Blond & Quantum Episode 8: Bond & Quantum Episode 9: "We've always lived in the quantum age, we just didn't know it" | Bert de Jong Director of Quantum System Accelerator at Berkeley Lab

Dr. Bert de Jong, Director of the Quantum Systems Accelerator at Berkeley National Lab, joins Eva Galant on Blond & Quantum to pull back the curtain on the part of quantum most people never hear about — the national labs quietly driving the science before any startup ever touches it.

We treat quantum like it's coming. Dr. Bert de Jong says it's been here the whole time  every chip in your phone already runs on quantum mechanics. What's changed is that we can finally use it in new ways.

As Director of the Quantum Systems Accelerator at Berkeley National Lab, Bert leads one of the country's largest quantum efforts from inside a $1.5B, 4,200-person institution that lives somewhere between a university and a startup. In this conversation with Eva, he breaks down what that actually means — and where quantum is genuinely heading.

In this episode:
📍 What a US National Lab really is, and how Berkeley's 16+ Nobel legacy still shapes the science
📍 Why the lab deliberately backs neutral atoms, trapped ions, and superconducting qubits — and which Bert expects to arrive first 
📍 The near-term reality: industry-relevant simulations in 3–5 years, starting with chemistry, materials, and optimization 
📍 Why the story is bigger than computing — quantum sensing for GPS-free navigation, secure networking, and the race to stay quantum-safe 
📍 How Berkeley's incubator turns researchers into founders, and the moonshot keeping Bert up at night: zero-energy fresh water from the sea

A grounded, jargon-free look at what's real, what's hype, and what's three years away.

🎙️ Guest: Dr. Bert de Jong | Director, Quantum Systems Accelerator, Berkeley National Lab 🎧 Host: Eva | Blond & Quantum

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Transcript

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Hey, my name is Eva and this is Blond and Quantum, the podcast that breaks down quantum technology into real-world business impact. Here we make the complex simple. No equation, no overthinking, just insight, innovation, and a bit of humor. So don't worry, you don't need a PhD in physics to follow. In each episode, I talk to funders, scientists, and investors about how quantum is reshaping the industries today, not in some distant future. Oh, and if you hear a black cat boring in the background, that's my co-host, a very alive shorting cat joining the conversation. Her name is Moon. Let's get started. Welcome everyone to the next episode of Blown and Quantum. Today I have a pleasure to host uh Dr. Bert De Jong, Director of Quantum System Accelerator at Berkeley National Laboratory, called also for short Berkeley Lab. Bert, thank you so much for being here today with me. Thank you, Eva, for having me. Pleasure. So let's kick it off. My first question will be about the lab itself. So most people have never heard about National Laboratory. So please tell us a little bit more what the Berkeley Lab is and why we all should care. So let me start by giving you the whole broad spectrum. So the Department of Energy actually operates 17 national labs. So Berkeley Lab is one of the 17 that have some of them have many missions, some have very focused, narrow missions. But they're really about driving prosperity by addressing the key energy and environmental and nuclear challenges. And that we do through transformative science and technology solutions. And we're really, if you look at it as national labs, we operate kind of between a university and industry. So we are doing research that ranges from fundamental science all the way to, I would say, TRL levels, technical readiness levels that allow industry to take the IP, take the technology, and commercialize it. And so we do a very broad range of research, but it's all about helping the society move forward and focus on clean energy and all of those kinds of energy. That's obviously a beautiful mission. And as you said, the the the spectrum is pretty broad. But how this looks like in terms of the numbers? So you said 17 labs around, but in if we focus only on the Berkeley lab, um how many people do you have inside? Like what's the budget and um the the budget and what's the footprint? Yeah, so Berkeley Lab is one of those 17, and we have a very clean mission statement, and that is uh uh uh deliver solutions that advance science and improve lives. So uh our budget is about $1.5 billion. Um, that does not all come from the Office of Science. There is also um other parts of the government and also commercial um entities that fund some of that research. We're about 42, 4300 people, uh, and that is includes PhD scientists but also engineers uh and the support staff because we operate a lot of user facilities. So we have um one of our flagship user facilities is the advanced light source, uh where um uh users, and that means including industry, can take advantage of that light source and do experiments. Often on a uh if it's open research, on our is it it's free, otherwise it's on a cost recoverable basis. The other facility we have is the molecular foundry, where they're actually working very closely with industry to translate the fabrication of materials and and the analysis of them into products that can actually be used for uh society. And then the third user facility we have is the advanced um NERSC, which is our national uh computing resource, that also has a lot of uh industry partners actually utilizing that technology to do simulations that they have to do. So we're really a mix where we are focused on the science and the scientific discovery, which is what drives me. This is why I'm in the