SPEAKER_02 0:10

In episode 139, Patrick Insecret speaker with senior scientists at the Lawrence Berkeley National Laboratory, Bert De Young, to discuss quantum advantage for scientific discovery, chemistry and material science, and the pursuit of the right modality. Welcome to Entangled Things, your quantum computing podcast, hosted by Patrick and Cyprien.

SPEAKER_03 0:40

Hey Cyprian, how are you doing?

SPEAKER_00 0:42

Hey Patrick. I'm doing great. Looking forward for another episode of Entangle Things.

SPEAKER_03 0:46

Well, this is a good one today. So, Bert, do you mind introducing yourself to our audience?

SPEAKER_01 0:52

Sure. Well, Patrick and Brittany, thank you for having me here. So I'm Bert DeYoung. I'm a senior scientist here at Lawrence Berkeley National Lab. So one of the uh 17 Department of Energy national labs. Um what drives me with quantum is so right now I lead the Quantum System Accelerator, which is one of the five national quantum initiative centers. Um that's a large investment by the Department of Energy focused on uh on delivering kind of the next generation quantum technologies and not just the technologies, but also starting to demonstrate that there is a quantum advantage or a usefulness uh for scientific discovery using quantum computing specifically. That is our focus. Uh so the five centers, in case you uh you're not familiar with that, uh the National Quantum Initiative is something that uh uh Congress has been uh um putting in place for the last five years. They are reauthorising it right now, um, which really lays out the drive for the US to be a leader in quantum technologies. Um so these five centers are the Department of Energy's effort to really show advance the technology and show the opportunities and uh the uh challenges and how we can solve those uh in the next, well, for us five years. We just got renewed as five centers.

SPEAKER_03 2:17

Excellent.

SPEAKER_01 2:18

Um our undersecretary Dario Gill announced that late November. Um, so we have been running um to really start this process and and really making significant advances.

SPEAKER_03 2:31

That's amazing.

SPEAKER_01 2:32

Um so I can tell you a little bit about my center. Please. Uh my center has been developing, um we have a very broad view of how we're going to approach this. Uh so first of all, I should have said maybe I'm a computational chemist by training, so I'm not a quantum physicist. Um I come from a uh a background where I want to solve scientific problems. So these kind of what you heard at the beginning, we want to solve problems, this is really what drives anything that I do. So I lived in a in in for a long time in the high performance computing world, so in the exascale world. And for the last 14 years, I've been focused more on AI and quantum. And quantum is really the biggest portfolio right now. Um, so I want to solve real chemistry problems in my case, but there is very interesting problems for the Department of Energy to be solved in a very broad set of um fundamental science problems. Uh think: can we build a thousand-mile battery? Can we build a solar cell that recovers 80% of the energy instead of 20%? Uh there is a lot of opportunities, and that's the energy part of the story.

SPEAKER_03 3:42

But there is a lot of material science, really.

SPEAKER_01 3:45

Material science, that's a very good, but also chemistry. Um, everybody that has worked in quantum uh has heard the story of FOMOCO, which is uh the system, the the natural system that can convert nitrogen and hydrogen into ammonium. That takes about three percent of the world's energy right now. Well, if we can replace the so-called Fritz Haver process um that is used in industry, very energy consumption high energy consumption, if we can replace that with a natural version of it, so to speak, or mimicked natural version, because nature knows how to do it, then we could potentially save a lot of energy. But that's not a trivial simulation. So um that's why people have been using that as one of the benchmarks. Um, that's a very complicated system uh that needs to be solved, and uh uh we will need a very significant size quantum computer for that.

SPEAKER_03 4:45

So Cyprian's talked about this quite a bit. He's the one that brought this molecule to my attention years ago. Um, and he's mentioned it in the past. Do you have an idea? I mean, uh we have an idea of how big of a quantum computer in terms of logical bits we'd need for Schore's algorithm to impact RSA 2048. We're looking at five to fifteen thousand logical qubits. Do you have an idea of how big of a quantum of a logical quantum qubits you'd need in order to do that that molecule? Is it thousands? Is it millions?

