WEBVTT
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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.
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Here we make the complex simple.
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No equation, no overthinking, just insight, innovation, and a bit of humor.
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So don't worry, you don't need a PhD in physics to follow.
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In each episode, I talk to funders, scientists, and investors about how quantum is reshaping the industries today, not in some distant future.
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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.
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Her name is Moon.
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Let's get started.
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Welcome everyone to the next episode of Blown and Quantum.
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Today I have a pleasure to host uh Dr.
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Bert De Jong, Director of Quantum System Accelerator at Berkeley National Laboratory, called also for short Berkeley Lab.
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Bert, thank you so much for being here today with me.
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Thank you, Eva, for having me.
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Pleasure.
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So let's kick it off.
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My first question will be about the lab itself.
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So most people have never heard about National Laboratory.
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So please tell us a little bit more what the Berkeley Lab is and why we all should care.
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So let me start by giving you the whole broad spectrum.
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So the Department of Energy actually operates 17 national labs.
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So Berkeley Lab is one of the 17 that have some of them have many missions, some have very focused, narrow missions.
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But they're really about driving prosperity by addressing the key energy and environmental and nuclear challenges.
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And that we do through transformative science and technology solutions.
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And we're really, if you look at it as national labs, we operate kind of between a university and industry.
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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.
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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.
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That's obviously a beautiful mission.
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And as you said, the the the spectrum is pretty broad.
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But how this looks like in terms of the numbers?
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So you said 17 labs around, but in if we focus only on the Berkeley lab, um how many people do you have inside?
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Like what's the budget and um the the budget and what's the footprint?
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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.
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So uh our budget is about$1.5 billion.
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Um, that does not all come from the Office of Science.
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There is also um other parts of the government and also commercial um entities that fund some of that research.
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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.
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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.
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Often on a uh if it's open research, on our is it it's free, otherwise it's on a cost recoverable basis.
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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.
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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.
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So we're really a mix where we are focused on the science and the scientific discovery, which is what drives me.
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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.
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And it changes also the way we do our research.
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And university can work on the same topic your whole career in national labs.
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The timelines for a lot of these developments are three to five years, sometimes a little longer.
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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.
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That sounds uh really exciting, and it seems like I I hear a lot of fashion in your voice.
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So I actually wanted to ask you because you said, okay, this is what drives me.
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And the three to five years COP is also very very cool, actually.
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It's like the startup range as well, right?
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Um to solve the problem.
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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.
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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.
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Do you feel more a manager right now, or do you still feel at the hair that you are you're the lead scientist?
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That dynamic definitely has changed.
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I came to the to the US as a postdoctoral fellow, actually at Pacific Northwest National Labs.
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Um, and I was going to just be a postdoc and then decide what I wanted to do after that.
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And I liked the National Lab so much that I stayed at PL for a long time.
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Um and then at a certain point it was time for a change and uh Berkeley had opportunities and came to Berkeley.
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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.
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Um but over time I've evolved into actually doing bigger and bigger, managing bigger and bigger programs.
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The quantum system accelerator is uh the largest that I lead right now.
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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.
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So yeah, I've become more of a manager facilitator.
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I would not call myself a manager.
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I I'm looking at myself as a facilitator.
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Um I want to, this is how I operate in general.
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I hire the people, I'm bringing the people into my teams, multidisciplinary, multidisciplinary teams.
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I bring them together and I bring people together that are smarter than me because that's how we can move science forward.
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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.
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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.
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That's a beautiful name, enabler.
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Um I like that.
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And I will I'll memorize this.
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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.
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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.
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And I have read that uh Berkeley Lab itself has 17 Nobel Prize winners.
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Is that correct?
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16 or 17, yes.
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So we had two uh Nobel Prize winners.
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Actually, two?
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I think more than that.
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But uh we had two in the quantum world actually uh uh this year.
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Um, so yes, we have a lot of Nobel Prize winners.
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So I was about to ask you if you're able to mention all of them, but then maybe too much.
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But I wanted to ask you about the legacy.
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Is this still actively shaping uh how the science is done?
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Is this what brings the new people, the new wave of you know, generation of scientists to Berkeley Lab specifically?
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It's of course a great selling point, uh, in addition to the beautiful view we actually have over the bay.
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For those, yeah, so we we keep that legacy uh alive in different ways.
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Our supercomputers uh that our NERSC are actually named after Nobel Prize winners.
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The newest one is going to be called Dutna.
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The last one was Perlmuter, so Permuter for Casma.
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And all of the Nobel Prize winners get their own street named after them.
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So you uh will walk, drive through Dutna and Chew Road.
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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.
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And we have a as a core, so we are one of the first national labs.
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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.
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So the Manhattan Project.
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That's what most of the labs that are around were focused on.
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And we had a very clear big mission solving big problems.
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That is still alive here.
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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.
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Yeah, that's that's that's beautiful.
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I read a little bit more about the history and um the Cold War, accelerate all of that.
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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?
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Uh can you clarify that question a little bit?
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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.
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Clearly, US and China are racing here amongst others.
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Europe obviously trying to catch up, and Japan and everyone else.
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So, do you feel this spirit of competition during your everyday work?
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Because the technology, quantum technology has such a potential to be disruptive to national security.
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Yes, it impacts the way we do the research.
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This is not beer researchers, we just want to move the technology forward, we want to do move the science forward.
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So we would want to work with everybody.
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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.
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I think that is an evolving thing.
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I would say friendly partners, there's lots of collaboration and cooperation.
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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.
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We need the technology and we need the expertise from across the US and the world.
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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.
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And we're still learning from each other because all of the research that's being done is published in open literature.
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Different approaches.
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China is really government funded.
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Uh um the US is, I would say, heavily VS uh VC funded uh these days, with a good, strong footprint by the government.
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I would say Europe is a lot of startups, but very heavily government uh funded at this point in time.
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So there are different ways to get to the same goal, I guess.
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Um you mentioned that um you mentioned about the structure of national labs as well, including uh Berkeley, which is a very unique organization.
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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.
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And you are funded by the Department of Energy, but managed by University of California, right?
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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.
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So, yeah, um, national labs generally so they are owned by the Department of Energy.
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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.
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That is that's the truth for 16 out of 17 labs.
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There's actually one that is actually operated and managed by uh by the Department of Energy.
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Berkeley Lab is a little unique in the in the 17 lab ecosystem.
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Um we literally have uh the University of California.
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Um I I can throw a rock out of my window and and probably hit somebody at UCE space.
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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.
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Um but does it so but yeah, so they have an operator.
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So University of California has operated Berkeley Lab since its inception.
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Um so it has been around for I don't even know the math, but it's like 70 years or so.
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Um 75, I think they might be reaching now.
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Um so they have been operating that, but they are that's what they are.
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They operate.
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Um, they get a small fee, but what they get as an opportunity is a first ride to to IP.
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And so that's how most of the contractors operate, is that they can get early access to the IP.
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But on the long run, who is driving our our research and our funding?
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That's easy.
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The person that gives us the money, and that is the Department of Energy.
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That's DOE.
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Yes.
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In the sense that they do dictate the agenda, or is there some space for your you as a scientist to explore your curiosity?
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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.
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We are bound by uh the research that is going to be relevant to the DOE mission.
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Now, that is a very broad mission.
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And you can do fundamental science from materials.
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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.
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Exactly.
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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?
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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.
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So uh please tell us more about your day-to-day um research and your teams and what you're working on very closely.
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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.
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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.
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And really that's what drives me in the long run, is I want to accelerate scientific discovery.
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Um and I come from a world where I focused on HPC, so I was involved in Exascale for a long time.
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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.
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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.
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It's amazing.
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And then during my preparation for for the interview of you, I have read that you're also focusing right now.
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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.
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Is that still relevant?
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Yes, so I'm a computational chemist by training.
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This is and also I have a chemical engineering background on top of that.
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So yes, chemistry is is very important to me.
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That's why I mentioned things like batteries.
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This is what drives me.
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Actually, there's a lot of things you can think of.
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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.
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It's very energy intensive.
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Can we do this with um zero cost, zero energy, right?
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Big ambition.
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Big ambition.
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Yeah.
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But when you think through that, you realize there's a lot of fundamental science problems.
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Uh we need thermoelectrics that have a very high return and that can convert warmth heat, low, low uh gradient heat into usable electricity.
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We need batteries that can store way more energy so that we can do that process at night.
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But there's also a lot of products that are in the ocean.
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Uh, there is an enormous amount of lithium, but it's need for batteries in the future, that is actually in the ocean.
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There's actually also a lot of uranium in the ocean that uh one could think of recovering.
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So that's another problem to think about.
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How can we recover these materials out of the residual products that can come out of generating fresh water, the slurry that's left?
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Could we actually then recover something?
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And so a lot of these little topics have fundamental problems that need to be solved, to engineering problems that have to be solved.
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And that that gets me excited.
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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.
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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.
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And can you tell us what exactly quantum computers are you using and what modality and maybe what products?
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Well, yes, we use uh any uh platform that we can get our hands on, to be honest.