national labs, is because we have we think about larger problems as a team, and we bring that together with the expertise of the people, the experimental capabilities, and really drive the technology forward. And it changes also the way we do our research. And university can work on the same topic your whole career in national labs. The timelines for a lot of these developments are three to five years, sometimes a little longer. But it's that combination of large-scale problems, team science, and a slightly shorter window than you would have in academia that drives a lot of people to work in the national labs. That sounds uh really exciting, and it seems like I I hear a lot of fashion in your voice. So I actually wanted to ask you because you said, okay, this is what drives me. And the three to five years COP is also very very cool, actually. It's like the startup range as well, right? Um to solve the problem. And I understand when I come from because obviously the market and science is going very fast right now in many of those areas, so you can do, I think, research forever. But I wanted to ask you a little bit about your personal journey because, from what I understand, you uh complete a PhD in Netherlands, and now you lead the scientist, you manage budget, you chase the funding, and you still publish the research. Do you feel more a manager right now, or do you still feel at the hair that you are you're the lead scientist? That dynamic definitely has changed. I came to the to the US as a postdoctoral fellow, actually at Pacific Northwest National Labs. Um, and I was going to just be a postdoc and then decide what I wanted to do after that. And I liked the National Lab so much that I stayed at PL for a long time. Um and then at a certain point it was time for a change and uh Berkeley had opportunities and came to Berkeley. What I started to do early on is was, yeah, I had projects, I was doing science, I was writing the papers, I was writing the code. Um but over time I've evolved into actually doing bigger and bigger, managing bigger and bigger programs. The quantum system accelerator is uh the largest that I lead right now. I also lead uh efforts uh software-focused quantum programs and also some other programs that are related to QBI and DARPA on the matchmarketing initiative. So yeah, I've become more of a manager facilitator. I would not call myself a manager. I I'm looking at myself as a facilitator. Um I want to, this is how I operate in general. I hire the people, I'm bringing the people into my teams, multidisciplinary, multidisciplinary teams. I bring them together and I bring people together that are smarter than me because that's how we can move science forward. So I'm not considering myself a manager, I am an enabler, a facilitator that allows research to move forward and scientific discovery to flourish. And I still do a little bit of research on the side, but I now have a lot of people that work with me to do the research. That's a beautiful name, enabler. Um I like that. And I will I'll memorize this. And I hope later on we definitely can get a little bit deeper on those quantum quantum activities that you're doing in your lab. But before that, I would like to still zoom out and come back a little bit to the to the national lab itself because it's also a fascinating institution itself. And I have read that uh Berkeley Lab itself has 17 Nobel Prize winners. Is that correct? 16 or 17, yes. So we had two uh Nobel Prize winners. Actually, two? I think more than that. But uh we had two in the quantum world actually uh uh this year. Um, so yes, we have a lot of Nobel Prize winners. So I was about to ask you if you're able to mention all of them, but then maybe too much. But I wanted to ask you about the legacy. Is this still actively shaping uh how the science is done? Is this what brings the new people, the new wave of you know, generation of scientists to Berkeley Lab specifically? It's of course a great selling point, uh, in addition to the beautiful view we actually have over the bay. For those, yeah, so we we keep that legacy uh alive in different ways. Our supercomputers uh that our NERSC are actually named after Nobel Prize winners. The newest one is going to be called Dutna. The last one was Perlmuter, so Permuter for Casma. And all of the Nobel Prize winners get their own street named after them. So you uh will walk, drive through Dutna and Chew Road. So it's basically remind you every single day when you are on the facility that hey, here's the place when the history was made, and and it's still going on. And we have a as a core, so we are one of the first national labs. So national labs, in case people don't know that, the national labs came out of a very big mission during World War II where we were developing nuclear weapons. So the Manhattan Project. That's what most of the labs that are around were focused on. And we had a very clear big mission solving big problems. That is still alive here. These user facilities is a transition of these national labs to not just do the research but actually provide the resources, the advanced tools that we develop to enable the broader research community and also industry to advance the technologies. Yeah, that's that's that's beautiful. I read a little bit more about the history and um the Cold War, accelerate all of that. And I actually wanted to ask you if this is still relevant today, but I guess in terms of quantum, it is right, would you say it is? Uh can you clarify that question a little bit? Yes, so I was I was thinking obviously during Cold War, when there was the race of the technology, and today we see a similar race in the quantum industry. Clearly, US and China are racing here amongst others. Europe obviously trying to catch