In this theCUBE Research interview, Paul Gillin of SiliconANGLE Media speaks with Laura Schulz of Argonne Leadership Computing Facility at Argonne National Laboratory about integrating quantum computing into high performance computing workflows. Schulz draws on a background in population genetics and leadership roles in supercomputing to discuss heterogeneous architectures, qubit modalities, software stacks and collaboration across laboratories and vendors. They examine early quantum accelerators and practical workflow integration for scientific users.
Schulz identifies major challenges including qubit stability, error correction and the need for software abstraction so domain scientists need not program at the qubit level. They emphasize integrating quantum, artificial intelligence and high performance computing workflows and developing common open reference stacks as a potential Linux moment for quantum to broaden access. Analysts underscore the role of user facilities and cross-industry collaboration in accelerating practical applications.
This interview provides insights for researchers engineers and technology leaders who evaluate quantum accelerators, quantum software and heterogeneous computing strategies. It highlights factors to consider for adoption including ecosystem standardization, vendor collaboration and user facility engagement.
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Laura Schulz, Argonne National Lab
In this theCUBE Research interview, Paul Gillin of SiliconANGLE Media speaks with Laura Schulz of Argonne Leadership Computing Facility at Argonne National Laboratory about integrating quantum computing into high performance computing workflows. Schulz draws on a background in population genetics and leadership roles in supercomputing to discuss heterogeneous architectures, qubit modalities, software stacks and collaboration across laboratories and vendors. They examine early quantum accelerators and practical workflow integration for scientific users.
Schulz identifies major challenges including qubit stability, error correction and the need for software abstraction so domain scientists need not program at the qubit level. They emphasize integrating quantum, artificial intelligence and high performance computing workflows and developing common open reference stacks as a potential Linux moment for quantum to broaden access. Analysts underscore the role of user facilities and cross-industry collaboration in accelerating practical applications.
This interview provides insights for researchers engineers and technology leaders who evaluate quantum accelerators, quantum software and heterogeneous computing strategies. It highlights factors to consider for adoption including ecosystem standardization, vendor collaboration and user facility engagement.
Project Lead for Innovation, Argonne Leadership Computing FacilityArgonne National Laboratory
Paul Gillin
Enterprise Editor & HostSiliconANGLE Media, Inc.
In this interview from HPE World Quantum Day 2026, Laura Schulz, project lead for quantum innovation at the Leadership Computing Facility at Argonne National Laboratory, joins theCUBE's Paul Gillin to discuss how quantum computing is transitioning from a physics experiment into a practical accelerator within the broader HPC and AI ecosystem. Schulz draws on her unconventional path — from population genetics to leading quantum systems at Argonne — to explain why hardware diversity among qubit types is both an opportunity and a persistent challenge. She breaks ...Read more
exploreKeep Exploring
What is the task of integrating quantum computing into Argonne National Laboratory's existing computing ecosystem, and how does quantum computing relate to previous paradigm shifts such as traditional CPU/parallel computing and the GPU/AI wave?add
How did you become involved in supercomputing and quantum computing, and what steps did you take to develop and integrate quantum computing capabilities at your institution?add
How is the current excitement around AI affecting work on quantum computing (yours and your associates’), and is it changing any priorities?add
Do you foresee a "Linux moment" for quantum computing — a common open-source system software stack that standardizes interfaces across different quantum hardware?add
How do you view the quantum computing community's collaboration and efforts to create a common computing environment and interoperable software stack, including the roles of user facilities, startups, and standards projects?add
>> This is theCUBE. I'm Paul Gillin. We're continuing our series of video interviews in celebration of World Quantum Day. That's an annual affair in which public awareness is raised about the understanding of quantum science and technology around the world. Quantum computing is moving ahead quickly into practical applications, and we're interviewing a number of customers of Hewlett Packard Enterprise's Cray Supercomputers who are also working with quantum computers and looking for the intersection points between them. My guest today is Laura Schulz. She is project lead for quantum innovation at the leadership computing facility at Argonne National Labs. Her focus is on future heterogeneous systems, integration, and workflows. And she has led multiple efforts into quantum accelerators, looking into quantum accelerators as part of the high performance computing ecosystem. Laura Schulz, welcome to theCUBE.