SPEAKER_01 5:17

So the interesting part is it's not always driven by um the size, but rather the fidelity. So let's say uh I can do probably simulate the system with a couple of hundred logical qubits. But unlike what you said, uh we want to solve Schor's algorithm, we need to kind of have 10 to the minus 16, 10 to the minus 18 fidelity on the operations to be able to get a reliable result. We don't need that as much for these chemistry simulations. We are very happy if we get a final answer that is 10 to the minus 1, because that is enough of an accuracy for us to provide an understanding and get an understanding of how the process works. So we have always targeted, and and my center is really focused on targeting uh systems that are maybe 10 to the minus seven, 10 to the minus eight fidelity per operation. Um so if I can get a 200 logical qubits at 10 to the minus eight, that means I can do about a million to 10 million operations uh before that result gets worse than 10 to the minus one. I can do a lot.

SPEAKER_02 6:32

Yeah.

SPEAKER_01 6:33

The key part with ki with the FOMOCO system, for example, the chemistry uh molecule is everybody has to be focused on let's get the ground state, get to get to the lowest energy state of that system. Completely irrelevant. It's a very dynamic process, so you actually need to start to simulate the dynamics. And this is actually really interesting because when we say we want to take a quantum computer, a quantum system to mimic a dynamical evolution of a quantum system, that's exactly what Feynman had in mind. Um so it's very Feynman-style thinking of simulations uh and computing.

SPEAKER_00 7:12

So you you were saying that you're like very interested in solving chemistry problems. Uh, for our audience, like what would be your like top three problems that you would love to see being solved by computers in the near future?

SPEAKER_01 7:29

So this is the in chemistry specifically.

SPEAKER_00 7:32

In chemistry, yeah, yeah, yeah.

SPEAKER_01 7:34

Yeah, so that's the hard part. The chemistry is such a broad field, right? Um, so this is why I brought up can we actually build a battery that is where the charge transfer is more easy so that we can, and the transformation that have to happen in the battery are not as extreme so that they last longer and we can get more miles out of a single battery. Uh that to me is a very important one. I again I'm in California, so we we like to think of green energy to some extent, right?

SPEAKER_03 8:08

Uh and gas prices aren't hurting that that thinking either.

SPEAKER_01 8:12

Uh yeah, there is some some gas stations here that are reaching seven or eight dollars right now. So, yes, it's a little bit of a challenging situation. Uh but in general, um, there is a lot of problems here too. Let think of fresh water. This is something that is a major problem in the nation uh going forward. Uh less snow, less fresh water. So we need to extract it from seawater. That's very energy intensive. So um, can we use solar? Recover energy, recover energy at a low energy gradient is is something that you need to have organic uh uh organic materials to actually extract the energy back out of something that has been boiling. It's so you need to actually have uh thermoelectrics that are of a very low energy gradient, and that is something that we cannot really get. We have a very low percentage of return on that. It's like we can't recover 10% of the energy. We need to recover 80% of the energy.

SPEAKER_03 9:15

Exactly.

SPEAKER_01 9:15

So being able to model that is again, it's a very dynamical process, uh, a very Feynman-style process. And so impossible to do in classical computers. And so we need to do that.

SPEAKER_03 9:29

Forget about the fact that efficiency is the is better than invention, because you already it's a burden hand if you can make it more efficient. You mentioned solar. Uh perovskite seems to be the the thing that's getting us to 40%, perhaps by layering. Uh is that the future, or are there other things that we might be on uh thinking about that'll get us to 80%?

SPEAKER_01 9:51

Who knows, right? So so yeah, my center is focused on doing some of the chemical dynamics, but also on exactly that. Can we look at the high energy, uh sorry, the high TC uh superconductors, right? Exactly like that is related to these perovskites. Can we actually really understand how these systems work? Again, we have models on classical computers, we have some understanding, but they are not of a level that are predictive, so that we could actually do the simulation and design on a quantum computer and then build.

SPEAKER_03 10:29

When you say TC, I do you mean temperature High temperature, yeah. Okay, so basically something that might even work at at ambient temperatures with the Trevor Burrus, Jr.

SPEAKER_01 10:37

That's I have a hard time believing.

SPEAKER_03 10:41

You don't think that's possible at the moment?

SPEAKER_01 10:43

I would have a hard time believing that because if that would be feasible, I would uh I would assume nature has already done it. Okay. They have had what? Um billions of years of of evolution, and then they must have figured it out somehow.

SPEAKER_03 10:59

Okay, I hear you.

SPEAKER_01 11:01

So but there is a lot of uh uh opportunities along that way, along the way to make that happen.