up, and Japan and everyone else. So, do you feel this spirit of competition during your everyday work? Because the technology, quantum technology has such a potential to be disruptive to national security. Yes, it impacts the way we do the research. This is not beer researchers, we just want to move the technology forward, we want to do move the science forward. So we would want to work with everybody. But we understand from a national security point of view that there is going to be restrictions on what can and cannot be done and how we move these things forward. I think that is an evolving thing. I would say friendly partners, there's lots of collaboration and cooperation. Because one of the things that people have to realize with quantum technologies in general is that this is not one person or one group, one company that, or one country that can solve these problems. We need the technology and we need the expertise from across the US and the world. Now, there is indeed some restrictions for certain countries, and what we see is actually all of these countries, China, Europe, US, we are all really moving the field forward. And we're still learning from each other because all of the research that's being done is published in open literature. Different approaches. China is really government funded. Uh um the US is, I would say, heavily VS uh VC funded uh these days, with a good, strong footprint by the government. I would say Europe is a lot of startups, but very heavily government uh funded at this point in time. So there are different ways to get to the same goal, I guess. Um you mentioned that um you mentioned about the structure of national labs as well, including uh Berkeley, which is a very unique organization. Um, because as you said, you're not university, you're not a company, you're not a government agency, you're something there between. And you are funded by the Department of Energy, but managed by University of California, right? So I wanted to ask you, Bert, here who's actually called the shots when needed, and if there is any tension that those different entities may create for you as a scientist and for your teams. So, yeah, um, national labs generally so they are owned by the Department of Energy. So, what that means is the equipment and the buildings effectively are owned by the Department of Energy, but they have a contractor operate the other national labs. That is that's the truth for 16 out of 17 labs. There's actually one that is actually operated and managed by uh by the Department of Energy. Berkeley Lab is a little unique in the in the 17 lab ecosystem. Um we literally have uh the University of California. Um I I can throw a rock out of my window and and probably hit somebody at UCE space. So it's a very different lab in the sense that it's so close to the academic community, and there's so many professors that are actually faculty scientists at the lab that it feels a lot more academic than some of the other national labs that uh that we have around. Um but does it so but yeah, so they have an operator. So University of California has operated Berkeley Lab since its inception. Um so it has been around for I don't even know the math, but it's like 70 years or so. Um 75, I think they might be reaching now. Um so they have been operating that, but they are that's what they are. They operate. Um, they get a small fee, but what they get as an opportunity is a first ride to to IP. And so that's how most of the contractors operate, is that they can get early access to the IP. But on the long run, who is driving our our research and our funding? That's easy. The person that gives us the money, and that is the Department of Energy. That's DOE. Yes. In the sense that they do dictate the agenda, or is there some space for your you as a scientist to explore your curiosity? Now that's I think the real difference between the uh a national lab and a university where a professor can just go off and go in any direction that they would like in a lot of ways. We are bound by uh the research that is going to be relevant to the DOE mission. Now, that is a very broad mission. And you can do fundamental science from materials. We are developing, we're actually trying to find ways to generate biofuels uh directly from cells, to we are building new types of uh facilities that can uh self that self-driving facilities that generate next generation surfaces that can be used to capture light or or other sources that could have a very broad application in the industry. Exactly. And I would love to right now to talk to a little bit more about it and your work on your work on the quantum, because obviously when people think DOE, Department of Energy, they think energy, right? But here you are, you work on quantum computing, there's a climate, there's a chemistry, there is a biology, and all of this happening under one roof of the Berkeley lab. So uh please tell us more about your day-to-day um research and your teams and what you're working on very closely. So I would say my large by far most of my research is all in quantum, although I should say that they also have a considerable portfolio uh in uh in AI. Um so we're we're doing research uh and developing um models uh via developing self-driving labs and infrastructure so that we can accelerate science. And really that's what drives me in the long run, is I want to accelerate scientific discovery. Um and I come from a world where I focused on HPC, so I was involved in Exascale for a long time. That when I came to Berkeley, I transitioned really to quantum and AI as alternative compute technologies to be able to simulate the systems that I need to understand to really build better batteries, build new kinds of uh solar cells, convert waste into