AI-Driven Hardware-Enabled Platform Security | The Six Five Summit

Hi, I am Rob Hayes, CEO Adam Computing. We're a 6-year-old quantum computing company based in Boulder, Colorado and Berkeley, California. We've got operations in both.

We're building quantum hardware platforms out of the neutral atom modality. Uh, so lemme tell you a little bit about the platform. So basically what you're looking at here is a, uh, vacuum system, which is the heart of our system.

And in that vacuum system, through that tiny black, uh, window, you see in the bottom right, uh, is, uh, an array of atoms that we have trapped in optical tweezers. So every individual atom is in an individual laser beam. And with different, uh, flashes of light, you were able to manipulate the quantums or the nuclear spin of those atoms, and that becomes the basis of our cubits.

Uh, and, uh, what you're seeing in that, um, grid there is actually through a microscope objective, you're seeing, uh, a photograph of individual atoms that are trapped in those, uh, optical tweezers. And this makes really good qubits because we're using a natural material. Atoms, the qubits are inherent in there.

All atoms are identical, so there's no manufacturing defects. We control all these things wirelessly with lasers. Um, and it's generally exponentially scalable with more, uh, more array or more laser beams in the array.

So it's also very compact 'cause atoms are to, uh, tidy. And so, um, one of the things that we and our collaborators, uh, have been looking at is how do we improve the sustainability of the planet? And specifically, how can quantum computing, uh, help us address sustainability not in a marginal or incremental way, but in major breakthroughs.

And so there's been a lot of research in this, and this is what's so exciting about quantum computing. So we ask ourselves, what can we do? Well, what if, uh, there were new processes for fertilizer production that reduced carbon emissions and saved a huge amount of the energy consumption, uh, worldwide.

What if we could bring on, uh, orders of magnitude more sustainable resources like wind and solar and batteries to the energy grid and have it, uh, be optimized as far as how the power gets routed and more efficient and resilient as a result? What if the fuel efficiency of aircraft could be improved through enhanced modeling and optimized spike dynamics? What if solar cells could be twice as efficient at half the cost, uh, through better material simulation and combinatorial optimization?

What if sued additives and anti methane vaccines could reduce emissions from cattle through precise molecular simulation? So these are some of the areas and many more that researchers have been studying around how quantum computing can help. And we've been collaborating with, uh, partners on a number of these ourselves.

And so, for one example is we've been working with the National Renewable Energy Laboratory, or NRL on looking at how we could, or they have, uh, connected DN nr quantum computing into the lube with their super computing, uh, systems in order to go simulate the, uh, digital twin of the electric grid and see how that can be more resilient for, uh, for basically, uh, adapting to new supply of energy coming online, more demand weather, and different, um, you know, disasters or events that happen in the grid. Uh, but it goes beyond that energy, agriculture, transportation, smart cities. In fact, um, McKinsey put out a report last month that said, just in these kind of four, uh, segments alone, finance, logistics, chemicals and pharma, there's $2 trillion worth of economic value that can be unlocked by quantum computing, uh, through some very specific use cases over the next decade, which is obviously, uh, a huge value that we want to go after.

And so what are some of those specific applications or algorithms that are listed here on the right? Um, and we're also mapping to like, what are the physical resource or what are the resources required in order to, uh, to compute some of these different applications? So what we're seeing here is, uh, machine learning acceleration, molecular simulations, financial simulations, seismic weight modeling.

These are some of the applications that can be unlocked at a production scale around 100, what we call logical qubits. We'll talk more about logical qubits in a second, but, um, that's kind of like the magic number where we have enough resources in the quantum computer to actually produce some of these results that people are looking for in a production environment. Now, there's other applications and algorithms that are gonna require thousands or even tens of thousands or more qubits.

They're also gonna require what we call deep circuits. So think about as, uh, a, a long, uh, deep code or long software programs in order to actually compute some of these applications. And so that raises the question, what do, what, what more do we need in the physical hardware in order to, uh, be able to address some of these applications?

And so that's what I wanna spend a little bit of time diving into. And it's the challenge of deep circuits. And so what we have here is we have an example of a, uh, a kind of a generic, uh, quantum computing circuit.

Uh, this circuit has four qubits that are represented by the zeros, the four zeros, and the briquettes, uh, on the rows there. And it's a, a circuit depth of n as represented by the columns there and want to kind of look at how error rates affect the ability to actually compute a circuit of this size. 9% fidelity, which means that they would get one error out of every thousand tries.

Um, that doesn't sound too bad. That's actually about state of the art for why any of the companies that have produced quantum computers to date have, uh, have shown. So that's a pretty good error rate, especially by today's standards.

And if you run the first four, uh, um, time steps through this circuit, you could think of this as like lines in a program. 9 and multiply it by itself four times. 6.