SPEAKER_00 11:07

So speaking about uh you mentioned the topic of quantum advantage, and I know that's a topic that sparked a lot of debate and back and forth. Where do you think we are um right now in terms of uh being to able to prove actual quantum advantage? Because we've seen it in the past, right? And it was kind of like a back and forth. So I would love to hear your take on where are we now as of 2026 with with this 2026?

SPEAKER_01 11:44

I would say in the next couple of years, we actually will demonstrate an advantage in the sense that we can do simulations that are not feasible in classical computers anymore. I I don't think we're that far away. You see that in a lot of companies uh and and research institutions right now. They are actually working on simulations, demonstrating simulations that are getting close. Um we're actually working on something that uh we're trying to prove actually might be a true scientific advantage using a quantum computer. Um that and that's where actually your statement actually becomes very interesting, right? So um you have that dynamic where the quantum computing side will say, hey, we have an advantage. And then uh the classical computing side comes back and says, No, we know how to fix this now. We learn from you. But the interesting part is that is a good dynamic. Uh, it keeps us honest uh in the quantum computing world, um, but it also actually advances the classical computing side too. Yeah there is a lot of quantum-inspired algorithms that have come been developed as a result of what we learned of using a quantum computer. And that means we can actually get more bang for a buck out of a classical computer. So honestly, to me, that is a win on its own. Uh that dynamic, that that not fight, but kind of that that tension between classical and quantum, pushing each other exactly, driving this forward, and and jointly get to be able to get to points where we can simulate things we couldn't do a couple of years ago, either classically or quantumly. And the reality is, in the long run, I would expect the classical computers and the quantum computers to be very closely working together. A quantum computer is not good at everything. No. It has its quantum nature, it has powers uh that can be harnessed for specific parts of simulations. There is other parts of the simulations that are way better on classical computers. Classical computers are good at adding one plus one, a quantum computers not. Um so you need to kind of start looking at how we can use the strength of both. And that's what you start to see these days is that um now that the quantum computers get a scale, there is that need to integrate with HPC. Um and that's in many different ways. Uh we have simulations that are jointly run on quantum and classical. The the key components that are need to be run on quantum computers are run on a quantum computer, and vice versa. Um and the reality is quantum computing needs to rely on HPC going forward. Um we need to go to error correction. The only way to get error correction and and know where to correct is to analyze the data. How do you do that? You need to use classical computers. Um we are going to be relying on classical computers for a long time. Quantum is not just plainly replacing everything.

SPEAKER_00 14:50

Well, I think the the perfect example, right, is we've been mentioning on the show many, many times Schorr's algorithm, right, and the the problem of uh factorization. Quantum actually solves a very specific part of that, like the order-finding part. Um and and the rest of it is this is classical.

SPEAKER_01 15:09

So um, yeah, exactly. And a lot of you actually have to take the data from that you want to actually go and and and uh and push through Schure's algorithm. You have to transform it first anyway. You have to get it into a shape that you can feed into a quantum computer, yeah, do the in the uh run the simulation on a quantum computer, get the data out, and have to do pro-processing again. So that's that synergy is going to go and stay there for the long run. And and honestly, in the future, what I see is an uh a more it's not a complete black box, but kind of a an ecosystem where you have quantum computers, classical computers, and AI working together in any way, shape, or form um to advance the knowledge that they can produce.

SPEAKER_03 16:02

I the way I like to think about it is or explain it to people is it's like you're trying to peel the paint off of something. It's hard to get that first little corner. That's where quantum gives you something. It does something that's not possible to do otherwise, but you still need the normal classical to do the the heavy lifting. And that that shores kind of taught us that, and as did uh Grover's as well.

SPEAKER_01 16:26

Yeah. Actually, sometimes it might be the other way around. The classical might give us enough of appeal than the rest of the quantum and get it. I've got actually converting a lot of information that we have in the classical space onto a quantum computer is is a very challenging problem.

SPEAKER_03 16:42

I assume AI is going to help us quite a bit with that. That AI will will that the two will help each other move forward in a way that uh I mean I'm starting to see articles about quantum memory, which uh Cyprian you know pointed out very long ago was was a problem. And and the the advancements in error correction, um at least the buzz about it in the last few weeks has been quite big. I uh Quantinium came out with a um a system they're saying that they can do a two to one logical to physical qubits. I I haven't dug into the papers or anything, but even if it's ten times worse than that, that's still better than I expected as far as error correction goes.