useful products, and so on. And so my drive always has been, and this is what my day-to-day research really is focused on how can I take the tools, computational tools that I have, and deliver scientific discovery, get deliver new scientific insights together with our experimental partners that are here at the Berkeley Lab or other labs, actually. It's amazing. And then during my preparation for for the interview of you, I have read that you're also focusing right now. I'm not sure if it's still actual, so you tell me, that you are focusing on the quantum algorithm for a chemistry industry. Is that still relevant? Yes, so I'm a computational chemist by training. This is and also I have a chemical engineering background on top of that. So yes, chemistry is is very important to me. That's why I mentioned things like batteries. This is what drives me. Actually, there's a lot of things you can think of. I had a creative idea a long time ago that uh drove me to think about what it would take to, for example, generate fresh water out of ocean water. It's very energy intensive. Can we do this with um zero cost, zero energy, right? Big ambition. Big ambition. Yeah. But when you think through that, you realize there's a lot of fundamental science problems. Uh we need thermoelectrics that have a very high return and that can convert warmth heat, low, low uh gradient heat into usable electricity. We need batteries that can store way more energy so that we can do that process at night. But there's also a lot of products that are in the ocean. Uh, there is an enormous amount of lithium, but it's need for batteries in the future, that is actually in the ocean. There's actually also a lot of uranium in the ocean that uh one could think of recovering. So that's another problem to think about. How can we recover these materials out of the residual products that can come out of generating fresh water, the slurry that's left? Could we actually then recover something? And so a lot of these little topics have fundamental problems that need to be solved, to engineering problems that have to be solved. And that that gets me excited. It's like there's little pieces that have to be done that requires experimental work, that requires computational work, which is really what I do. That's where I then have been thinking and using uh quantum computers to really try to see if can we actually do some of these simulations faster with quantum computers than other computers, because if we can do them faster, we can again accelerate our scientific discovery portfolio. And can you tell us what exactly quantum computers are you using and what modality and maybe what products? Well, yes, we use uh any uh platform that we can get our hands on, to be honest. Um, so one of the things that we have in the the National Quantum Initiative Center that I lead, the quantum system accelerator, we actually have taken our approaches uh driven by our drive to accelerate scientific discovery and then being able to do real simulations on quantum computers. We have chosen a path of not choosing a technology. We don't know who's going to be the winner, and the reality is there might not be a winner. Uh different technologies might be useful for different platforms. So uh we are focusing on neutral atoms, trap tyons, or subconducting qubits. They are really, I would say, at the forefront at this point in time. Um, and some of these technologies, I would not uh have no doubt that they will be able to deliver a system uh in the next three to five years that can do simulations that I could not do on a classical computer anymore. And do you have your personal um preferences? Because I also am asking about it as a full disclosure because I noticed that you are a scientific advisor in uh atom computing, which is one of the leading American um companies building computers based on neutral atoms. Um is that showing your preference or um or means something else? No, that would not be a preference. Um what we have seen is um since the start of my center that I now lead, I did not lead it when the original center started. Atoms were far behind the other technologies, and they have drastically accelerated and my My gut feeling is that it will be an atomic technology that will get there first, so in the next three years. Now there is definitely a path for superconducting qubits, but it's a lot harder. The challenges that need to be addressed right now are a lot harder. Now you can because of errors. Now you can, of course, just do the simple thing and get more and more qubits and do error correction, but that requires scales of systems that become very expensive and very resource intensive. So I think there's challenges with all of these technologies. Atoms are slow, so we cannot do as many computations or operations as we could. So there is the balance, and I I think you see the use of these different technologies also depends on the type of problems we want to solve. We have a focus uh on more fundamental science problems, so Hamiltonian simulation as it's called, or better said, can we actually look at um the particles that make up the universe? So that's a problem that is very big in high-energy physics. We know that we can solve, we can use quantum computers in the near future to do problems we could not do in the classical computers anymore. And that is important for the discovery of the next generation of particles or the theory of everything, to be honest. Uh and the CERN just recently announced that they see potentially some non-standard model physics. Well, this is where we need simulations to be able to better understand and develop a better theory. And so those kind of that interplay of experiment and computation is where I think quantum can actually have