Laura Schulz
>> Thank you, and thank you for having me.
Paul Gillin
>> So what does a project lead for quantum innovation do?
Laura Schulz
>> So my task is really to look at the new sort of computing that is available with quantum computing in order to integrate it into the overall computing ecosystem that we have at Argonne National Lab. I mean, as you well know, we've had a big successful decades long storied history with modeling and simulation from traditional CPU and parallel computing. Then we have experienced a paradigm shift in the way that we can add more capability to our science, more output through the use of AI and machine learning. That's been a whole introduction of GPUs, that whole wave. And now quantum is coming in as a new way to compute for very specific workloads and to give us results that we have not been able to achieve before, the potential to do that. And so my job is really fantastic that I get to look at the entire landscape, that entire spread, and I get to see how quantum computing is going to come into that ecosystem and really be another way for our scientists to compute to get some results that they've not been able to achieve before.
Paul Gillin
>> Now, yours is not a conventional computing background. How did you get into quantum computing?
Laura Schulz
>> So actually I studied population genetics, molecular population genetics in school. I got to be one of those end users that was really at the cluster level, department cluster level of compute. So I really even never got to experience a lot of the big parallelization work that we do on some of these big systems, which is really impressive. But I was head of strategic development for supercomputing in Munich, where I was previously at. And I got to deal with all facets of supercomputing from new technologies that were coming in in the traditional compute, AI test-bed environments. And then when we saw that quantum computing was coming out of the physics labs and starting to emerge from the startups, we realized, that for very particular types of workloads, they were going to be interesting and potentially very useful for our research scientists, our domain users. And so we had to get ahead of the curve and figure out how this innovative, unique, quirky type of technology was going to be able to be folded in to the overall compute ecosystem in a way that our scientists could actually be able to use it for groundbreaking scientific results. So wrote a strategy plan, put that all together. And then we applied for funding, got way more funding than we thought we were. And then with that, we needed to stand up a whole department. And so I got the pleasure, the real pleasure, of standing up multiple quantum systems, building a software environment, bringing in a whole heterogeneous ecosystem of scientists and try to make all this all run. So it's been a really fantastic ride.
Paul Gillin
>> More funding than you anticipated. That's a lucky position to be in. You said you're standing up several quantum computers. So as we know, there are multiple quantum architectures. What do you find are some of the significant differences between them?
Laura Schulz
>> Yeah. So there's many, and one of the big takeaway points is that there's many, many different ways to make a qubit. You've got the synthetic qubits that come from superconducting. You've got the natural qubits that come from neutral atoms or ions. And the idea is that there's many, many different ways to make these qubits, and that also means that they have different behaviors. So they can sometimes hold their quantum state a bit longer. Some are a bit shorter. They're some of them are in lines, and so there's only so much swapping that you can do with them. Some of them are in grids. I kind of say that they have their personalities. And what's really interesting is that in the early stages of all of this, we had to look at those type of modalities or qubit types, and we had to figure out maybe what sort of applications might be better for some versus from others. This is still very much a ongoing exploration. And we've got a lot of work to do in that space still.
Paul Gillin
>> It seems that researchers are still grappling with some fundamental issues when it comes to quantum, such as the lifespan of qubits and error correction. I guess what are some of the biggest problems still that need to be worked out before quantum computing can really go mainstream?