SPEAKER_01 17:23

Oh yeah. There is a very I would say the last couple of years have been very, very exciting in in the quantum computing ecosystem in general. It's like, one, we are now getting systems that are at scale. Um two, we are actually figuring out how we can harness the errors one way or another, and that is error correction. And we have seen literally in the last year, so many demonstrations where they have at least a break-even, are starting to see advantage of using error correction. And this is also the first time that we can actually start to work on these kinds of things, because error correction requires enough qubits. Right. So, yeah, you can develop all the theories you want, you don't know how they actually are going to work in practice. And this is the cool part again, is that now that we have the systems at scale, you see more and more practitioners push the boundaries on what error correction can do, test error correction, and as a result, start to figure out new ways of doing error correction. And so, yes, the quantitium is a great example. Um, quantinium and uh the neutral atom companies have a little bit of an advantage. They all use atoms, and atoms are all the same. So they have um a little bit easier than, for example, the superconducting qubit guys that where every qubit is different. Um so a lot of the uh important things where we want to go to with error correction is things like transversal operations, transversal gates. Um, that is very hard to do classic uh with superconducting qubits, but it's very easy to do with neutral atoms and trap diodes.

SPEAKER_03 19:05

So do you have a modality that that you think is serving it? Well, I I want to put this in a in the right way without shading it too much. There are multiple modalities to choose from. Um it in the beginning there was superconducting qubits, and that seemed to be the whole field. Microsoft went off and decided to try something that could promise a very rapid expansion if they can get it right, but they're starting from further back. And then we've got the the neutral atoms and the trapped ion people with their little lasers. Those modalities, they I don't know that I would ever fear that there's going to be a winner. We've said there'd probably be multiple modalities based on what you're trying to do. Is there one that's right now looking more promising for the energy solutions that you're looking for, the chemistry solutions you're looking for? Or is are you guys just still playing the field?

SPEAKER_01 19:56

So my center has taken a very unique approach there. So um uh the first we have been for the last five years, and we'll continue to do that in the next five years, for we have focused on neutral atoms, trapt ions, and superconducting qubits.

SPEAKER_03 20:12

So no, you haven't made a choice, really.

SPEAKER_01 20:14

We haven't made a choice, but actually you're exactly right. What we're seeing is that different technologies might have different advantages in the different science domains of the Department of Energy.

SPEAKER_03 20:27

Could we could could we have a single quantum computer with qubits of all three types? Possible.

SPEAKER_01 20:33

Uh so uh you could do that with atoms, but it's it's superconducting qubits because of the difference in speed, but also the type of frequencies that they work on. Uh um there's a lot of more distributed computing.

SPEAKER_03 20:46

You have one of one type doing one kind of operation. Uh we we've we've talked about uh networking. There's uh Cisco came on and talked about their their ability, they're trying to network quantum computers. So you could take 1050 logical qubit quantum computers and have a 500 quantum computer, uh uh cubic quantum computer. Um maybe we'll see that where you could network together disparate topologies or m modalities, but but uh you you're probably right. It's the they're not gonna be in the same box.

SPEAKER_01 21:16

Uh and that's fine, right? So um we're doing a little bit of that in our center too. Uh we have been coupling uh trapped ions uh systems together. Um we at Berkeley uh at Berkeley Lab, we actually also have a team uh that is focused specifically on how we can connect quantum computers together. So they are actually doing it between um NV centers and and um and trapped ions. But there's a reason for that. They're in the same frequency regime, they can actually go in the telecom bands uh rally relatively easily, and so it's easy to communicate. But if you have to take um information, let's say you have a quantum computer. That is neutral atom and then one that's superconducting qubits. And you need to kind of transfer quantum information from the neutral atoms to the superconducting qubits, there is a very big change in frequencies that have to be done. So that that conversion process is actually something that is very hard to do, and people are starting to still figure out. Although there's always people that are creative and see if they can actually put them physically together and just based on space and separation, maybe you have them interact with.

SPEAKER_03 23:27

So we know them quite well. Yeah.