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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.
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We have chosen a path of not choosing a technology.
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We don't know who's going to be the winner, and the reality is there might not be a winner.
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Uh different technologies might be useful for different platforms.
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So uh we are focusing on neutral atoms, trap tyons, or subconducting qubits.
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They are really, I would say, at the forefront at this point in time.
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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.
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And do you have your personal um preferences?
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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.
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Um is that showing your preference or um or means something else?
00:21:41.279 --> 00:21:44.000
No, that would not be a preference.
00:21:44.160 --> 00:21:53.519
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.
00:21:53.680 --> 00:22:08.319
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.
00:22:08.480 --> 00:22:13.200
Now there is definitely a path for superconducting qubits, but it's a lot harder.
00:22:13.440 --> 00:22:16.960
The challenges that need to be addressed right now are a lot harder.
00:22:17.200 --> 00:22:19.440
Now you can because of errors.
00:22:19.599 --> 00:22:33.839
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.
00:22:34.000 --> 00:22:37.359
So I think there's challenges with all of these technologies.
00:22:37.599 --> 00:22:42.640
Atoms are slow, so we cannot do as many computations or operations as we could.
00:22:42.960 --> 00:22:52.079
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.
00:22:52.319 --> 00:23:04.799
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?
00:23:04.960 --> 00:23:08.880
So that's a problem that is very big in high-energy physics.
00:23:09.039 --> 00:23:17.200
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.
00:23:17.359 --> 00:23:24.960
And that is important for the discovery of the next generation of particles or the theory of everything, to be honest.
00:23:25.039 --> 00:23:33.759
Uh and the CERN just recently announced that they see potentially some non-standard model physics.
00:23:33.920 --> 00:23:40.400
Well, this is where we need simulations to be able to better understand and develop a better theory.
00:23:40.559 --> 00:23:48.480
And so those kind of that interplay of experiment and computation is where I think quantum can actually have a very big impact.
00:23:48.880 --> 00:23:49.359
I see.
00:23:49.599 --> 00:24:02.480
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.
00:24:02.559 --> 00:24:04.000
Um you mentioned the climate.
00:24:04.079 --> 00:24:06.720
I bet here you you mean the carbon capture.
00:24:06.880 --> 00:24:13.200
Can I ask you which of those um research at Berry Cray's lab are doing right now?
00:24:13.519 --> 00:24:15.119
Move the needle the most.
00:24:15.359 --> 00:24:17.519
And how how are you doing it?
00:24:18.240 --> 00:24:28.079
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.
00:24:28.319 --> 00:24:33.119
So a quantum computer is driven by quantum mechanics, so it's a natural fit.
00:24:33.279 --> 00:24:42.720
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.
00:24:43.039 --> 00:24:53.920
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.
00:24:54.160 --> 00:25:03.200
We have a small portfolio also thinking about how we could use quantum computers to do image recognition and and analyze image and data.
00:25:03.440 --> 00:25:13.519
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.
00:25:13.920 --> 00:25:16.799
And can you tell us a little bit more about the image recognition?
00:25:16.880 --> 00:25:20.880
This is very fascinating because that itself can have so many different applications.
00:25:20.960 --> 00:25:23.759
It can be used in um healthcare, right?
00:25:23.920 --> 00:25:27.680
It can be used in industry, robotics.
00:25:27.920 --> 00:25:37.200
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.
00:25:37.279 --> 00:25:45.519
We're trying to understand so how can we actually encode an image and then extract some relevant and important information out of it?
00:25:45.680 --> 00:25:47.279
That is not a trivial task.
00:25:48.480 --> 00:25:52.640
It's not a field that has historically thought about how we can use a quantum computer.
00:25:52.960 --> 00:25:53.359
Yeah.
00:25:53.599 --> 00:25:57.920
And related to images, there is a different slight of slightly different type of image.
00:25:58.079 --> 00:26:04.559
Think of um genoming, uh genome sequencing, and they need to do matching and comparison.
00:26:04.880 --> 00:26:09.839
It's not an image in the per se sense, but it's it's similar in that vein.
00:26:10.079 --> 00:26:20.079
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.
00:26:20.319 --> 00:26:20.960
No, you're right.
00:26:21.039 --> 00:26:26.240
This is very interesting, actually, the genomics, because the underlying stuff is is basically the same.
00:26:26.480 --> 00:26:30.160
And yep, image recognition is definitely not a trivial problem.
00:26:30.240 --> 00:26:31.839
So I'm very happy you're working on it.
00:26:32.000 --> 00:26:35.599
If it would, we would probably already have robots all around us, right?
00:26:35.759 --> 00:26:41.039
Um and there's a lot of company, also commercial companies who are trying to solve these problems all around the world.
00:26:41.279 --> 00:26:41.519
Okay.
00:26:41.680 --> 00:26:58.000
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?
00:26:59.920 --> 00:27:01.359
That's a good question.
00:27:01.519 --> 00:27:06.640
I don't know specific examples of of uh of uh Berkeley Lab.
00:27:06.960 --> 00:27:10.720
I can say that there is a lot of startup companies that have come out of Berkeley Lab.
00:27:11.039 --> 00:27:21.200
So in security, insecurity in biosciences, in material sciences, they range from consulting companies to full startup companies.
00:27:21.440 --> 00:27:36.240
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.
00:27:36.799 --> 00:27:37.279
Amazing.
00:27:37.440 --> 00:27:39.200
So this is called Berkeley Road.
00:27:39.440 --> 00:27:53.359
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.
00:27:53.519 --> 00:28:00.079
So we have been seeding that, and that's part of I think also our mission.
00:28:00.240 --> 00:28:05.119
I can give you interesting examples, and that is uh from my time at Pieternal.
00:28:05.359 --> 00:28:22.640
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.
00:28:22.799 --> 00:28:31.279
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.
00:28:31.680 --> 00:28:32.559
Of course.
00:28:33.200 --> 00:28:34.400
That's very interesting.
00:28:34.559 --> 00:28:39.440
But I wanted to uh ask you a little bit more about the Berkeley Road, uh, which is relevant.
00:28:39.599 --> 00:28:42.079
We have some startups on our audience.
00:28:42.319 --> 00:28:58.480
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.
00:28:58.640 --> 00:29:01.359
So which stage are we talking here about it?
00:29:01.759 --> 00:29:10.319
And everyone is welcome, or we talk about quantum technologies here, or any application potentially of research?
00:29:11.279 --> 00:29:13.519
Yeah, there is a there is a broad range.
00:29:13.680 --> 00:29:30.960
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.
00:29:31.119 --> 00:29:33.359
All of them, all of that range can be there.
00:29:33.519 --> 00:29:44.160
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.
00:29:44.559 --> 00:29:54.720
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.
00:29:55.119 --> 00:29:55.920
And that's beautiful.
00:29:56.079 --> 00:30:02.240
Everyone is welcome, or this is because of the nature of the lab, only dedicated to American companies?
00:30:02.559 --> 00:30:03.680
Uh that's a good question.
00:30:03.759 --> 00:30:06.880
I think it's probably more of the American companies.
00:30:07.039 --> 00:30:12.480
I should say that our lab partners with industrial companies outside of the US too.
00:30:12.799 --> 00:30:13.279
This too.
00:30:13.440 --> 00:30:13.599
Okay.
00:30:14.079 --> 00:30:18.799
And so I'm so sorry, but I have like so many questions, and this is all very exciting.
00:30:18.960 --> 00:30:20.319
So I wanted to ask you as well.
00:30:20.400 --> 00:30:39.599
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?
00:30:40.079 --> 00:30:46.079
So one of the things that the lab does really well is it keeps a very strong IP portfolio.
00:30:46.240 --> 00:30:57.440
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.
00:30:57.519 --> 00:31:02.720
Now, what's interesting, of course, is we're researchers, so we don't know really well how to build a business.
00:31:02.880 --> 00:31:07.440
And that's I think the biggest hurdle for us to engage actually VCs.
00:31:07.599 --> 00:31:16.240
But honestly, if VCs think there is, hey, there might be some interesting opportunities there, maybe we should have a conversation.
00:31:16.480 --> 00:31:24.400
I would honestly recommend they they reach out to our IPO office and say, are there potentials?
00:31:24.559 --> 00:31:37.680
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.
00:31:38.160 --> 00:31:38.960
That's interesting.
00:31:39.119 --> 00:31:43.359
And out of curiosity, it's okay if you don't know, but this is like the details.
00:31:43.440 --> 00:31:50.400
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.
00:31:50.559 --> 00:31:58.960
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?
00:31:59.039 --> 00:32:04.160
Because usually university, for example, they're keeping 10% um or around 10%.
00:32:04.559 --> 00:32:08.319
Is it the same for the lab, or is it I don't know.
00:32:08.960 --> 00:32:11.920
So this is the interesting angle here.
00:32:12.000 --> 00:32:19.519
So again, University of California operates the lab, and so they actually have access to most of that IP.
00:32:19.599 --> 00:32:21.920
So I don't know what the percentages are.