Laura Schulz
>> And to be honest, there's still quite a bit. I mean, this is a very hard problem that we're facing. So we've got the stability of the systems. As I said, these qubits, they can achieve this quantum mechanical effect, this state, but they can only hold it for so long. So one thing that we want to try to do is have the overall stability of the qubits so that we can actually compute on them. When they drop out of their state, we lose the ability to compute on them. So we have to keep them stable. We also have to integrate them into an entire ecosystem that we can use with them. And this is non-trivial. Right now, the people who are using quantum computers are those who are intimately knowledgeable of how the actual qubit hardware operates. So as comparison, from the classical compute folks or the HPC environment, that would be like asking a biologist to program all the way down to assembly or already program all the way down to the bits. That's what we're actually asking the quantum computer users to do now. So what we want to try to do is develop an entire software ecosystem that builds in levels of abstraction for them so that they can remove themselves from the nuances of that quantum hardware. We also really, at the end of the day, we're going to have users that are going to want to know very intimately in how to work with the quantum systems and be those physicists that are really studying it as a quantum system. But we're going to have a whole lot of what I lovingly call the old dog HPC users that have these big science campaigns they've been working on forever. They really want to understand how to take their previous model and simulation based workflows. They want to figure out how they can offload some parts of their HPC codes to quantum, figure out how to get more accurate data than they've gotten before with the simulation of quantum systems. They can actually now put it on a quantum system and get that data, and then bring it back into the overall workflow of the simulation that they're working on. So we have to build that environment, and we don't want them to understand what a Hamiltonian is or an Ansatz. We want to be able to remove that vocabulary, that weird quantum thinking in this sense, and be able to really powerfully utilize this new accelerator type into their overall workflows to gain results.
Paul Gillin
>> It does have a language all its own. You talked about supercomputering, the coexistence of supercomputers and quantum computers. Can you give an example what kinds of workloads make more sense to offload from supercomputers to quantum computers, and what kind of workloads will probably never be offloaded?
Laura Schulz
>> Oh gosh, that's a really good question. So the ones that could be offloaded are going to be ones that are already really close to quantum itself. And by that, I mean that the beauty of quantum computers is that this is a scientific instrument. This was developed as a scientific experiment to allow researchers to study quantum mechanical effects. And currently, when we're trying to simulate or study quantum mechanic effects with HPC, with classical computers, we're having to build a simulation. So a simulation has, by its very nature, inexactitudes, abstractions, it's a simulation of that. Quantum computing is so powerful because we can compute and register results from a classical mechanical system itself to study quantum mechanic effects. And so that means when we're wanting to utilize quantum in these large scale HPC jobs, we can offload the quantum part of an overall workflow, get those results from the quantum system itself, put it back into the HPC workflow. And with that, we get a level of exactitude that we wouldn't be able to get in the same sense, or we would be able to do it, but with a level of HPC compute that really might not make a lot of sense. So the closer that we get, quantum chemistry, materials, these sort of things, some biological systems, some optimization, these sort of things would really be really the perfect sort of candidates for that sort of work. And that's the kind of stuff that we're working on right now. There's going to be definitely use cases and workflows that aren't going to make a lot of sense. And we're going to explore those as we go. But of course, you have to remember too that quantum computing is also rapidly evolving as it goes. So I almost don't want to say there's not going to be something that we're going to use it for because maybe that's just an artifact of the time that we're presently in. But for right now, things that are like quantum are going to be the candidates that we're going to want to use it for.
Paul Gillin
>> There's a lot of excitement about AI, of course, right now. How is that affecting the work you're doing with quantum and your associates are doing with quantum? Is that changing any of your priorities?
Laura Schulz
>> It's all integrated together. I mean, it truly is. AI is a method that we can use, just like the way that quantum is a particular type of compute method. AI is a compute method that's going to get us to our results. And so with that, we can use AI to get to some other quantum results. With several issue or initiatives that we have, we've got things like AI for quantum algorithms. We've got to explore. We have to find new algorithms, algorithms that make sense for us to utilize with the overall ecosystem. And for that, we're going to apply AI to help us in part of that discovery. That's really important. We can use really AI for a great many very interesting things. We can use it for discovering new algorithms in quantum. We can use it for optimizing quantum error correction, which is what we need when I mentioned before about stabilizing those qubits and keeping them in that quantum state longer. We can use AI to help us do that. We can use AI to also optimize the overall operation of the quantum system. And one thing that I'm really interested in is looking at how to use AI to optimize the overall operations of HPC, QC, and AI systems altogether. So it's really a very powerful compute method that we can use for many, many different things.