SPEAKER_01 23:30

And that's the I think the key part. Our center is not a, ah, let's do fundamental science, we'll see where it goes. We are actually looking at understanding the technology holes, the technology advances that need to have a have to happen, uh, and do the fundamental science to the engineering to make that happen. And a great example is, for example, first five years, we've been working a lot on integrated photonics. This is done uh uh with our partners at Cindilla and uh and some work also in Colorado. That technology has reached a point where we actually have chips that are desired by the Neutral Atoms and Ions companies. So now not only Quera, but also Atom Computing, uh INQ and uh um and quantinium are actually now partners in the center.

SPEAKER_03 24:22

Oh wow.

SPEAKER_01 24:23

And I think this is the a real powerful part of a center like ours. This is we are closely working with industry. And this is what DUE wants. This is also, it wants to see a tech transfer. Um kind of us de-risking some of these companies because integrated photonics is something that is on the roadmap for companies, but further out. They have to deliver two VCs right now, um, in the next couple of years. Um, so if we can actually uh de-risk them by actually providing some technologies going forward, then they can take advantage of it. They they can deliver their products in the future more more efficiently and and more cost-effectively. So uh this is really a very close partnership that uh our center has with a lot of these kind of companies. Um and we're even doing it in the superconducting qubit world. Uh so um uh we are we're partnering with Colab. Um so Colab uh is founded by John Martinez, our Nobel Prize winner. So I'm I'm always excited to say, yeah, we have a Nobel Prize winner.

SPEAKER_03 25:29

It's nice to have those in the in the back room, right?

SPEAKER_01 25:32

But he is really thinking about how to scale these quantum computers on uh with superconducting qubits and understands a lot of the challenges. And this is where uh the national labs, these centers can actually help a company, help the industry accelerate. Um, so we're actually going to use uh an entity called the Molecular Foundry, which is a user facility here in Berkeley Lab. Um we will have do a lot of rapid testing of chip designs and understanding where the errors come from so that we can actually help them build that next generation technology. And that's again a partnership that has to happen.

SPEAKER_03 26:11

That's very exciting. Yeah, and and I mean that's been since the end of World War II, that or since before World War II, really, that's been a big hallmark of private-public cooperation. You know, DARPA goes and figures out what the military needs, the DOE figures out what the energy needs are, and energy needs uh are really going to be top of mind for the next decade.

SPEAKER_01 26:34

Oh, for sure.

unknown 26:35

Yeah.

SPEAKER_00 26:36

One of the things, in addition to the actual building, right, and the error correcting, one of the things that we've been struggling for ever since the quantum computing was proposed as a computing model, is correctly embedding the problems or defining the problems, the real-world problems, right, uh, for uh quantum, because it's it's completely different, fundamentally different from what we used to do for classical. So I'd like to hear your opinion on what kind of progress are we seeing on that front? Are we getting better of framing the problems, uh kind of defining them in a way that they can benefit from these advancements? Or we're still at the state where we are really struggling with that.

SPEAKER_01 27:20

For I would say the Department of Energy problems, we definitely seeing that transition into we know what we need to simulate, but just trying to make it more and more efficient. Okay. Um, so my center, uh I'll talk a lot about the technology that our centers focus on, but we have uh uh a very significant effort actually in uh doing the application side. So we have research in high-NG physics, in, as I said, the chemical dynamics, and then the many body physics, which is really um these uh high TC superconductor type uh problems. They all have a commonality, and that is the dynamical nature of these problems. And we're actually believing that in the next couple of years we can demonstrate an advantage there. So we understand what needs to be done, and we also understand the problem we want to run and the outcome, because that outcome is something that community will be very excited about. And that's how I drive it anyway. I want to, we're not just going to run a simulation and say, oh, we can run it at something that that could not be done classically. We see that a lot. Now we're starting to see more and more people understanding this is a problem we want to solve, and if we get an answer, we have some new insight.

SPEAKER_02 28:41

Right.

SPEAKER_01 28:41

And that's where we want to go, and that's where our center wants to go. It's not just advancing the technology in thousand decks, but also starting to demonstrate that using this quantum computer, we got some new scientific insight, we discovered something new, uh, because that's really what we are about. That's what we should be about.

SPEAKER_03 29:02

Solving real problems, yeah.