a very big impact. I see. Coming back to from the CERN to more commercial usage of this research in this technology, you mentioned previously the drug discovery, I think you mentioned drug discovery at the battery technology. Um you mentioned the climate. I bet here you you mean the carbon capture. Can I ask you which of those um research at Berry Cray's lab are doing right now? Move the needle the most. And how how are you doing it? Yeah, so any problem that is biochemical, chemical, they are a very natural fit for quantum computers because fundamentally they are driven by quantum mechanics. So a quantum computer is driven by quantum mechanics, so it's a natural fit. So we are really focused on developing algorithms, scalable algorithms, and that allow us to actually simulate larger and larger systems on these kinds of quantum computers. So chemical sciences and very closely related material sciences are two areas that that we focus on a lot here at Berkeley Lab, in addition to high-energy physics. We have a small portfolio also thinking about how we could use quantum computers to do image recognition and and analyze image and data. So we're starting to get into the research that is focused more on how we can do effectively things like AI using quantum computers. And can you tell us a little bit more about the image recognition? This is very fascinating because that itself can have so many different applications. It can be used in um healthcare, right? It can be used in industry, robotics. So, which of these applications is there any specific vertical you're focusing on in image recognition, or just broadly thinking how it's very broadly. We're trying to understand so how can we actually encode an image and then extract some relevant and important information out of it? That is not a trivial task. It's not a field that has historically thought about how we can use a quantum computer. Yeah. And related to images, there is a different slight of slightly different type of image. Think of um genoming, uh genome sequencing, and they need to do matching and comparison. It's not an image in the per se sense, but it's it's similar in that vein. Is we're trying to take larger sets of data and actually get more information out of it with a quantum computer than we could potentially get out of it with a classical computer. No, you're right. This is very interesting, actually, the genomics, because the underlying stuff is is basically the same. And yep, image recognition is definitely not a trivial problem. So I'm very happy you're working on it. If it would, we would probably already have robots all around us, right? Um and there's a lot of company, also commercial companies who are trying to solve these problems all around the world. Okay. From the other hand, can you give us some specific example on any research done on Berkeley Lab in the past that is already maybe, you know, you already became a product or services that we as a people are using, but we are maybe not aware is coming from Berkeley Lab? That's a good question. I don't know specific examples of of uh of uh Berkeley Lab. I can say that there is a lot of startup companies that have come out of Berkeley Lab. So in security, insecurity in biosciences, in material sciences, they range from consulting companies to full startup companies. One of the things that Berkeley Lab started early on is that they actually have an incubator at the lab where they allow entrepreneurs to come in, they get a small, very small seat of funding, but they get access to the lab. Amazing. So this is called Berkeley Road. They renamed it now something, but that program has allowed a lot of companies to come through Berkeley Lab, and they have um I think the last what I saw is like some of these companies have brought together now 4.5 billion dollars in investments. So we have been seeding that, and that's part of I think also our mission. I can give you interesting examples, and that is uh from my time at Pieternal. So one of the things they have done there, if you go through the um airport right now and you get your uh luggage scanned, or you go to the body scanner, that's technology that was actually developed at they did it also for radiation monitoring and at ports. So a lot of that technology finds its roots for services or problems that are relevant to the Department of Energy, but then find a broader applicability. Of course. That's very interesting. But I wanted to uh ask you a little bit more about the Berkeley Road, uh, which is relevant. We have some startups on our audience. So I guess the question here would be if the startups that you attract through the accelerator are pre-seed or the seed level, or or maybe even they are directed to you know researcher itself, even before any company was established. So which stage are we talking here about it? And everyone is welcome, or we talk about quantum technologies here, or any application potentially of research? Yeah, there is a there is a broad range. If you look at if people are interested, uh Cyclotron Road, uh you can see the broad swat of companies that range from building new materials, new batteries, new chemical processes, to actually doing work in quantum technology. All of them, all of that range can be there. There is some focus generally on a yearly call, but uh these tend to be very, very early startups, so often pre-seed or about seed. And what we see is actually being part of this program allows them to actually uh often get some additional funding to to build their their company up. And that's beautiful. Everyone is welcome, or this is because of the nature of the lab, only dedicated to American companies? Uh that's a good question. I think it's probably more of the American companies. I should