Paul Gillin
>> So you're saying you can use quantum to orchestrate different kinds of architectures than to solve bigger AI problems?
Laura Schulz
>> You can use AI to orchestrate HPC systems, AI systems, quantum systems, for example. You can utilize it to optimize the overall workflow. You can use AI to understand, to basically look at the sort of workflows that you're trying to run and figure out, based on models that you've already developed, where there would be better optimization points, where you could do it better, faster, more energy efficient. So the AI is this baseline, this fundamental technology that can be applied to so many different avenues that we're looking to explore that'll get us to better science overall.
Paul Gillin
>> What are the most exciting aspects of your work right now? What projects get you most excited?
Laura Schulz
>> Everything. I mean, at Argonne, I genuinely enjoy being back in the Department of Energy just because of the scale of the problems that we take on, the complexity, frankly, the hardness, and just the mission that we have to drive these things forward. It's really exciting. I think that we're at such an exciting point in compute. We're at exascale level computing now, which is just a whole new paradigm to get us to results that are just so large that we hadn't been able to do it before. And now we're shifting our problems and we're looking at it from how do you approach it from an AI perspective? How do you train models? How do you inference them? How do you put it all together? And now we're looking at quantum at the same time, and saying, hey, wait a minute, these problems that we've had, how can we retune our thinking a little bit and use quantum to them? And now we're looking at bringing it all together. So I think this is an incredibly exciting time in computing. And if you're not in this space, I'm like, what are you doing? I think it's such a great opportunity.
Paul Gillin
>> Do you foresee that off-the-shelf quantum computers will be available in the foreseeable future?
Laura Schulz
>> Probably not in... I always want to be positive. I think that we have a lot of work to do. I think that there are very small scale quantum computers that allow us to get acquainted with quantum computing, to understand what it looks like, but I think that it's probably going to be a while until we get to that point. Well, it's going to be a while, but eventually we'll probably get there.
Paul Gillin
>> You mentioned that skills are an issue, and essentially you're doing assembly level programming on these computers right now. What avenues do you foresee as resolving that skills issue or making those skills more available to you?
Laura Schulz
>> Yeah. Well, I mean, honestly, to be honest with you, I think saying that you code at assembly level for quantum is being generous. It's even lower than that. But we need to bring the level of the quantum... Excuse me. We want to not have folks have to code at the qubit. What we want to do, like we have done over decades in HPC, we have very diligently went from full stack HPC solutions where you had one architecture that had its system software, that had its application software, that had its user interfaces, and then you would have these individual stacked silos of HPC. We went through this whole process where we had to merge all of that together, where we had to bring cohesion into the ecosystem. And what's happening with quantum right now is that you get a quantum, a new technology, several different types of super conducting, but each technology has its own solution and it has the hardware, it has the control electronics, it has the system software, the application space, et cetera. And you have that same sort of siloing happening. And so I'm hoping that we're going to be smarter and faster with integrating quantum, all the quantum together itself, and then the quantum with HPC. So for that, we need the same things. We need to have that abstracted software layer where we remove a lot of the messiness of the hardware away from the users. And we have programming models and all that can go from one type of system to the next. We want to have that across quantum, but then even better, we eventually want to have where quantum systems can operate with AI systems, can operate with HPC systems, and you're using the same sort of programming models, the same sort of pattern and process so that you don't have to learn unique skills or unique information, unique coding for each of those individual types of machines. That's kind of counterproductive over the long haul.