00:32:22.079 --> 00:32:23.839
I've not done a startup yet.
00:32:23.920 --> 00:32:25.519
We're working on one, but we haven't.
00:32:27.920 --> 00:32:32.640
We haven't gotten to that point that that we actually have to deal with those kind of things.
00:32:33.200 --> 00:32:38.240
And you mentioned that you also cooperate with different institutions or companies all around the world.
00:32:38.400 --> 00:32:42.240
So I wanted to follow up on this as well, if you can give us some names.
00:32:42.480 --> 00:32:50.799
But also if any commercial company, even American company, want to cooperate with the lab, are there any pathway for it?
00:32:51.440 --> 00:33:01.519
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.
00:33:01.680 --> 00:33:08.079
So there's a lot of um, again, if it's open research, then it's it's all for free.
00:33:08.240 --> 00:33:16.160
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.
00:33:16.319 --> 00:33:18.640
So there will be some cost to the labs for that.
00:33:18.880 --> 00:33:23.920
And we have a large number of companies working with different entities and parts of the lab.
00:33:24.079 --> 00:33:30.319
I find one example is very interesting to me, is uh so the I mentioned the advanced light source.
00:33:30.400 --> 00:33:39.920
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.
00:33:40.480 --> 00:33:44.799
That's one of those stations that actually was funded by companies.
00:33:44.960 --> 00:33:54.720
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.
00:33:54.960 --> 00:34:02.880
And they have access to unique capabilities to do analysis of their chips, their materials, and so on.
00:34:03.200 --> 00:34:03.680
Amazing.
00:34:03.839 --> 00:34:15.519
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?
00:34:16.079 --> 00:34:20.079
There is a huge plethora of our options here.
00:34:20.239 --> 00:34:26.239
Again, if it's proprietary research, there's ways to to for the company to keep that IP.
00:34:26.639 --> 00:34:34.719
There is alternative approaches through what they call CRADAS, which allow us to actually lay out how that IP should be distributed.
00:34:34.880 --> 00:34:37.119
If it's open research, then it's open research.
00:34:37.280 --> 00:34:40.800
So I don't know how that would go with IP at that point in time.
00:34:41.119 --> 00:34:54.400
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.
00:34:54.800 --> 00:34:55.760
Okay, I understand.
00:34:55.920 --> 00:35:02.639
And are there any areas for industry where you would not work on in terms of the research?
00:35:02.800 --> 00:35:08.079
Like, is there any like a line that is like, okay, above the line, not touching it?
00:35:08.800 --> 00:35:17.280
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.
00:35:17.440 --> 00:35:19.519
So we are an Office of Science lab.
00:35:19.599 --> 00:35:26.639
There is some other labs that are focused more on national security that have slightly different missions that would be more amenable to that.
00:35:26.800 --> 00:35:39.760
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.
00:35:40.000 --> 00:35:53.599
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.
00:35:53.760 --> 00:36:06.000
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.
00:36:06.320 --> 00:36:07.199
Yeah, I understand.
00:36:07.280 --> 00:36:12.400
So this is basically a strategic decision to just cut off the defense and leave it for the other labs.
00:36:12.719 --> 00:36:18.880
It becomes a national security problem, and that changes the dynamic of the lab because uh there's a lot of uh yeah.
00:36:19.119 --> 00:36:22.400
We are open science, we focus on open science.
00:36:22.480 --> 00:36:23.440
Okay, that's the key point.
00:36:23.840 --> 00:36:38.159
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?
00:36:38.480 --> 00:36:39.519
I do not believe that.
00:36:39.599 --> 00:36:51.280
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.
00:36:51.519 --> 00:36:59.679
And again, I want to reiterate Berkeley Lab and UC Berkeley are really at the forefront of doing that.
00:36:59.920 --> 00:37:07.679
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.
00:37:07.840 --> 00:37:09.760
And we do that on a regular basis.
00:37:10.000 --> 00:37:21.760
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?
00:37:21.920 --> 00:37:24.639
It's like the funding problem, because that's hard to believe.
00:37:24.800 --> 00:37:27.519
It may be a culture, is a change of lifestyle.
00:37:27.599 --> 00:37:28.400
What is it?
00:37:28.719 --> 00:37:31.599
Oh, yeah, it's a change that would be a change of lifestyle.
00:37:31.679 --> 00:37:38.239
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.
00:37:38.400 --> 00:37:48.000
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.
00:37:48.480 --> 00:38:06.239
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.
00:38:06.559 --> 00:38:13.119
But there is enough people within our lab that have made that transition and started companies.
00:38:13.360 --> 00:38:15.199
And were probably very successful.
00:38:15.840 --> 00:38:19.840
Like any other startup, there is successes and failures, but yes.
00:38:20.159 --> 00:38:39.119
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.
00:38:39.280 --> 00:38:44.639
Anyway, I would like to also ask you a hypothetical question completely.
00:38:44.800 --> 00:38:56.639
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?
00:38:56.880 --> 00:39:02.159
How this will looks like what can be improved?
00:39:02.559 --> 00:39:17.599
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.
00:39:17.840 --> 00:39:26.320
It would be good to see more smooth pipelines from the national lab research to industry.
00:39:26.719 --> 00:39:31.760
And yeah, that's I think is the biggest thing that we need to see happen.
00:39:32.000 --> 00:39:47.840
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.
00:39:48.079 --> 00:39:51.039
And there is a lot of programs that that support that.
00:39:51.519 --> 00:40:00.400
I've been involved in a program called the HBC for energy innovation, where we actually directly have to work with companies.
00:40:00.559 --> 00:40:06.079
So companies can pretty much pose a problem and say, can we solve this problem?
00:40:06.320 --> 00:40:08.400
Can you guys help us solve this problem?
00:40:08.639 --> 00:40:16.320
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.
00:40:16.559 --> 00:40:24.719
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.
00:40:24.880 --> 00:40:31.280
We're trying to figure out how they can actually press paper and uh get the water out more efficiently.
00:40:31.599 --> 00:40:40.960
And so what it ended up being is a project where multiple companies, competitors, work together to put a proposal in.
00:40:41.280 --> 00:40:51.679
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.
00:40:51.920 --> 00:41:05.199
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.
00:41:05.599 --> 00:41:15.280
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.
00:41:15.440 --> 00:41:22.960
Like the ability that the companies, competitors' companies are able to come together to achieve one goal as an industry.
00:41:23.199 --> 00:41:26.880
That was something that I think Europe is still need to learn.
00:41:27.119 --> 00:41:37.360
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.
00:41:37.599 --> 00:41:38.800
Okay, that's very interesting.
00:41:38.960 --> 00:41:46.000
So um I would like to also ask you a little bit more about the quantum and the future of quantum.
00:41:46.079 --> 00:41:57.039
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.
00:41:57.119 --> 00:42:01.679
So we can compare this in the next five, ten years, maybe together.
00:42:01.840 --> 00:42:05.039
Uh, what work out well, what didn't.
00:42:05.280 --> 00:42:07.440
So the same here will be Bert for you.
00:42:07.599 --> 00:42:15.519
I would like to ask you what do you think will be the biggest breakthrough for quantum technologies in the next five years?
00:42:15.920 --> 00:42:19.199
Well, breakthroughs are all uh serendipitous.
00:42:19.440 --> 00:42:21.199
You cannot plan breakthroughs.
00:42:21.440 --> 00:42:27.599
I would say we've actually gone through a couple of pretty big revolutions in the last couple of years.
00:42:27.840 --> 00:42:32.480
Last year's or the last 12 months, revolution has been error correction.
00:42:32.719 --> 00:42:57.199
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.
00:42:57.440 --> 00:43:16.960
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.
00:43:18.559 --> 00:43:25.840
And if you can pick um specific industry, we which industry do you think they will adopt quantum first?
00:43:26.159 --> 00:43:38.000
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.
00:43:38.159 --> 00:43:50.079
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.
00:43:50.239 --> 00:43:55.199
What we also see is a very big opportunity in what's called the optimization space.
00:43:55.360 --> 00:43:57.440
So logistics, all of those.
00:43:57.599 --> 00:43:59.760
Um we've seen already well.
00:44:00.000 --> 00:44:04.960
Well, D-Wave has shown that for a while, that they can actually do uh optimization really well.
00:44:05.199 --> 00:44:15.440
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.
00:44:15.599 --> 00:44:19.519
And so the logistics world that could be anything, right?
00:44:20.159 --> 00:44:23.599
Traffic to uh airplanes to scheduling.
00:44:23.760 --> 00:44:27.280
That is, I think, another a second very big potential market.
00:44:27.440 --> 00:44:34.880
For the rest, it becomes a little bit more focused on when we get the algorithms and applications ready to run on quantum computers.
00:44:35.039 --> 00:44:43.199
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.
00:44:43.280 --> 00:44:52.000
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?
00:44:52.079 --> 00:44:54.159
The optimization and minimization problems.
00:44:54.480 --> 00:44:56.880
It's 100% in their interest to do that.