say that our lab partners with industrial companies outside of the US too. This too. Okay. And so I'm so sorry, but I have like so many questions, and this is all very exciting. So I wanted to ask you as well. So if any VC, venture capitalist people are listening to us right now, is there anything sitting in the lab or in the accelerator that is right now maybe massively undervalued and um you know it can ripe for commercial commercialization very soon? So one of the things that the lab does really well is it keeps a very strong IP portfolio. What we see in the Berkeley Lab ecosystem, which is University of California and Berkeley Lab, is we have a lot of researchers that advance technology and then realize they want to do a startup. Now, what's interesting, of course, is we're researchers, so we don't know really well how to build a business. And that's I think the biggest hurdle for us to engage actually VCs. But honestly, if VCs think there is, hey, there might be some interesting opportunities there, maybe we should have a conversation. I would honestly recommend they they reach out to our IPO office and say, are there potentials? Because they keep track of all the all of the opportunities, the potential companies that could come out, and they can actually serve as a bridge to bring the feces uh closer to potential startups uh that come out of Berkeley Lab. That's interesting. And out of curiosity, it's okay if you don't know, but this is like the details. I just wanted to ask you, because you you mentioned that the lab is really very protective with the IP, which should be, I guess. Um, how much percent of the equity is the lab keeping for themselves if they're um any researcher decide to do um spin-off technology? Because usually university, for example, they're keeping 10% um or around 10%. Is it the same for the lab, or is it I don't know. So this is the interesting angle here. So again, University of California operates the lab, and so they actually have access to most of that IP. So I don't know what the percentages are. I've not done a startup yet. We're working on one, but we haven't. We haven't gotten to that point that that we actually have to deal with those kind of things. And you mentioned that you also cooperate with different institutions or companies all around the world. So I wanted to follow up on this as well, if you can give us some names. But also if any commercial company, even American company, want to cooperate with the lab, are there any pathway for it? So, but we see a lot of companies that are interested in working and partnering with the lab because of the unique capabilities that we have, the user facilities, but also the expertise. So there's a lot of um, again, if it's open research, then it's it's all for free. If you want to do proprietary research, you can do that too with the national lab, but that becomes a cost recovery type uh uh research plan. So there will be some cost to the labs for that. And we have a large number of companies working with different entities and parts of the lab. I find one example is very interesting to me, is uh so the I mentioned the advanced light source. We actually have at the advanced light source an end station, as it's called, that uh takes some of the light coming from that light source, and it's called CXRO. That's one of those stations that actually was funded by companies. So it's the silicon companies, uh, the silicon industry that came together and put a facility there, paid for the facility, and it's being operated by Berkeley Lab. And they have access to unique capabilities to do analysis of their chips, their materials, and so on. Amazing. And in case of such a cooperation, the IP is also split it, or is it just uh more that is the contractor works that the commercial company is coming, they'd be putting some money on, and the lab's working on? There is a huge plethora of our options here. Again, if it's proprietary research, there's ways to to for the company to keep that IP. There is alternative approaches through what they call CRADAS, which allow us to actually lay out how that IP should be distributed. If it's open research, then it's open research. So I don't know how that would go with IP at that point in time. When a company wants to work with a national lab, they find a partner at the national lab, we will work out what the right model is to support the mission of both the lab and the company at that same time. Okay, I understand. And are there any areas for industry where you would not work on in terms of the research? Like, is there any like a line that is like, okay, above the line, not touching it? So one of the things that I would say we are not really focused on is anything that has to do with uh with uh defense type capabilities. So we are an Office of Science lab. There is some other labs that are focused more on national security that have slightly different missions that would be more amenable to that. So we see still a broad range of companies that are that could even um the in the uh stock market companies right now that could take full advantage of of the national labs. Just a couple of weeks ago I I spoke at ISIC, which is the silicon industry, really also reiterating and for them to realize that there's a lot of opportunities to take advantage of the capabilities that are here. We're here to advance science, we are here to advance advanced discovery and technology development, and we will integrate and partner with whoever is interested in working with us. Yeah, I understand. So this is basically a strategic decision to just cut off the defense and leave it for the other labs. It becomes a national security problem, and that changes the dynamic of the lab because uh there's a lot of uh yeah. We