You go, Hmm, not so bad. Uh, but what if we're running a real circuit that has a real depth that could be much deeper? 9% of physical error or fidelities that we're seeing in the best systems today, after only 500, um, time steps through this, this circuit, we have 60% error rate.

And so we might as well flip a coin at that point. What do you need a quantum computer for? That's not a very good error rate.

So we need to improve. So the first thought is, well, what if we improve the fidelity of these qubits by one order of magnitude? 9% fidelity, one error out of every 10,000 tries.

And you see that, not surprisingly, you get an order magnitude better, um, circuit depth, uh, for the same error rate. So we move that 60% from 500 lines of code to 5,000. Um, but that's still not sufficient.

So what we really need is in or is to get to millions of lines of code to run or, or circuit depths of, of millions or even tens of millions, uh, to run some of these algorithms. And if you do the math, you're gonna see that you actually need to get something on the order of like 10, or sorry, eight, eight nines, or 10 to the minus eight, uh, error rate in order to, uh, be able to run these, uh, production algorithms, uh, that really are gonna matter for the world. And so that same data that I have on the right is represented on the graph of the left.

And it just kind of shows you this large gap from where we are today. Kind of three nines fidelity on physical qubits to eight nines fidelity that are required, uh, to run meaningful applications. And so now the question is, well, how do we get there?

So, uh, again, these are some of the applications and how many logical qubits are required. 9 to one order of magnitude better fidelity, uh, physicists believe that they can identify the noise sources, we can drive those out of the system. And with great effort and time and cost, we can actually improve by an order of magnitude, maybe two, probably not gonna get much further than that.

And so we're gonna need something that leaves this large gap between, you know, three or four nines of fidelity and eight or greater nines of fidelity that are gonna be required in order to run these applications. So how do we close that gap? That's where, uh, error correction codes come in.

So there's been a lot of research, uh, in the past about how do we basically map physical qubits to these logical qubits using kind of error correction codes that can actually, uh, yield better error rates, um, uh, and so forth and close that gap to logical qubits. So, um, let's talk about what a logical qubit is. So logical qubit really just starts with a large number of physical qubits, uh, that have a error rate below a certain threshold.

567 is probably the minimal in that, uh, most error correction codes can, can withstand. Um, and then what we do is we basically map a bunch of logical qubits, uh, uh, or a few logical qubits on top of a bunch of, of physical qubits. And so you could think of this as clusters of physical qubits that are connected through an algorithm, uh, and some features in the hardware that allow them to yield out by design a logical qubit that has a, a much better Error rate, lower error rate, um, you know, by design.

And so how we do that is, uh, we form these logical qubits, uh, by, by clustering these physical qubits together, we apply algorithms, um, a controls to be able to have those clusters of Facebook qubits act together as one logical qubit. And there's a bunch of hardware capabilities that are required in order to make that happen. Uh, we need low physical error rates, we need mid circuit measurement, we need long co appearance times, we need to be able to reload atoms.

Uh, there's a bunch of stuff that actually you don't need to worry about at all. That's our job is to worry about that kind of stuff. But one big takeaway is that in order to get to a large number of logical qubits, we needed an even larger number of physical qubits, and therefore we're gonna need the systems to scale.

And so having a platform that's scalable to get more and more physical qubits with all of these features and the error correction codes is what the whole game of quantum computing is about in order to realize the value of these applications. And that's where our platform at Neutral Atoms is really shining, is that we're using these tiny atoms that have this wireless control, uh, and uh, and proven scalability to really ramp up the number of physical qubits with good fidelity and great coherence times. Uh, and then we're working with partners, uh, to, to put novel error correction codes on top of that to, to yield out, um, the, the logical qubits that we need.

So here's our roadmap. So our roadmap, uh, we started with our Phoenix prototype that we announced back in 2021. It's a hundred qubit, uh, physical qubit system, uh, and that's been up and running for a number of years and allowed us to do a lot of experimentation and really kind of perfect our technology.

Uh, we're now working on systems that we announced last year that are 1,225 qubits. Um, these systems, uh, are, uh, are will be available more generally to, to more customers later this year. Um, but those are kind of up and running.

And what we're currently working on our next generation systems will scale by another order of magnitude to 10,000 plus qubits, and then again, uh, in the next generation of a hundred thousand plus qubits. So we're, we're on a path to deliver an order of magnitude more physical qubits every generation. And this is gonna give us a really fast path to logical qubits.

So, uh, in the past or up until recently most, uh, research on like surface codes and other air correction codes, uh, were we're requiring something on the order of a thousand to one ratio between physical to logical cubits. Meaning I'd take a thousand physical cubits, I'd map 'em Down to one logical cubit. And that means that you would need, in order to get like 10 logical qubits, you'd need 10,000 physical qubits.