00:44:57.280 --> 00:44:57.920
Exactly.
00:44:58.159 --> 00:45:05.599
So I can see a win there, but they really need to combine it probably with secure networking at some point.
00:45:05.760 --> 00:45:10.639
So um there I would say those are the primary angles that I can see.
00:45:10.800 --> 00:45:13.119
Um and we're talking about quantum computing.
00:45:13.199 --> 00:45:16.239
There's other angles to think about when it comes to quantum sensing.
00:45:16.320 --> 00:45:21.360
There's sensors that are being built that are much smaller, more efficient, and more accurate.
00:45:21.519 --> 00:45:29.679
And so you could start thinking about sensing quantum sensors being potentially used for navigation without GPS.
00:45:29.920 --> 00:45:35.360
So there is a lot of other, and that's why I wanted to point that out.
00:45:35.519 --> 00:45:39.440
There is a lot of quantum beyond quantum computing too.
00:45:39.679 --> 00:45:45.360
Sensing and um also networking are going to become very important.
00:45:45.519 --> 00:46:00.320
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.
00:46:00.559 --> 00:46:04.480
So bit systems that are within reach in the next three to five years.
00:46:05.199 --> 00:46:12.000
So uh the Google story is really focused on companies you need to get uh quantum secure.
00:46:12.079 --> 00:46:15.599
Uh and that is definitely a true thing uh to think about.
00:46:15.679 --> 00:46:21.039
And that's then again back related to quantum networking and moving data in a secure way.
00:46:21.440 --> 00:46:25.440
Yes, there's that a lot going on, definitely, and you are 100% right.
00:46:25.599 --> 00:46:33.280
People usually when they think about quantum, they think about quantum computer and then looking for the mysterious growl, right?
00:46:33.360 --> 00:46:35.920
Uh waiting for the moment when we commercialize.
00:46:36.000 --> 00:46:37.679
But um, you said it yourself.
00:46:37.760 --> 00:46:42.159
Um, DOF is already proving that uh optimalization can happen.
00:46:42.320 --> 00:46:50.880
Uh, in fact, I was just reading recently their um the reports, and they claiming they already have like 135 clients, commercial clients.
00:46:50.960 --> 00:46:55.360
So they they go from live deployment for a while now, right?
00:46:55.599 --> 00:46:58.320
Um another thing that is you talk about GP Morgan.
00:46:58.480 --> 00:47:08.000
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.
00:47:08.480 --> 00:47:18.159
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.
00:47:18.480 --> 00:47:20.880
He said there is no future.
00:47:21.039 --> 00:47:28.559
Well, he said quantum sensing is not the future, quantum sensing as an industry is already here, it's existing, it's working.
00:47:28.800 --> 00:47:31.679
It's not uh speaking of the melody of the future.
00:47:31.920 --> 00:47:33.280
It's here already today.
00:47:33.440 --> 00:47:35.760
So there's a lot, a lot going on.
00:47:36.079 --> 00:47:47.199
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.
00:47:47.440 --> 00:47:52.000
We have been living in a quantum age since quantum mechanics was invented.
00:47:52.239 --> 00:47:59.599
Actually, the fact that you can watch us or listen to us is partly driven because of quantum mechanics.
00:47:59.840 --> 00:48:07.119
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?
00:48:07.280 --> 00:48:17.360
And that's where quantum computing, quantum networking, and and quantum sensing really have become new ways of using quantum technology in everyday life.
00:48:17.440 --> 00:48:20.800
Uh and that I think is a very big thing to think about.
00:48:20.960 --> 00:48:22.719
We've always lived in the quantum age.
00:48:22.800 --> 00:48:25.840
Every chip is driven by quantum mechanics.
00:48:26.000 --> 00:48:27.360
So uh yes.
00:48:28.000 --> 00:48:29.280
Quantum is already here.
00:48:29.360 --> 00:48:29.840
I like it.
00:48:30.000 --> 00:48:31.760
Quantum has been here for a long time.
00:48:32.239 --> 00:48:32.719
I love it.
00:48:32.800 --> 00:48:35.519
And with this accent, we probably should wrap it up.
00:48:35.920 --> 00:48:43.440
Bert, thank you so much for being here today with me and spending your uh morning, late morning, afternoon uh already.
00:48:43.599 --> 00:48:44.480
It was a pleasure.
00:48:45.199 --> 00:48:45.760
Thank you.
00:48:45.920 --> 00:48:47.199
I appreciate the time.
00:48:47.599 --> 00:48:48.239
Thank you.
00:48:49.679 --> 00:48:51.840
That was Blond and Quantum.
00:48:52.000 --> 00:48:55.760
Thank you for joining me on this journey through the quantum business frontier.
00:48:55.920 --> 00:49:07.119
If you like the episode, please review another podcast and help more people discover the quantum world without needing to untangle the theoretical physics.
00:49:07.360 --> 00:49:08.559
See you next time!
00:00:00.719 --> 00:00:09.839
Hey, my name is Eva and this is Blond and Quantum, the podcast that breaks down quantum technology into real-world business impact.
00:00:10.240 --> 00:00:12.320
Here we make the complex simple.
00:00:12.480 --> 00:00:18.559
No equation, no overthinking, just insight, innovation, and a bit of humor.
00:00:18.800 --> 00:00:22.320
So don't worry, you don't need a PhD in physics to follow.
00:00:22.640 --> 00:00:32.159
In each episode, I talk to funders, scientists, and investors about how quantum is reshaping the industries today, not in some distant future.
00:00:32.479 --> 00:00:42.320
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.
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Her name is Moon.
00:00:44.479 --> 00:00:46.079
Let's get started.
00:00:48.479 --> 00:00:52.399
Welcome everyone to the next episode of Blown and Quantum.
00:00:52.560 --> 00:00:55.280
Today I have a pleasure to host uh Dr.
00:00:55.520 --> 00:01:04.560
Bert De Jong, Director of Quantum System Accelerator at Berkeley National Laboratory, called also for short Berkeley Lab.
00:01:04.959 --> 00:01:07.840
Bert, thank you so much for being here today with me.
00:01:08.719 --> 00:01:10.640
Thank you, Eva, for having me.
00:01:11.599 --> 00:01:12.159
Pleasure.
00:01:12.319 --> 00:01:14.079
So let's kick it off.
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My first question will be about the lab itself.
00:01:17.840 --> 00:01:21.519
So most people have never heard about National Laboratory.
00:01:21.680 --> 00:01:27.680
So please tell us a little bit more what the Berkeley Lab is and why we all should care.
00:01:28.400 --> 00:01:31.599
So let me start by giving you the whole broad spectrum.
00:01:31.680 --> 00:01:36.319
So the Department of Energy actually operates 17 national labs.
00:01:36.480 --> 00:01:45.200
So Berkeley Lab is one of the 17 that have some of them have many missions, some have very focused, narrow missions.
00:01:45.439 --> 00:01:52.640
But they're really about driving prosperity by addressing the key energy and environmental and nuclear challenges.
00:01:52.879 --> 00:01:57.280
And that we do through transformative science and technology solutions.
00:01:57.359 --> 00:02:05.760
And we're really, if you look at it as national labs, we operate kind of between a university and industry.
00:02:06.400 --> 00:02:24.000
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.
00:02:24.719 --> 00:02:35.199
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.
00:02:35.599 --> 00:02:37.039
That's obviously a beautiful mission.
00:02:37.120 --> 00:02:40.479
And as you said, the the the spectrum is pretty broad.
00:02:40.639 --> 00:02:42.960
But how this looks like in terms of the numbers?
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So you said 17 labs around, but in if we focus only on the Berkeley lab, um how many people do you have inside?
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Like what's the budget and um the the budget and what's the footprint?
00:02:55.439 --> 00:03:07.439
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.
00:03:07.680 --> 00:03:13.280
So uh our budget is about$1.5 billion.
00:03:14.080 --> 00:03:16.879
Um, that does not all come from the Office of Science.
00:03:16.960 --> 00:03:24.240
There is also um other parts of the government and also commercial um entities that fund some of that research.
00:03:24.400 --> 00:03:38.080
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.
00:03:38.240 --> 00:03:52.800
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.
00:03:52.960 --> 00:04:00.960
Often on a uh if it's open research, on our is it it's free, otherwise it's on a cost recoverable basis.
00:04:01.199 --> 00:04:17.360
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.
00:04:17.600 --> 00:04:31.920
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.
00:04:32.079 --> 00:04:38.480
So we're really a mix where we are focused on the science and the scientific discovery, which is what drives me.
00:04:38.639 --> 00:04:53.759
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.
00:04:54.319 --> 00:04:57.439
And it changes also the way we do our research.
00:04:57.680 --> 00:05:02.399
And university can work on the same topic your whole career in national labs.
00:05:02.639 --> 00:05:07.759
The timelines for a lot of these developments are three to five years, sometimes a little longer.
00:05:07.920 --> 00:05:20.160
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.
00:05:20.879 --> 00:05:26.560
That sounds uh really exciting, and it seems like I I hear a lot of fashion in your voice.