are open science, we focus on open science. Okay, that's the key point. In that case, if we focus on open science, do you think that national labs should be more chasing the commercialization of the research, or doing so it may kind of compromise the integrity of science itself? I do not believe that. No, if there is an advancement that comes out of the lab and that is something that could be commercializiable, it could impact uh uh our society in general, yes, we should commercialize it. And again, I want to reiterate Berkeley Lab and UC Berkeley are really at the forefront of doing that. If there is a way, if people see this as a potential product, they'll leave the lab or become part-time at the lab to start a new company. And we do that on a regular basis. So, what do you think today is like a bottleneck for those scientists to become entrepreneurs um and do more spin-outs um on the you know daily basis? It's like the funding problem, because that's hard to believe. It may be a culture, is a change of lifestyle. What is it? Oh, yeah, it's a change that would be a change of lifestyle. Uh you're you're up there, you're doing your research, now you have to manage a business, deal with all the other aspects. And so within the lab, you now have some programs that are focused on if you want to become an entrepreneur, these are things that you have to learn and understand. So I would say we're getting better at it, but it's it's an effort, and it's very easy to stick within the resources that you have in the lab to do your research, and it's a very change, big change in lifestyle and and the way you operate if you go and become a company. But there is enough people within our lab that have made that transition and started companies. And were probably very successful. Like any other startup, there is successes and failures, but yes. The ratio is is is is very uh brutal, I would say, but I bet again, that's not a that's uh just an assumption, not not really on the data, but I would believe that someone coming out of Berkeley Lab will have a much higher chance than, you know, I don't know, graduate student to be successful with the startup. Anyway, I would like to also ask you a hypothetical question completely. Like if you could redesign the relationship between the science, government, and the commercial industry or industry itself from scratch completely, what would you would you do? How this will looks like what can be improved? I think what we see right now with uh with the Department of Energy and the leadership there is they are trying to make a transition where they are eliminating bridges between the science side at the national labs and industry more and more. It would be good to see more smooth pipelines from the national lab research to industry. And yeah, that's I think is the biggest thing that we need to see happen. I think also it would be good for more and more industry to realize the enormous potential that these national labs have for them to do to advance their technology, advance their research, help them develop the next generation products. And there is a lot of programs that that support that. I've been involved in a program called the HBC for energy innovation, where we actually directly have to work with companies. So companies can pretty much pose a problem and say, can we solve this problem? Can you guys help us solve this problem? And so we get some small amount of funding to then support them to actually try and make an in-road in solving these problems. The greatest story that I've seen there, one of the great stories I've seen there, is um a whole bunch of companies that are making paper. We're trying to figure out how they can actually press paper and uh get the water out more efficiently. And so what it ended up being is a project where multiple companies, competitors, work together to put a proposal in. The lab, and especially our computing sciences area, developed new simulation capabilities that allowed them to actually design a better process, and that has come back to industry. So there is those partnerships that uh interestingly, it's not even competitors against that wanting to get ahead, but actually competitors that are working together because they see the power of the national apps. There's actually um something that really fascinated me in the business from day one, um, as being a European and looking at an American uh business ecosystem. Like the ability that the companies, competitors' companies are able to come together to achieve one goal as an industry. That was something that I think Europe is still need to learn. It's better than it was 10 or 15 years ago, but uh we're still not there in terms of this you know cooperation and and not treating yourself as a competitors. Okay, that's very interesting. So um I would like to also ask you a little bit more about the quantum and the future of quantum. So I have this tradition that's usually on the end of each interview, trying to pick my guest brains about the future of quantum itself. So we can compare this in the next five, ten years, maybe together. Uh, what work out well, what didn't. So the same here will be Bert for you. I would like to ask you what do you think will be the biggest breakthrough for quantum technologies in the next five years? Well, breakthroughs are all uh serendipitous. You cannot plan breakthroughs. I would say we've actually gone through a couple of pretty big revolutions in the last couple of years. Last year's or the last 12 months, revolution has been error correction. Being able to scale and demonstrate error correction, I would say the biggest revolution or the biggest advancement we will see is people being able to build systems of a scale and an accuracy so that they can