SPEAKER_01 29:03

Real problems. Now, so that is for the Department of Energy. I think there is uh a broader question of how this could how quantum technology is going to be brought be utilized or uh given advantage to industry at large, right? So I would say there is some opportunities. Uh so the optimization community has demonstrated there is definitely an advantage there. Uh the hard part is encoding the problem into a uh a good representation that you can run on a quantum computer efficiently. But I think that community is getting there. Um and we know that because the banks have figured those kind of things out already. Uh JP Morgan Chase is doing a lot in that area. Uh and then another area that we now start to see progress in is more the kind of CFD type community, right? Um nonlinear PDEs that are particle different partial differential equations uh that are kind of solved by companies like Boeing, like car companies. Uh that's a nascent area, but people are getting really into trying to understand how they can encode that kind of simulation, that kind of problem on a quantum computer. And we see slowly more and more papers and more ideas and and approaches that are being developed there. And it's again, that's it's because we are now at scale and we can actually start to tackle these problems.

SPEAKER_03 30:33

Aaron Powell That's felt like the domain of um of the uh annealing computers like D-Wave for a while. The the the if if I'm thinking if I'm understanding what you're talking about, the optimization problems and things like that. Um but even they are looking at doing uh classical uh quantum uh universal gate quantum computing as well. Yes. Um so maybe you know we got some advantage from the annealing being uh available while we waited for the universal gates to mature. And so so maybe that's that's the phase that we're coming into.

SPEAKER_01 31:06

I agree. And then so the hard the interesting challenge opportunity neutral atoms are actually very good at doing optimization problems too. We've we've started to demonstrate that now. They're naturally a collection of atoms that are going to go for the most optimal configuration, the lowest energy configuration, which is what you want to do for an optimizer. Right. So there is that natural way to do these kind of things. Um, it's not really then a digital simulation, right? So, and that's that's the interesting part that we're also looking at a lot. It's like a lot of these problems don't need to be digital, they could be an analog simulation. Um, Feynman's idea was an analog simulation, not a digital simulation. Um Shore came and said, oh, we can do digital operations. Uh that's how the community is like, oh, we can do digital cooperations. Oh, we can do something that looks like a classical computer. Right.

SPEAKER_00 32:05

So it's it's that's just fabulous how we're going full circle, right? We essentially simplified our world model by introducing the digitalization, because that's essentially sampling of reality, right? Now we're kind of going back full circle and we're starting to look at at analog again just because we have better technology.

SPEAKER_01 32:27

Yeah, so digital is an interesting thing to think about, right? As we had all our problems if in our community, any of these equations fundamentally are an analog equation, right? But we had to find a way to compute it. So early on, we digitized or we discretized, which automatically meant digitizing. Um, and that's how we've evolved. We've done this for what, 50, 60 years now?

SPEAKER_00 32:57

Yeah.

SPEAKER_01 32:58

Um now the quantum computing comes around and says, well, it might not be as clear-cut. Maybe some of these things need to be revisited. And and I always push my my students, my postdocs also, it's like, think creatively. This is your basic equation. How would you solve it when you have things like entanglement, um, uh evolution, natural evolution of quantum systems, um and and and uh uh all the quantum features that you have in your system, how would you solve this problem differently? Um so some of them have come up with the idea of simulations that are more of the analog nature, some are mixtures, um, because hey, it might be easier to do something in a digital version uh versus an analog version. And so this opportunity of using different the modality in different ways is is a unique way, but it also changes the thinking and has to change the thinking of how we solve problems.

SPEAKER_00 33:59

Yeah, that's actually one of the things that we've been talking also a lot is um how much does it count in the bias of being basically educated and uh growing in the world of classical computing when trying to have this completely different mindset. And we're talking a lot about what we call the quantum-born generation, who are younger folks who have been basically uh developing their professional careers in a world where quantum is a thing, it's not just some kind of theory.

SPEAKER_01 34:39

As I said, I come from the IHBC world for a lot for at least 15 years. Uh and the first time I started to work on quantum problems, it's the same mode. This is how I did it on a classical computer. Let's just translate.

SPEAKER_03 34:56

Yeah.

SPEAKER_01 34:57

And see how how we can simply translate it and run it. That's not the most efficient way to do it. And so we're starting to see more and more people realizing you cannot just translate, you have to rethink the problem. And that's where I think the next couple of years will be interesting when people see new ways to use the quantum technology that's there and actually make significant gains.

