In The Arena by TechArena - Quantum Computing Comes of Age: How Fermilab Is Shaping the Future

Episode Date: January 29, 2026

Fermilab’s Silvia Zorzetti explains how quantum computing and sensing are evolving, where they outperform classical systems, and what’s next for the field.

Welcome to Tech Arena, featuring authentic discussions between tech's leading innovators and our host, Alison Klein. Now, let's step into the arena. Welcome in the arena. I'm Allison Klein. Today, we're here for a Data Insights episode, which means I'm with Janice Naurowski of Solidine. Welcome, Janice. How are you doing? Hi, Allison. I'm doing great. How are you? I'm fantastic. I am really excited for this episode. We are diving into the world of quantum computing. Why don't you tell me who you brought along with you today? So today we have Sylvia Zeretti from Fermi Labs, who is the principal engineer department head and PI at one of the top five DOE quantum centers. So we're really excited to have this opportunity to talk to Sylvia. Sylvia, welcome to the show.

Thank you for having me here. And I'm very excited to talk about my favorite topic, which is quantum computing. Sylvia, I know that we've talked before, and I think that you have been recognized as a quantum leader in the United States. Why don't you go ahead and share a little bit about your background and your role at Fermilab and the areas of scientific research and innovation your team is focusing on today? Yeah, so I sat from Fermilab. So Fermilab is the U.S. Practical Accelerator Laboratory. We are leader in what's called high energy physics, just behind me, at least my tourism. not very visible, but there is a particle accelerator, actually several particle accelerators,

and also a collider. So we have this long-standing history in the technology for particle accelerators, and relatively recently, but still in the past decade, we started to work towards quantum computing and sensing in the use of the technology that we have developed for particle accelerators and also using several high-energy physics problems as use cases for quantum computing and sensing. So actually, my background is really in a particular accelerator technology. I did my PhD thesis at CERN on a research project on a new collider that was called Click. And then in 2017, we started working on quantum computing here at Fermilab.

And I was very excited to join this new emerging technology. Excellent. So we dive right in, I guess, with that information, you were first on the tech arena, which was last year at Supercompute 24, and mind-blowing conversations around quantum. In your mind, where is quantum computing today and is it advancing at the rate that the headlines really suggest? So it is a very exciting time for quantum computing.

We start to see practical use of quantum computing. We start to see applications that can run on existing quantum computers. And also, like, sensing is a big thing. So for sensing, for example, most of the technology already exist. Again, those are tools that we have developed also here at Fermilab, and we are applying them into quantum sensing. So I think it's very exciting to be in this field right now, and I believe the next decade will be really revolutionary for quantum computing,

and we'll see more and more. One thing that I'm thinking about is that, you know, Fermilab is obviously known for supercolliders and different types of research that are related to the supercollecting. Now, quantum computing doesn't necessarily mean that it can do every type of calculation that scientists want. What types of calculations are you thinking that quantum is going to be a good fit for the work that Fermi Lab is doing? Yes, Alison, you say the right thing. So we know that quantum computing is very good for some application, not for everything.

So applications where we know that quantum computing is particularly effective is what we call quantum simulation for field theories, basically where we can approximate or when we can model the system as many oscillators that then those oscillators interact to each other. So we have these huge many body problems in which there are so many entities that we know how to describe them if they are alone. But then when they start to interact to each other, then becomes a very complex model. So we know that quantum computing is very good for those kind of applications. There are like two killers applications that we know.

that are the shore algorithm, the growers search. Also there, the community, and starting from those two outstanding scientists made great progress. And we know that we have a very strong theoretical background that quantum computing can be revolutionary for those applications. Now, the reason why we are interested, for example, in the shore algorithm for the factorization of prime numbers is for applications that are very close to us, like cryptography, for example. So cryptography is basically anywhere we put a password. like our bank account, right? So we have several codes and two or three factor authorization systems. So those are all algorithms that go around cryptography.

And so short algorithm really gives us a way to factorize prime numbers. There are very important for this cryptography, which means that we can have like systems that are very secure from that standpoint because they cannot be broken by, let's say, someone else who has a more advanced cryptography system that could be like another nation or, let's say, an alien, because here on the heart, we all have to comply with the quantum mechanics rules.