actually do real simulations of industry-relevant processes or industry-relevant applications within the next three to five years. So I would expect within the next three years to actually see those first demonstrations where we can show that these quantum computers can do what they need to be doing for, again, for us scientific discovery for industry to open up the new ways of solving their applications, their problems. And if you can pick um specific industry, we which industry do you think they will adopt quantum first? Well, I know that a lot of companies are at least trying to keep abreast and and have some uh some uh um finger in the pond to make sure that they uh understand what they would have to do. But I would say the easiest one I can think of is the chemical materials and biosciences, simply because again, their problems are closer to what one would expect from a quantum technology. What we also see is a very big opportunity in what's called the optimization space. So logistics, all of those. Um we've seen already well. Well, D-Wave has shown that for a while, that they can actually do uh optimization really well. I would say the current technologies that are now online through quantum computing technologies have also shown that they can actually do better than what you could do in classical computers. And so the logistics world that could be anything, right? Traffic to uh airplanes to scheduling. That is, I think, another a second very big potential market. For the rest, it becomes a little bit more focused on when we get the algorithms and applications ready to run on quantum computers. I think uh I know there's a lot of effort in the financial companies to see if they can get a little bit of a competitive advantage over others. Uh JP Morgan Chase has a very big effort in in quantum, for example, which by the way also is tends to be a lot of focus on on optimization problems, right? The optimization and minimization problems. It's 100% in their interest to do that. Exactly. So I can see a win there, but they really need to combine it probably with secure networking at some point. So um there I would say those are the primary angles that I can see. Um and we're talking about quantum computing. There's other angles to think about when it comes to quantum sensing. There's sensors that are being built that are much smaller, more efficient, and more accurate. And so you could start thinking about sensing quantum sensors being potentially used for navigation without GPS. So there is a lot of other, and that's why I wanted to point that out. There is a lot of quantum beyond quantum computing too. Sensing and um also networking are going to become very important. Um a recent paper that came out of, well, there's a couple of recent works coming out of Google and a brand new startup uh or atomic, but they talk about being able to break quantum encryption or breaking the Bitcoin chain. So bit systems that are within reach in the next three to five years. So uh the Google story is really focused on companies you need to get uh quantum secure. Uh and that is definitely a true thing uh to think about. And that's then again back related to quantum networking and moving data in a secure way. Yes, there's that a lot going on, definitely, and you are 100% right. People usually when they think about quantum, they think about quantum computer and then looking for the mysterious growl, right? Uh waiting for the moment when we commercialize. But um, you said it yourself. Um, DOF is already proving that uh optimalization can happen. Uh, in fact, I was just reading recently their um the reports, and they claiming they already have like 135 clients, commercial clients. So they they go from live deployment for a while now, right? Um another thing that is you talk about GP Morgan. I spoke recently with a very interesting startup who's working on uh using quantum-alike algorithm for trading, and it seems that this is also spreading very fast here. You mentioned sensing uh recently, actually, today I just published uh uh episode of our our podcast with uh CTO of Alan Hamilton, Bill Vass. He said there is no future. Well, he said quantum sensing is not the future, quantum sensing as an industry is already here, it's existing, it's working. It's not uh speaking of the melody of the future. It's here already today. So there's a lot, a lot going on. Yeah, what I tell a lot of people uh um when I approach an audience uh that that is not really quantum aware, the first thing I will tell them is you know, we are already living in a quantum age. We have been living in a quantum age since quantum mechanics was invented. Actually, the fact that you can watch us or listen to us is partly driven because of quantum mechanics. So what we've seen in the last 10 years, 10, 15 years is really a change of can we use quantum in a new way? And that's where quantum computing, quantum networking, and and quantum sensing really have become new ways of using quantum technology in everyday life. Uh and that I think is a very big thing to think about. We've always lived in the quantum age. Every chip is driven by quantum mechanics. So uh yes. Quantum is already here. I like it. Quantum has been here for a long time. I love it. And with this accent, we probably should wrap it up. Bert, thank you so much for being here today with me and spending your uh morning, late morning, afternoon uh already. It was a pleasure. Thank you. I appreciate the time. Thank you. That was Blond and Quantum. Thank you for joining me on this journey through the quantum business frontier. If you like the episode, please review another podcast and help more people discover the quantum world without needing to untangle the theoretical physics. See you next time!