00:05:26.639 --> 00:05:30.800
So I actually wanted to ask you because you said, okay, this is what drives me.
00:05:30.959 --> 00:05:35.600
And the three to five years COP is also very very cool, actually.
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It's like the startup range as well, right?
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Um to solve the problem.
00:05:39.519 --> 00:05:50.399
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.
00:05:50.720 --> 00:06:05.279
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.
00:06:05.439 --> 00:06:11.519
Do you feel more a manager right now, or do you still feel at the hair that you are you're the lead scientist?
00:06:12.079 --> 00:06:14.160
That dynamic definitely has changed.
00:06:14.240 --> 00:06:19.920
I came to the to the US as a postdoctoral fellow, actually at Pacific Northwest National Labs.
00:06:20.079 --> 00:06:25.439
Um, and I was going to just be a postdoc and then decide what I wanted to do after that.
00:06:25.519 --> 00:06:30.480
And I liked the National Lab so much that I stayed at PL for a long time.
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Um and then at a certain point it was time for a change and uh Berkeley had opportunities and came to Berkeley.
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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.
00:06:44.879 --> 00:06:52.160
Um but over time I've evolved into actually doing bigger and bigger, managing bigger and bigger programs.
00:06:52.399 --> 00:06:58.160
The quantum system accelerator is uh the largest that I lead right now.
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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.
00:07:09.519 --> 00:07:13.759
So yeah, I've become more of a manager facilitator.
00:07:13.920 --> 00:07:15.759
I would not call myself a manager.
00:07:15.839 --> 00:07:18.639
I I'm looking at myself as a facilitator.
00:07:18.800 --> 00:07:21.920
Um I want to, this is how I operate in general.
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I hire the people, I'm bringing the people into my teams, multidisciplinary, multidisciplinary teams.
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I bring them together and I bring people together that are smarter than me because that's how we can move science forward.
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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.
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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.
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That's a beautiful name, enabler.
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Um I like that.
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And I will I'll memorize this.
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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.
00:08:03.920 --> 00:08:13.360
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.
00:08:13.519 --> 00:08:19.279
And I have read that uh Berkeley Lab itself has 17 Nobel Prize winners.
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Is that correct?
00:08:21.279 --> 00:08:22.639
16 or 17, yes.
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So we had two uh Nobel Prize winners.
00:08:25.839 --> 00:08:26.560
Actually, two?
00:08:26.720 --> 00:08:27.759
I think more than that.
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But uh we had two in the quantum world actually uh uh this year.
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Um, so yes, we have a lot of Nobel Prize winners.
00:08:35.679 --> 00:08:41.440
So I was about to ask you if you're able to mention all of them, but then maybe too much.
00:08:41.679 --> 00:08:44.000
But I wanted to ask you about the legacy.
00:08:44.159 --> 00:08:47.919
Is this still actively shaping uh how the science is done?
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Is this what brings the new people, the new wave of you know, generation of scientists to Berkeley Lab specifically?
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It's of course a great selling point, uh, in addition to the beautiful view we actually have over the bay.
00:09:02.639 --> 00:09:08.399
For those, yeah, so we we keep that legacy uh alive in different ways.
00:09:08.559 --> 00:09:13.519
Our supercomputers uh that our NERSC are actually named after Nobel Prize winners.
00:09:13.679 --> 00:09:16.240
The newest one is going to be called Dutna.
00:09:16.559 --> 00:09:20.000
The last one was Perlmuter, so Permuter for Casma.
00:09:20.159 --> 00:09:25.200
And all of the Nobel Prize winners get their own street named after them.
00:09:25.440 --> 00:09:29.759
So you uh will walk, drive through Dutna and Chew Road.
00:09:30.559 --> 00:09:40.799
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.
00:09:41.120 --> 00:09:46.080
And we have a as a core, so we are one of the first national labs.
00:09:46.159 --> 00:09:56.720
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.
00:09:56.879 --> 00:09:58.320
So the Manhattan Project.
00:09:58.480 --> 00:10:02.080
That's what most of the labs that are around were focused on.
00:10:02.240 --> 00:10:06.080
And we had a very clear big mission solving big problems.
00:10:06.399 --> 00:10:08.080
That is still alive here.
00:10:08.320 --> 00:10:24.080
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.
00:10:24.480 --> 00:10:26.559
Yeah, that's that's that's beautiful.
00:10:26.720 --> 00:10:32.799
I read a little bit more about the history and um the Cold War, accelerate all of that.
00:10:32.879 --> 00:10:41.200
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?
00:10:42.480 --> 00:10:45.840
Uh can you clarify that question a little bit?
00:10:46.720 --> 00:10:57.360
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.
00:10:58.159 --> 00:11:02.080
Clearly, US and China are racing here amongst others.
00:11:02.320 --> 00:11:05.759
Europe obviously trying to catch up, and Japan and everyone else.
00:11:05.919 --> 00:11:12.320
So, do you feel this spirit of competition during your everyday work?
00:11:13.919 --> 00:11:21.279
Because the technology, quantum technology has such a potential to be disruptive to national security.
00:11:21.440 --> 00:11:24.320
Yes, it impacts the way we do the research.
00:11:24.480 --> 00:11:29.679
This is not beer researchers, we just want to move the technology forward, we want to do move the science forward.
00:11:29.840 --> 00:11:31.679
So we would want to work with everybody.
00:11:31.919 --> 00:11:40.399
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.
00:11:40.639 --> 00:11:42.720
I think that is an evolving thing.
00:11:42.960 --> 00:11:47.440
I would say friendly partners, there's lots of collaboration and cooperation.
00:11:47.600 --> 00:11:59.120
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.
00:11:59.440 --> 00:12:06.240
We need the technology and we need the expertise from across the US and the world.
00:12:06.480 --> 00:12:18.879
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.
00:12:19.039 --> 00:12:25.200
And we're still learning from each other because all of the research that's being done is published in open literature.
00:12:25.759 --> 00:12:26.720
Different approaches.
00:12:26.879 --> 00:12:29.120
China is really government funded.
00:12:29.279 --> 00:12:40.879
Uh um the US is, I would say, heavily VS uh VC funded uh these days, with a good, strong footprint by the government.
00:12:41.039 --> 00:12:46.720
I would say Europe is a lot of startups, but very heavily government uh funded at this point in time.
00:12:46.960 --> 00:12:50.000
So there are different ways to get to the same goal, I guess.
00:12:50.320 --> 00:12:58.720
Um you mentioned that um you mentioned about the structure of national labs as well, including uh Berkeley, which is a very unique organization.
00:12:58.960 --> 00:13:06.080
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.
00:13:06.240 --> 00:13:13.519
And you are funded by the Department of Energy, but managed by University of California, right?
00:13:13.759 --> 00:13:27.279
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.
00:13:27.840 --> 00:13:34.879
So, yeah, um, national labs generally so they are owned by the Department of Energy.
00:13:35.120 --> 00:13:43.919
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.
00:13:44.080 --> 00:13:47.120
That is that's the truth for 16 out of 17 labs.
00:13:47.200 --> 00:13:52.000
There's actually one that is actually operated and managed by uh by the Department of Energy.
00:13:52.240 --> 00:13:56.559
Berkeley Lab is a little unique in the in the 17 lab ecosystem.
00:13:56.639 --> 00:13:59.840
Um we literally have uh the University of California.
00:13:59.919 --> 00:14:05.919
Um I I can throw a rock out of my window and and probably hit somebody at UCE space.
00:14:06.080 --> 00:14:21.519
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.
00:14:21.679 --> 00:14:26.559
Um but does it so but yeah, so they have an operator.
00:14:26.720 --> 00:14:32.000
So University of California has operated Berkeley Lab since its inception.
00:14:32.159 --> 00:14:37.840
Um so it has been around for I don't even know the math, but it's like 70 years or so.
00:14:38.000 --> 00:14:41.039
Um 75, I think they might be reaching now.
00:14:41.120 --> 00:14:44.559
Um so they have been operating that, but they are that's what they are.
00:14:44.720 --> 00:14:45.759
They operate.
00:14:45.919 --> 00:14:52.159
Um, they get a small fee, but what they get as an opportunity is a first ride to to IP.
00:14:52.240 --> 00:14:57.919
And so that's how most of the contractors operate, is that they can get early access to the IP.
00:14:58.080 --> 00:15:02.080
But on the long run, who is driving our our research and our funding?
00:15:02.159 --> 00:15:02.799
That's easy.
00:15:02.960 --> 00:15:05.600
The person that gives us the money, and that is the Department of Energy.
00:15:05.919 --> 00:15:06.960
That's DOE.
00:15:07.279 --> 00:15:07.519
Yes.
00:15:08.080 --> 00:15:17.600
In the sense that they do dictate the agenda, or is there some space for your you as a scientist to explore your curiosity?
00:15:18.720 --> 00:15:30.320
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.
00:15:30.639 --> 00:15:36.080
We are bound by uh the research that is going to be relevant to the DOE mission.