So now if we apply quantum mechanics to this very human problem, then we know that it cannot be broken. Interesting. Amazing. I mean, you just gave some really good examples of where quantum is going, but for audiences who don't have a lot of understanding who are really close to the field, Where would you say today we are in development of quantum computing technology just at large?

So, as I say, it's a very good time to be in the field. We see that there are major research institutions like the one I mean, like Fermilab. And also there is a great involvement from the Department of Energy in the creation of D5 Department of Energy quantum centers to keep pushing on this technology and create large infrastructures to support many use cases. And then we see that there are major industry leaders that they are building different quantum platforms. And each one of them has different roadmaps, but we should see a relatively large system in the next five to 10 years. Now, Sylvia, every conference I go to, I'm seeing more quantum computers on show floors. I'm seeing large industry investing more deeply in this space.

You put out a roadmap of when we're going to see that big quantum computer and that really is a game changer. Tell me what stands in the way between today's quantum technology and that vision of the future, technical or otherwise, that is gating the progress that we need to make meaningful quantum capabilities a reality. Yeah. So in those roadmaps for quantum computing, I think we can make a distinction between two main problems. One is what we call engineering problem. Basically, it's something where we know the solution. We need to put more research effort or we need to pursue the technology and make it more efficient. But it's something that we pretty much know how to do it. And then there are problems in which we need more research to the

fundamental level. And those are mainly between the interconnects. Like the interconnects are needed for scaling those quantum computing and every very, very large systems. And in particular, are interconnects with very low losses because you don't want to lose your quantum information on interconnecting multiple computers, right? You want to be able to preserve it. Now, this quantum information is a very weak signal. And so that's one of the main challenges. And then another problem that the community is really focusing on is the quantum error correction. So how to make algorithms that are robust to noise. And so there are some mitigation strategy on the hardware, but there is also some algorithmic tricks that we can play on the main algorithms.

Amazing. So with that, Sylvia, how is Fermilab contributing to moving the industry forward? And what strengths does that lab have that we were just talking about? How does it bring quantum science to life? Yeah, so we are mostly focusing on that we're conducting quantum computing. As I say, like what we do at Fermilab is particle accelerators. And we also make those accelerators. So, like, there are people that work on assembling those big machines, and those are based on superconductivity.

What we have really mastered in the past years is how to make the superconducting cavities more and more efficient. So in a particle accelerator, a superconducting cavity is what is the boost. We also call it kick to the particle beams to arrive to 99.7.7 speed of light. So those objects are very efficient. We studied them very well. We know all the sources of all possible sources of noise. So in 2017, then we started to study them also at the quantum level, which means very much lower energy. Much lower energy also means that there are other sources of losses that we did not.

And so we are also considering them. So basically what Fermilab is doing is on one side studying the materials for quantum computing, in benchmarking, understanding all different sources of loises, understanding how those materials interact to each other at the single photon level. Most of this is non-knowledge for higher temperatures and higher energy. And now we are basically bringing this down to the single photon level, a very low temperature. This is a very distinct effort from, let's say, industry roadmaps, right? Because we have, if you want, the luxury that we can focus on the basic science

and the basic understanding of those mechanisms. And then there is the knowledge transfer to the field. At the same time, we also find a way to transform, if you want, these cavities, the superconducting cavities, again, very efficient objects for quantum computing. And then we are using those cavities. We put a qubit inside, and we have 20 milliseconds of coherence, which is amazing.

And we know that the cavity is even able to make more, so we can have even more coherence. Now with this device, it can be added. a quantum computer, which is a bisonic architecture where we leverage very high Hilbert space. We can talk more about that if you are interested, but it's also a quantum memory. So it's a very efficient object in which if quantum information is trapped inside, it can live for a very long time. So those are our major contributions. And then there are others in terms of use cases, sensing facilities and so on. Wow. That's amazing. I'd like to hear more details

about the initiatives that are going on at Fermilab right now. Can you give some more details? Yeah, I just talk about the technology, so what we are doing with the materials and the devices, and then there are the use cases. So here we have a theory group that is working on the use cases, specifically on the quantum simulations for field theories, and also applications for particle detectors,

and more on the computing side. And then we also use pretty much the same objects as sensors. The thing of quantum computers, the reason why, for example, we have to cool them down to 10 mili-calbine to very low temperature is because they highly interact with the environment. Now, this is a problem for quantum computers because it introduced errors into the competition. But on the other hand, it is a very good future to have for sensors, right? Because that means that they are very sensitive to the environment. And so we are using them to detect dark matter, to detect dark photon, to detect. particles that are called axioms and are still like in the dark matter regime.