SPEAKER_03 35:22

So we're coming close to the end of our time slot, but I did have one other thing to add to that, and that is back about 10 years ago at a conference Supreme and I were at, One Qubit, a company in uh Vancouver, north of you, um were talking about the fact that before there were quantum computers, they were getting an 8% increase in performance by just taking most algorithms and reimagining them in quantum space and using simulators. And um and since then they've you know they've taken that to the next level. And we've had other guests who who worry more about the quantum simulation and uh the quantum imagining, giving them an advantage even on simulations. Do you, as a chemist, I I study chemistry, I was a chemistry major for to the first two years of college and then switched. Um so I I I uh I I chickened out, I guess you would say. Um but would you there's been some talk in the past, mostly in passing, about a biological computing model or a chemical computing model. I I don't know what that would look like in in um, but I also had trouble imagining what a quantum computing model would look like before. Do you think there's gonna be other competitors in those spaces, especially as someone with a chemistry background?

SPEAKER_01 36:38

I think there's a lot of creative ways that uh uh once we really understand how to harness the quantum information, yeah, there might be different modalities. And the reality is one of the early demonstrations of a quantum computer was actually using NMR, nucleomagnetic resonance of a molecule. They encoded the information into the vibrational modes of a molecule. So, yes, early on they have done that. Uh interestingly, in my center we have the neutral atoms, but we also are working on neutral molecules or even charged molecules. Um why? Because now we have fermionic degrees of freedom, which is what the atoms are, but we also have what's called bosonic degrees of freedom, which is the coupling between these atoms. Uh and we can take advantage of that. Um that's what we're going to do with the trapped ion quantum computers too. It's like we're not going to put hundreds, we actually have build a trap that can hold 200 ions. Uh but um we're now much more interested in can we actually uh take 50 trapped ions and couple the modes between them. Um so instead of having two to the n, so two to the fifty, could we actually do six to the fifty? That's a computational power that's different. It's again, it's a different way of thinking of okay, we have a qubit that is two states. Well, maybe each bit has six states or ten states.

SPEAKER_03 38:08

Oh wow.

SPEAKER_01 38:09

And that changes the dynamic. And uh it's actually interesting because the first time uh the superconducting qubit community started to talk about Q-threads, which means they have three levels, the high energy physics community kind came around and said, that's exactly what we need, and we can now encode the information a lot more efficiently than we could do before.

SPEAKER_03 38:30

So that would be like taking a classical bit and changing it from a coin to a six-sided dice. Yeah. It would explode the memory, explode the possible permutations.

SPEAKER_01 38:40

Yes. But the way they have to think about encoding their information, they just don't have a spin that spin up and down. They also have a flavor or they have a color.

SPEAKER_02 38:49

Yeah.

SPEAKER_01 38:50

They want to encode that kind of an information that allows them to actually do that using more levels in a quantum system. And so that's those are the kind of things that we are exploring. It's the not just let's just scale. We are working on scaling, believe me. We're going to we're actually going to build a neutral atom system that is going to have 100 logical qubits, maybe a little more, at 10 to the minus six, 10 to the minus 7 in the next two, three years. Wow. So, yes, we are going to drive some of this. Um, they all have like 40,000 atoms. We can do a lot more with that. If you go and think of the high-NG physicists, they they are like, oh, give me 40,000 atoms. I can do some other things with that too.

SPEAKER_02 39:36

Right.

SPEAKER_01 39:36

So there's a lot of these interesting dynamics that are that are starting to happen where you get the application people, they now have access to systems, they are asking for new features within these systems to make them even more flexible, uh, to have more opportunities to kind of mimic the physics that they want to do onto the hardware that they have. Um, instead of the other way around, which we have been doing in classical computing. Hey, we have bits, we have um uh specific operations, just match the problem to whatever the hardware is. There is now still that dynamic where could we adjust the hardware potentially to solve the problem in a more efficient way? And that's that's I think the real cool dynamic that is happening right now in the quantum ecosystem, in the quantum computing ecosystem, where people are trying to figure out where that relationship is.

SPEAKER_03 40:31

Well, unless we're gonna make this a two-hour episode, I think we have to call it at some point. But this has been an amazing conversation, Bert. I really hope you're willing to come back on a more on a regular basis and talk to us because this has been very enlightening.

SPEAKER_00 40:45

Thank you.

SPEAKER_03 40:46

Thank you very much.

SPEAKER_00 40:47

It's been a real pleasure.

SPEAKER_03 40:48

So thanks everybody and join us again next time uh on Entangle Things.

SPEAKER_01 40:53

Thank you.

SPEAKER_02 41:28

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