Paul Gillin
>> Yeah. I'm struck by the analogy to the commercial computer market of the 1970s and '80s where you had each manufacturer had its own full stack, its own ecosystem, and none of them talked to each other. Then along came things like Linux that resolved that issue. Do you foresee that there will be kind of a Linux moment for quantum?
Laura Schulz
>> Oh, not only do I foresee it, I feel that I've been actively working on that. So I've been working for five, six years, before coming to Argonne, working on a system software stack with really, that's exactly kind of what you said, with that intention in mind to really try to provide a baseline reference, open source environment that had interfaces built into it so that other devices, other solutions can tap into that framework and still have the uniqueness that they want to have, but still be integrated into that baseline framework so that you could have this common environment where people can compute on. So yeah, that's been one of my passions. You've hit it exactly, the nail on the head.
Paul Gillin
>> what kind of collaboration is underway between your team? I know you work in Germany and their teams, we've spoken to Australia and Finland as part of this series. What kind of cooperation is going on between those teams?
Laura Schulz
>> Oh, well, I mean, just us all being part of the scientific community. And I think with quantum, just the newness and the emerging technology. All of this, people genuinely want to work together. That's one of the things I actually really enjoy about this community. Startup companies, they don't want to have to be responsible for figuring out their hardware, making their hardware competitive, making those qubits stable and all of that, and having to develop everything else on top of that. So they're looking to beg, borrow, and steal what's already been done. And I think that is fantastic. And it also helps them make more competitive. I wanted to mention, I think that the user facilities and the data centers and all, what I've noticed and experienced, they have a really fundamental role to play because, as a provider of resources, I want to make sure that the users have multiple different types of quantum technologies that they can utilize so they can test out for their science. However, that comes with a little bit of a cost because I also don't want to maintain all of those different stacks and all of those different solutions. And so with that, having a common framework that these systems can hang off of and the user can utilize the computing environment, the whole software environment, that's really powerful and that's really attractive to me as a center wanting to provide for those users. So with that, the companies have gotten on board, the integrators have gotten on board with that, and the community's working really well together. We also even have things like, we have this open QSE project that Oak Ridge is leading that we're a part of that's really working on figuring out the interfaces. We've got the entire European community that has been building a reference software environment. We've got all the companies that are building their software solutions. But what's really great is that I do see active worry and active concern for making sure that all of this works together. So I think that we're actually in a good place. I'm actually really positive and optimistic that we're going to have a common computing environment that at least doesn't conflict with each other, but that works with each other.
Paul Gillin
>> Very encouraging. Final question. You're in a new role as project lead for quantum innovation. If we were to come back to you a year from now, what would you like to be able to look back on and say that you accomplished in the past year?
Laura Schulz
>> Oh, that is a fantastic question. What would I want to accomplish? Gosh, well, I tend to be an overachiever who burns out quickly.
Paul Gillin
>> Clearly.
Laura Schulz
>> I would say quite a lot. I really want to see systems in place. I want to see our users working on them. I want to really get a deep understanding of what the users can do with it, maybe can't do with it, where they struggle. I always want just more information that we can build off of. I want to see the quantum software environment much more stable. I just want to see our users starting to work with it and just asking better, smarter, deeper, more complex questions that we can just continue to leverage and continue to build upon. So I don't think in a year we're going to be computing full throttle with quantum computing. We still have a ways to go, but it's always about the quality and complexity of questions that we can ask.
Paul Gillin
>> Laura Schulz, what a delight talking to you. Thanks for sharing some of your expertise. Looking forward to your accomplishments in your new role.
Laura Schulz
>> Thank you so much for having me. Really nice to meet you.
Paul Gillin
>> This is theCUBE. We are celebrating World Quantum Day, promoting public awareness and understanding of quantum science and technology around the world, worldquantumday.org. And thanks to Hewlett Packard Enterprise for making this series of interviews possible. I'm Paul Gillin. Stay with us.