And also to detect study radiations, like the radiations like the gamma ray or other type of radiations. So again, like these are studies. Everything can be applied to both sides. So for the same thing, we can understand those sources of radiations and maybe understand them better. For the quantum computers, we can understand how those radiations interact with the quantum computers and now we can build quantum computers that are robots.

to them. So with that, how do you kind of see quantum computing intersecting with traditional high-performance computing and what types of problems might benefit from those hybrid approaches? Yeah, that's a very good question. And I think that for quantum computing, we need a hybrid approach. So hybrid approach is whether you need different quantum computing or sensing modalities. So you have different platforms that can be photonic, superconducting, eye on, some, and atoms that interact to each other because each one of them has their pros and cons, right?

So you can take the best of all eventually in a future hybrid quantum computer. Regarding the HPC, if you want, we can also consider this as modality for quantum computer. So HPC can play a great role in the quantum data centers. We know that we can use AI for operational purposes. Like those qubits need to be retuned and calibrate every few hours. basically, if we want to be precise. And also to analyze the data that are collected, either during the calibration or during algorithm.

And then HPC can also be used to scale up. This is not yet fully understood, but we are trying to understand if we can feed classical data into a quantum computer and keep the quantumness or apply some tricks to keep these quantumness. So that's a field. That's kind of evolving. I'm very excited about that.

But if we are able to find an advantage in analyzing the classical data with quantum computers, then I think we can also use HPC to scale up the quantum computers. As you're describing this, one thing that I'm thinking about is, what are the key milestones that you're looking forward to seeing from the quantum community that we know that we're moving from this early research into practical applications? I think the answer to that is that we already have applications, practical applications. So like we have practical application for both the theoretical modeling and the experimental implementations. So I think that's a great part.

Now, the part that we have to work on is to have applications that are generalized. So basically to have like native gates and we can so that we can generalize and we can run any application on any quantum computer. So I think that's where we have to work more. What we are doing basically is to make quantum computers that are error-free or to lower the number of errors in quantum computers while increasing the size of those quantum computers. And that's basically what's in the roadmap of prominent institutions and industry. Amazing. This has been a wonderful discussion. I think we could keep going.

But where can our listeners go to learn more? How can they reach out to get a deeper dive into some of the insights? you just spoke about. So we have several activities. Actually, also here at Fermilab. Last week, we hosted more than 600 people at the Fermilab Quantum Symposium, which also had the track for non-expert, like we call it Quantum 101, in which we were explaining quantum computers and also sensing and also had some industry tutorials on how to use tools for quantum

computer. So like one way is to look for those kind of events. And there are several not only at Fermilab. So there are the five quantum centers. Each one of them organize several activities for outreach and disseminations throughout the year. There are tools that are very available like tutorial and to use some specific language or computers. They are very available on the internet. And I think everyone can learn how to use quantum computers.

And there are several free tools for emulation. and people can also apply to a free time on the cloud to use quantum computers. So for students, instead, I think that you should look, there are some specialized programs to study, like specific curriculum on quantum computing, specifically at the master's. I believe the offer in this field is increasing. We will we see this from any universities. But also if someone is just pursuing a degree in physics or engineering,

I think that's a very good way to start. and they can participate to internships, they can talk to experts. We usually are very valuable. There are several opportunities in that field, too. Sylvia, thank you so much for being on the program today. You're working on such foundational and important technology, and it offers so much promise to advancement of the world. I'm so glad that you spent some time with us.

We can't wait to have you on again. Thank you so much for having me. And Janice, that wraps another episode of Data Insights. Thanks so much for the collaboration. Thank you, Alison. Thank you very much, Sylvia. It was a pleasure. Thanks for joining Tech Arena. Subscribe and engage at our website, Techorina.AI. All content is copyright by Tech Arena.