkedIn, Veronica Combs, or you can go to the HKA website and share your suggestion via the Contact Us page.
June 20, 2025