00:15:36.240 --> 00:15:38.480
Now, that is a very broad mission.
00:15:38.639 --> 00:15:42.000
And you can do fundamental science from materials.
00:15:42.159 --> 00:16:07.200
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.
00:16:07.519 --> 00:16:07.919
Exactly.
00:16:08.000 --> 00:16:18.799
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?
00:16:19.039 --> 00:16:28.240
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.
00:16:28.559 --> 00:16:36.559
So uh please tell us more about your day-to-day um research and your teams and what you're working on very closely.
00:16:37.360 --> 00:16:48.559
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.
00:16:48.799 --> 00:17:00.879
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.
00:17:01.039 --> 00:17:06.079
And really that's what drives me in the long run, is I want to accelerate scientific discovery.
00:17:06.240 --> 00:17:12.559
Um and I come from a world where I focused on HPC, so I was involved in Exascale for a long time.
00:17:12.799 --> 00:17:36.720
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.
00:17:36.960 --> 00:17:57.119
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.
00:17:57.440 --> 00:17:58.160
It's amazing.
00:17:58.319 --> 00:18:04.160
And then during my preparation for for the interview of you, I have read that you're also focusing right now.
00:18:04.240 --> 00:18:10.799
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.
00:18:10.960 --> 00:18:12.480
Is that still relevant?
00:18:12.799 --> 00:18:15.759
Yes, so I'm a computational chemist by training.
00:18:15.920 --> 00:18:19.599
This is and also I have a chemical engineering background on top of that.
00:18:19.680 --> 00:18:22.240
So yes, chemistry is is very important to me.
00:18:22.319 --> 00:18:24.559
That's why I mentioned things like batteries.
00:18:24.720 --> 00:18:25.839
This is what drives me.
00:18:26.000 --> 00:18:28.000
Actually, there's a lot of things you can think of.
00:18:28.240 --> 00:18:37.920
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.
00:18:38.079 --> 00:18:39.519
It's very energy intensive.
00:18:39.599 --> 00:18:44.079
Can we do this with um zero cost, zero energy, right?
00:18:44.400 --> 00:18:45.119
Big ambition.
00:18:45.359 --> 00:18:46.079
Big ambition.
00:18:46.240 --> 00:18:46.400
Yeah.
00:18:46.559 --> 00:18:50.480
But when you think through that, you realize there's a lot of fundamental science problems.
00:18:50.559 --> 00:19:02.880
Uh we need thermoelectrics that have a very high return and that can convert warmth heat, low, low uh gradient heat into usable electricity.
00:19:03.039 --> 00:19:08.400
We need batteries that can store way more energy so that we can do that process at night.
00:19:08.559 --> 00:19:11.039
But there's also a lot of products that are in the ocean.
00:19:11.119 --> 00:19:17.519
Uh, there is an enormous amount of lithium, but it's need for batteries in the future, that is actually in the ocean.
00:19:17.920 --> 00:19:22.559
There's actually also a lot of uranium in the ocean that uh one could think of recovering.
00:19:22.720 --> 00:19:24.880
So that's another problem to think about.
00:19:25.039 --> 00:19:34.640
How can we recover these materials out of the residual products that can come out of generating fresh water, the slurry that's left?
00:19:34.799 --> 00:19:36.720
Could we actually then recover something?
00:19:36.960 --> 00:19:46.799
And so a lot of these little topics have fundamental problems that need to be solved, to engineering problems that have to be solved.
00:19:46.880 --> 00:19:48.559
And that that gets me excited.
00:19:48.640 --> 00:19:55.759
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.
00:19:56.000 --> 00:20:12.319
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.
00:20:12.640 --> 00:20:19.200
And can you tell us what exactly quantum computers are you using and what modality and maybe what products?
00:20:19.599 --> 00:20:24.319
Well, yes, we use uh any uh platform that we can get our hands on, to be honest.
00:20:24.400 --> 00:20:40.240
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.
00:20:40.480 --> 00:20:44.160
We have chosen a path of not choosing a technology.
00:20:44.400 --> 00:20:48.880
We don't know who's going to be the winner, and the reality is there might not be a winner.
00:20:48.960 --> 00:20:51.920
Uh different technologies might be useful for different platforms.
00:20:52.160 --> 00:20:58.000
So uh we are focusing on neutral atoms, trap tyons, or subconducting qubits.
00:20:58.160 --> 00:21:01.680
They are really, I would say, at the forefront at this point in time.
00:21:01.839 --> 00:21:17.279
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.
00:21:17.599 --> 00:21:21.200
And do you have your personal um preferences?
00:21:21.359 --> 00:21:34.160
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.
00:21:34.799 --> 00:21:40.640
Um is that showing your preference or um or means something else?
00:21:41.279 --> 00:21:44.000
No, that would not be a preference.
00:21:44.160 --> 00:21:53.519
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.
00:21:53.680 --> 00:22:08.319
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.
00:22:08.480 --> 00:22:13.200
Now there is definitely a path for superconducting qubits, but it's a lot harder.
00:22:13.440 --> 00:22:16.960
The challenges that need to be addressed right now are a lot harder.
00:22:17.200 --> 00:22:19.440
Now you can because of errors.
00:22:19.599 --> 00:22:33.839
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.
00:22:34.000 --> 00:22:37.359
So I think there's challenges with all of these technologies.
00:22:37.599 --> 00:22:42.640
Atoms are slow, so we cannot do as many computations or operations as we could.
00:22:42.960 --> 00:22:52.079
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.
00:22:52.319 --> 00:23:04.799
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?
00:23:04.960 --> 00:23:08.880
So that's a problem that is very big in high-energy physics.
00:23:09.039 --> 00:23:17.200
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.
00:23:17.359 --> 00:23:24.960
And that is important for the discovery of the next generation of particles or the theory of everything, to be honest.
00:23:25.039 --> 00:23:33.759
Uh and the CERN just recently announced that they see potentially some non-standard model physics.
00:23:33.920 --> 00:23:40.400
Well, this is where we need simulations to be able to better understand and develop a better theory.
00:23:40.559 --> 00:23:48.480
And so those kind of that interplay of experiment and computation is where I think quantum can actually have a very big impact.
00:23:48.880 --> 00:23:49.359
I see.
00:23:49.599 --> 00:24:02.480
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.
00:24:02.559 --> 00:24:04.000
Um you mentioned the climate.
00:24:04.079 --> 00:24:06.720
I bet here you you mean the carbon capture.
00:24:06.880 --> 00:24:13.200
Can I ask you which of those um research at Berry Cray's lab are doing right now?
00:24:13.519 --> 00:24:15.119
Move the needle the most.
00:24:15.359 --> 00:24:17.519
And how how are you doing it?
00:24:18.240 --> 00:24:28.079
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.
00:24:28.319 --> 00:24:33.119
So a quantum computer is driven by quantum mechanics, so it's a natural fit.
00:24:33.279 --> 00:24:42.720
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.
00:24:43.039 --> 00:24:53.920
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.
00:24:54.160 --> 00:25:03.200
We have a small portfolio also thinking about how we could use quantum computers to do image recognition and and analyze image and data.
00:25:03.440 --> 00:25:13.519
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.
00:25:13.920 --> 00:25:16.799
And can you tell us a little bit more about the image recognition?
00:25:16.880 --> 00:25:20.880
This is very fascinating because that itself can have so many different applications.
00:25:20.960 --> 00:25:23.759
It can be used in um healthcare, right?
00:25:23.920 --> 00:25:27.680
It can be used in industry, robotics.
00:25:27.920 --> 00:25:37.200
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.
00:25:37.279 --> 00:25:45.519
We're trying to understand so how can we actually encode an image and then extract some relevant and important information out of it?
00:25:45.680 --> 00:25:47.279
That is not a trivial task.
00:25:48.480 --> 00:25:52.640
It's not a field that has historically thought about how we can use a quantum computer.
00:25:52.960 --> 00:25:53.359
Yeah.
00:25:53.599 --> 00:25:57.920
And related to images, there is a different slight of slightly different type of image.
00:25:58.079 --> 00:26:04.559
Think of um genoming, uh genome sequencing, and they need to do matching and comparison.
00:26:04.880 --> 00:26:09.839
It's not an image in the per se sense, but it's it's similar in that vein.
00:26:10.079 --> 00:26:20.079
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.
00:26:20.319 --> 00:26:20.960
No, you're right.
00:26:21.039 --> 00:26:26.240
This is very interesting, actually, the genomics, because the underlying stuff is is basically the same.
00:26:26.480 --> 00:26:30.160
And yep, image recognition is definitely not a trivial problem.
00:26:30.240 --> 00:26:31.839
So I'm very happy you're working on it.
00:26:32.000 --> 00:26:35.599
If it would, we would probably already have robots all around us, right?
00:26:35.759 --> 00:26:41.039
Um and there's a lot of company, also commercial companies who are trying to solve these problems all around the world.
00:26:41.279 --> 00:26:41.519
Okay.
00:26:41.680 --> 00:26:58.000
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?
00:26:59.920 --> 00:27:01.359
That's a good question.