And that would, that would say that our next generation systems would be the first time we start to, uh, show off what we call a fault tolerance system. But in more recent times, there's been a a, an advancement in air correction algorithm research, and we're starting to see new codes, new novel codes come online, especially on the neutral atom platform that are yielding out, uh, something like, uh, a hundred to one ratio on physical to logical qubits, which pulls in the logical qubit roadmap by an entire generation. So on the current systems, the current hardware that we have today, we're gonna be able to demonstrate something on the order of 10 or more logical qubits on this generation.

And we will, in the very next generation be able to get to a hundred logical qubits, which is that threshold that everybody's looking for to get over in order to have some of these production applications, uh, come to life. And that's a really exciting, because there's been, this proverbial of quantum computing is 10 years out for a long time, but now we're only one generation away from seeing some real meaningful production applications. And in this current generation, we're gonna demonstrate all the technologies really required in order to go do that.

So the proof of concept and and and so forth will be kind of imminent. And then we'll have real production out in a, in a, in a very short order in the next, you know, couple years, uh, plus or minus. And so I'd like to just revisit just one application that's really important that we think we can get to when we get to that a hundred logical qubit, uh, threshold that we're, we're, we can see on the near horizon now.

And this is fertilizer production. So fertilizer today is, uh, is produced in large quantities to to feed the planet right to, to feed the fields and grow the plants. Um, it's today produced by one process, primarily it's called the Haber-Bosch process.

This process is a hundred years old. Um, it requires high heat and pressure to produce ammonia, which, which is one of the primary components of these, uh, synthetic fertilizers that we use around the world. Um, Microsoft put out a paperback in 2016, a great paper that basically studied this problem and how quantum computing could, could address it.

And so you may not know that fertilizer production actually consumes about 2% of the world's energy consumption today. So it's a huge contributor or consumer of that energy. Um, which is, which is great 'cause it feeds the world, but that's a lot of energy.

Uh, also produces 420, um, metric tons of carbon dioxide every year. So if we could reduce this, uh, that would be really meaningful. Now again, this Haber Bosch process uses high heat and pressure, but if you look at your compost bin in your garden, uh, you have bacteria and natural processes that are producing the same results, uh, and the same nutrients, uh, ambient temperature.

They don't require high heat that don't require energy in right, other than just the sun and the natural processes or the bacteria. The problem is we don't understand how that works. And so it's believed in what this paper study is that quantum computing actually can provide the right level of simulation at the molecular level in order to really understand what's going on with these bacteria and the natural processes to produce the ammonia.

And then hopefully we can reproduce that once we understand how it works at an industrial scale and come up with a new process for producing fertilizer that could significantly reduce the energy consumption required and the carbon dioxide emissions. So that's really exciting use case and that's just one we see on the near horizon as we get to fall tolerance on our neutral Adam platforms. So with that, thank you.

I'm Rob Hayes, Adam Computing and if you'd like to collaborate, reach out my contact information's on the screen. Welcome back to the six five summit. You just heard from Paul Smith Goodson of Adam Computing, one of the maybe 10 quantum computing players in the United States.

So it's crazy to hear from an innovator in this space and just all the complex problems it takes in getting it off the ground. But that said, quantum computing can also tackle a lot of us our biggest problems. So Lisa, what did you take away from that conversation?

I think what struck me most is the timeline that addressing valuable applications, the speed with which quantum is becoming more of a reality that the future might be, you know, what, what does it say in the rear view mirror objects closer than they appear. Yeah, that's exactly what I thought about when I heard that session. Yeah, It's, it's, um, I I have a hard time getting my head around what quantum is gonna do and, and, and the, the things that will be so different.

Things like cybersecurity will be entirely different. Um, hacking will be entirely different with quantum computing. Yes.

Um, the ability to make Bitcoin will be entirely different, uh, with quantum computing 'cause the power is just so much greater. And everything you're mentioning there is why it's such a concern in getting this technology off the ground and why we're seeing investment at the federal level to get it off the ground because it really does open up this world race right now to get quantum computing's a reality and a reality that will make artificial intelligence everything we've covered in our sessions, you know, really come into the, you know, into the, uh, into the, our environment. Yeah, Definitely.

Well, bill McDermott set us off on day one talking about this revolution. That's probably the biggest we've ever seen. And AI and quantum are really the catalysts I see as really driving that forward.

Yep. And, and you know, AI's happening now, Quantum's right around the corner. Definitely.

Yep. Well, you know what else is right around the corner, our recap session, so stay with Us. You're the best session, right?

I mean, it's us. Oh, flying Like Whatcha talking about. We're the best session.

We'll be back. Okay, well we're all aligned on that. It's the best session to come, so stay tuned.