00:27:01.519 --> 00:27:06.640
I don't know specific examples of of uh of uh Berkeley Lab.
00:27:06.960 --> 00:27:10.720
I can say that there is a lot of startup companies that have come out of Berkeley Lab.
00:27:11.039 --> 00:27:21.200
So in security, insecurity in biosciences, in material sciences, they range from consulting companies to full startup companies.
00:27:21.440 --> 00:27:36.240
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.
00:27:36.799 --> 00:27:37.279
Amazing.
00:27:37.440 --> 00:27:39.200
So this is called Berkeley Road.
00:27:39.440 --> 00:27:53.359
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.
00:27:53.519 --> 00:28:00.079
So we have been seeding that, and that's part of I think also our mission.
00:28:00.240 --> 00:28:05.119
I can give you interesting examples, and that is uh from my time at Pieternal.
00:28:05.359 --> 00:28:22.640
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.
00:28:22.799 --> 00:28:31.279
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.
00:28:31.680 --> 00:28:32.559
Of course.
00:28:33.200 --> 00:28:34.400
That's very interesting.
00:28:34.559 --> 00:28:39.440
But I wanted to uh ask you a little bit more about the Berkeley Road, uh, which is relevant.
00:28:39.599 --> 00:28:42.079
We have some startups on our audience.
00:28:42.319 --> 00:28:58.480
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.
00:28:58.640 --> 00:29:01.359
So which stage are we talking here about it?
00:29:01.759 --> 00:29:10.319
And everyone is welcome, or we talk about quantum technologies here, or any application potentially of research?
00:29:11.279 --> 00:29:13.519
Yeah, there is a there is a broad range.
00:29:13.680 --> 00:29:30.960
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.
00:29:31.119 --> 00:29:33.359
All of them, all of that range can be there.
00:29:33.519 --> 00:29:44.160
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.
00:29:44.559 --> 00:29:54.720
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.
00:29:55.119 --> 00:29:55.920
And that's beautiful.
00:29:56.079 --> 00:30:02.240
Everyone is welcome, or this is because of the nature of the lab, only dedicated to American companies?
00:30:02.559 --> 00:30:03.680
Uh that's a good question.
00:30:03.759 --> 00:30:06.880
I think it's probably more of the American companies.
00:30:07.039 --> 00:30:12.480
I should say that our lab partners with industrial companies outside of the US too.
00:30:12.799 --> 00:30:13.279
This too.
00:30:13.440 --> 00:30:13.599
Okay.
00:30:14.079 --> 00:30:18.799
And so I'm so sorry, but I have like so many questions, and this is all very exciting.
00:30:18.960 --> 00:30:20.319
So I wanted to ask you as well.
00:30:20.400 --> 00:30:39.599
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?
00:30:40.079 --> 00:30:46.079
So one of the things that the lab does really well is it keeps a very strong IP portfolio.
00:30:46.240 --> 00:30:57.440
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.
00:30:57.519 --> 00:31:02.720
Now, what's interesting, of course, is we're researchers, so we don't know really well how to build a business.
00:31:02.880 --> 00:31:07.440
And that's I think the biggest hurdle for us to engage actually VCs.
00:31:07.599 --> 00:31:16.240
But honestly, if VCs think there is, hey, there might be some interesting opportunities there, maybe we should have a conversation.
00:31:16.480 --> 00:31:24.400
I would honestly recommend they they reach out to our IPO office and say, are there potentials?
00:31:24.559 --> 00:31:37.680
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.
00:31:38.160 --> 00:31:38.960
That's interesting.
00:31:39.119 --> 00:31:43.359
And out of curiosity, it's okay if you don't know, but this is like the details.
00:31:43.440 --> 00:31:50.400
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.
00:31:50.559 --> 00:31:58.960
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?
00:31:59.039 --> 00:32:04.160
Because usually university, for example, they're keeping 10% um or around 10%.
00:32:04.559 --> 00:32:08.319
Is it the same for the lab, or is it I don't know.
00:32:08.960 --> 00:32:11.920
So this is the interesting angle here.
00:32:12.000 --> 00:32:19.519
So again, University of California operates the lab, and so they actually have access to most of that IP.
00:32:19.599 --> 00:32:21.920
So I don't know what the percentages are.
00:32:22.079 --> 00:32:23.839
I've not done a startup yet.
00:32:23.920 --> 00:32:25.519
We're working on one, but we haven't.
00:32:27.920 --> 00:32:32.640
We haven't gotten to that point that that we actually have to deal with those kind of things.
00:32:33.200 --> 00:32:38.240
And you mentioned that you also cooperate with different institutions or companies all around the world.
00:32:38.400 --> 00:32:42.240
So I wanted to follow up on this as well, if you can give us some names.
00:32:42.480 --> 00:32:50.799
But also if any commercial company, even American company, want to cooperate with the lab, are there any pathway for it?
00:32:51.440 --> 00:33:01.519
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.
00:33:01.680 --> 00:33:08.079
So there's a lot of um, again, if it's open research, then it's it's all for free.
00:33:08.240 --> 00:33:16.160
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.
00:33:16.319 --> 00:33:18.640
So there will be some cost to the labs for that.
00:33:18.880 --> 00:33:23.920
And we have a large number of companies working with different entities and parts of the lab.
00:33:24.079 --> 00:33:30.319
I find one example is very interesting to me, is uh so the I mentioned the advanced light source.
00:33:30.400 --> 00:33:39.920
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.
00:33:40.480 --> 00:33:44.799
That's one of those stations that actually was funded by companies.
00:33:44.960 --> 00:33:54.720
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.
00:33:54.960 --> 00:34:02.880
And they have access to unique capabilities to do analysis of their chips, their materials, and so on.
00:34:03.200 --> 00:34:03.680
Amazing.
00:34:03.839 --> 00:34:15.519
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?
00:34:16.079 --> 00:34:20.079
There is a huge plethora of our options here.
00:34:20.239 --> 00:34:26.239
Again, if it's proprietary research, there's ways to to for the company to keep that IP.
00:34:26.639 --> 00:34:34.719
There is alternative approaches through what they call CRADAS, which allow us to actually lay out how that IP should be distributed.
00:34:34.880 --> 00:34:37.119
If it's open research, then it's open research.
00:34:37.280 --> 00:34:40.800
So I don't know how that would go with IP at that point in time.
00:34:41.119 --> 00:34:54.400
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.
00:34:54.800 --> 00:34:55.760
Okay, I understand.
00:34:55.920 --> 00:35:02.639
And are there any areas for industry where you would not work on in terms of the research?
00:35:02.800 --> 00:35:08.079
Like, is there any like a line that is like, okay, above the line, not touching it?
00:35:08.800 --> 00:35:17.280
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.
00:35:17.440 --> 00:35:19.519
So we are an Office of Science lab.
00:35:19.599 --> 00:35:26.639
There is some other labs that are focused more on national security that have slightly different missions that would be more amenable to that.
00:35:26.800 --> 00:35:39.760
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.
00:35:40.000 --> 00:35:53.599
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.
00:35:53.760 --> 00:36:06.000
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.
00:36:06.320 --> 00:36:07.199
Yeah, I understand.
00:36:07.280 --> 00:36:12.400
So this is basically a strategic decision to just cut off the defense and leave it for the other labs.
00:36:12.719 --> 00:36:18.880
It becomes a national security problem, and that changes the dynamic of the lab because uh there's a lot of uh yeah.
00:36:19.119 --> 00:36:22.400
We are open science, we focus on open science.
00:36:22.480 --> 00:36:23.440
Okay, that's the key point.
00:36:23.840 --> 00:36:38.159
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?
00:36:38.480 --> 00:36:39.519
I do not believe that.
00:36:39.599 --> 00:36:51.280
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.
00:36:51.519 --> 00:36:59.679
And again, I want to reiterate Berkeley Lab and UC Berkeley are really at the forefront of doing that.
00:36:59.920 --> 00:37:07.679
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.
00:37:07.840 --> 00:37:09.760
And we do that on a regular basis.
00:37:10.000 --> 00:37:21.760
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?
00:37:21.920 --> 00:37:24.639
It's like the funding problem, because that's hard to believe.
00:37:24.800 --> 00:37:27.519
It may be a culture, is a change of lifestyle.
00:37:27.599 --> 00:37:28.400
What is it?
00:37:28.719 --> 00:37:31.599
Oh, yeah, it's a change that would be a change of lifestyle.
00:37:31.679 --> 00:37:38.239
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.
00:37:38.400 --> 00:37:48.000
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.
00:37:48.480 --> 00:38:06.239
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.
00:38:06.559 --> 00:38:13.119
But there is enough people within our lab that have made that transition and started companies.
00:38:13.360 --> 00:38:15.199
And were probably very successful.
00:38:15.840 --> 00:38:19.840
Like any other startup, there is successes and failures, but yes.
00:38:20.159 --> 00:38:39.119
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.
00:38:39.280 --> 00:38:44.639
Anyway, I would like to also ask you a hypothetical question completely.
00:38:44.800 --> 00:38:56.639
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?
00:38:56.880 --> 00:39:02.159
How this will looks like what can be improved?
00:39:02.559 --> 00:39:17.599
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.
00:39:17.840 --> 00:39:26.320
It would be good to see more smooth pipelines from the national lab research to industry.
00:39:26.719 --> 00:39:31.760
And yeah, that's I think is the biggest thing that we need to see happen.
00:39:32.000 --> 00:39:47.840
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.
00:39:48.079 --> 00:39:51.039
And there is a lot of programs that that support that.
00:39:51.519 --> 00:40:00.400
I've been involved in a program called the HBC for energy innovation, where we actually directly have to work with companies.
00:40:00.559 --> 00:40:06.079
So companies can pretty much pose a problem and say, can we solve this problem?
00:40:06.320 --> 00:40:08.400
Can you guys help us solve this problem?
00:40:08.639 --> 00:40:16.320
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.
00:40:16.559 --> 00:40:24.719
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.
00:40:24.880 --> 00:40:31.280
We're trying to figure out how they can actually press paper and uh get the water out more efficiently.
00:40:31.599 --> 00:40:40.960
And so what it ended up being is a project where multiple companies, competitors, work together to put a proposal in.
00:40:41.280 --> 00:40:51.679
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.
00:40:51.920 --> 00:41:05.199
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.
00:41:05.599 --> 00:41:15.280
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.
00:41:15.440 --> 00:41:22.960
Like the ability that the companies, competitors' companies are able to come together to achieve one goal as an industry.
00:41:23.199 --> 00:41:26.880
That was something that I think Europe is still need to learn.
00:41:27.119 --> 00:41:37.360
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.
00:41:37.599 --> 00:41:38.800
Okay, that's very interesting.
00:41:38.960 --> 00:41:46.000
So um I would like to also ask you a little bit more about the quantum and the future of quantum.
00:41:46.079 --> 00:41:57.039
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.
00:41:57.119 --> 00:42:01.679
So we can compare this in the next five, ten years, maybe together.
00:42:01.840 --> 00:42:05.039
Uh, what work out well, what didn't.
00:42:05.280 --> 00:42:07.440
So the same here will be Bert for you.
00:42:07.599 --> 00:42:15.519
I would like to ask you what do you think will be the biggest breakthrough for quantum technologies in the next five years?
00:42:15.920 --> 00:42:19.199
Well, breakthroughs are all uh serendipitous.
00:42:19.440 --> 00:42:21.199
You cannot plan breakthroughs.
00:42:21.440 --> 00:42:27.599
I would say we've actually gone through a couple of pretty big revolutions in the last couple of years.
00:42:27.840 --> 00:42:32.480
Last year's or the last 12 months, revolution has been error correction.
00:42:32.719 --> 00:42:57.199
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.
00:42:57.440 --> 00:43:16.960
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.
00:43:18.559 --> 00:43:25.840
And if you can pick um specific industry, we which industry do you think they will adopt quantum first?
00:43:26.159 --> 00:43:38.000
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.
00:43:38.159 --> 00:43:50.079
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.
00:43:50.239 --> 00:43:55.199
What we also see is a very big opportunity in what's called the optimization space.
00:43:55.360 --> 00:43:57.440
So logistics, all of those.
00:43:57.599 --> 00:43:59.760
Um we've seen already well.
00:44:00.000 --> 00:44:04.960
Well, D-Wave has shown that for a while, that they can actually do uh optimization really well.
00:44:05.199 --> 00:44:15.440
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.
00:44:15.599 --> 00:44:19.519
And so the logistics world that could be anything, right?
00:44:20.159 --> 00:44:23.599
Traffic to uh airplanes to scheduling.
00:44:23.760 --> 00:44:27.280
That is, I think, another a second very big potential market.
00:44:27.440 --> 00:44:34.880
For the rest, it becomes a little bit more focused on when we get the algorithms and applications ready to run on quantum computers.
00:44:35.039 --> 00:44:43.199
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.
00:44:43.280 --> 00:44:52.000
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?
00:44:52.079 --> 00:44:54.159
The optimization and minimization problems.
00:44:54.480 --> 00:44:56.880
It's 100% in their interest to do that.
00:44:57.280 --> 00:44:57.920
Exactly.
00:44:58.159 --> 00:45:05.599
So I can see a win there, but they really need to combine it probably with secure networking at some point.
00:45:05.760 --> 00:45:10.639
So um there I would say those are the primary angles that I can see.
00:45:10.800 --> 00:45:13.119
Um and we're talking about quantum computing.
00:45:13.199 --> 00:45:16.239
There's other angles to think about when it comes to quantum sensing.
00:45:16.320 --> 00:45:21.360
There's sensors that are being built that are much smaller, more efficient, and more accurate.
00:45:21.519 --> 00:45:29.679
And so you could start thinking about sensing quantum sensors being potentially used for navigation without GPS.
00:45:29.920 --> 00:45:35.360
So there is a lot of other, and that's why I wanted to point that out.
00:45:35.519 --> 00:45:39.440
There is a lot of quantum beyond quantum computing too.
00:45:39.679 --> 00:45:45.360
Sensing and um also networking are going to become very important.
00:45:45.519 --> 00:46:00.320
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.
00:46:00.559 --> 00:46:04.480
So bit systems that are within reach in the next three to five years.
00:46:05.199 --> 00:46:12.000
So uh the Google story is really focused on companies you need to get uh quantum secure.
00:46:12.079 --> 00:46:15.599
Uh and that is definitely a true thing uh to think about.
00:46:15.679 --> 00:46:21.039
And that's then again back related to quantum networking and moving data in a secure way.
00:46:21.440 --> 00:46:25.440
Yes, there's that a lot going on, definitely, and you are 100% right.
00:46:25.599 --> 00:46:33.280
People usually when they think about quantum, they think about quantum computer and then looking for the mysterious growl, right?
00:46:33.360 --> 00:46:35.920
Uh waiting for the moment when we commercialize.
00:46:36.000 --> 00:46:37.679
But um, you said it yourself.
00:46:37.760 --> 00:46:42.159
Um, DOF is already proving that uh optimalization can happen.
00:46:42.320 --> 00:46:50.880
Uh, in fact, I was just reading recently their um the reports, and they claiming they already have like 135 clients, commercial clients.
00:46:50.960 --> 00:46:55.360
So they they go from live deployment for a while now, right?
00:46:55.599 --> 00:46:58.320
Um another thing that is you talk about GP Morgan.
00:46:58.480 --> 00:47:08.000
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.
00:47:08.480 --> 00:47:18.159
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.
00:47:18.480 --> 00:47:20.880
He said there is no future.
00:47:21.039 --> 00:47:28.559
Well, he said quantum sensing is not the future, quantum sensing as an industry is already here, it's existing, it's working.
00:47:28.800 --> 00:47:31.679
It's not uh speaking of the melody of the future.
00:47:31.920 --> 00:47:33.280
It's here already today.
00:47:33.440 --> 00:47:35.760
So there's a lot, a lot going on.
00:47:36.079 --> 00:47:47.199
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.
00:47:47.440 --> 00:47:52.000
We have been living in a quantum age since quantum mechanics was invented.
00:47:52.239 --> 00:47:59.599
Actually, the fact that you can watch us or listen to us is partly driven because of quantum mechanics.
00:47:59.840 --> 00:48:07.119
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?
00:48:07.280 --> 00:48:17.360
And that's where quantum computing, quantum networking, and and quantum sensing really have become new ways of using quantum technology in everyday life.
00:48:17.440 --> 00:48:20.800
Uh and that I think is a very big thing to think about.
00:48:20.960 --> 00:48:22.719
We've always lived in the quantum age.
00:48:22.800 --> 00:48:25.840
Every chip is driven by quantum mechanics.
00:48:26.000 --> 00:48:27.360
So uh yes.
00:48:28.000 --> 00:48:29.280
Quantum is already here.
00:48:29.360 --> 00:48:29.840
I like it.
00:48:30.000 --> 00:48:31.760
Quantum has been here for a long time.
00:48:32.239 --> 00:48:32.719
I love it.
00:48:32.800 --> 00:48:35.519
And with this accent, we probably should wrap it up.
00:48:35.920 --> 00:48:43.440
Bert, thank you so much for being here today with me and spending your uh morning, late morning, afternoon uh already.
00:48:43.599 --> 00:48:44.480
It was a pleasure.
00:48:45.199 --> 00:48:45.760
Thank you.
00:48:45.920 --> 00:48:47.199
I appreciate the time.
00:48:47.599 --> 00:48:48.239
Thank you.
00:48:49.679 --> 00:48:51.840
That was Blond and Quantum.
00:48:52.000 --> 00:48:55.760
Thank you for joining me on this journey through the quantum business frontier.
00:48:55.920 --> 00:49:07.119
If you like the episode, please review another podcast and help more people discover the quantum world without needing to untangle the theoretical physics.
00:49:07.360 --> 00:49:08.559
See you next time!
