Microsoft Research Podcast - Ideas: Quantum computing redefined with Chetan Nayak

Episode Date: February 19, 2025

Microsoft announced the creation of the first topoconductor and first QPU architecture with a topological core. Dr. Chetan Nayak, a technical fellow of Quantum Hardware at the company, discusses how the breakthroughs are redefining the field of quantum computing.

People sometimes say, well, quantum computers are just going to be like class computers, but faster. And that's not the case. So I really want to emphasize the fact that quantum computers are an entirely different modality of computing. There are certain problems which quantum computers are not just faster at than class computers, but quantum computers can solve and class computers have no chance of solving. You're listening to Ideas, a Microsoft research podcast

that dives deep into the world of technology research and the profound questions behind the code. I'm Gretchen Huizinga. In this series, we'll explore the technologies that are shaping our future and the big ideas that propel them forward. My guest today is Dr. Chaitin Nayak, a technical fellow of quantum hardware at Microsoft Quantum. Under Chaitin's leadership, the Microsoft Quantum team has published a paper

that demonstrates a fundamental operation for a scalable topological quantum computer. The team also announced the creation of the world's first topo conductor, more on that later, and first QPU architecture with a topological core called the Myorana 1. Chetan Nayak, I can't wait to find out what all of this is. Welcome to Ideas. Thank you. Thanks for having me and I'm excited to tell you about this stuff. Well, you have a huge list of accomplishments, accolades, and awards, a little alliteration there. But I want to start by getting to know a bit more about you and what got you there. So specifically, what's your research origin story, as it were? What big idea inspired

you to study the smallest parts of the universe? It's a great question. I think if I really have to go back to the origin story, it starts when I was a kid, you know, probably a preteen. And you know, I'd go to bookstores to, I know, people, I guess, maybe many of the people listen to this may not know what that is, but there used to be these brick and mortar storefronts where they would sell books, physical books. And I'd go to bookstores to buy books to read,

fit fiction, but I would browse through them and there'd be a non-fiction section, and often there'd be used books, sometimes used textbooks or used popular science books. And I remember, even though there were bookstores and libraries, I would spend a lot of time there leafing through books and got exposed to, accidentally exposed to a lot of ideas that I wouldn't otherwise have been. Just sort of, I maybe went there looking to pick up the next Lord of the Rings book.

And while I was there, you know, wandering to a book that was sort of explaining the theory of relativity to non-scientists. And I remember leafing through those books and actually reading about Einstein's discoveries, you know, most famously equals MC squared, but actually a lot of those books are explaining these thought experiments that Einstein did where he was thinking about if he were on a train that were traveling at the speed of light, what would light look like to him? Would he catch up to it? And all these incredible thought experiments that he did to try to figure out, to really

play around with the basic laws as they were currently understood of physics and by stretching and pulling them and going into extreme, taking them to extreme situations, you could either find the flaws in them or in some cases see what the next steps were. And that was really inspirational to me. I, around the same time, also started leafing through various advanced math books and picked up a book, a little later, picked up a book on calculus and started flipping through it.

Used book with the cover falling apart and the pages starting to fall out. But there was a lot of accidental discovery of topics through wandering through bookstores, actually. I also went to this great magnet high school in New York City called Stuyvesant High School, where I was surrounded by people who are really interested in science and math and technology. So I think for me, that origin story know, maybe even earlier, but at least in my preteen years when, you know, I went through a process of learning new things and trying to understand them in my own way.

And the more you do that, eventually you find maybe you're understanding things in a little different way than anybody else ever did. And then pretty soon, you know, you're discovering things that no one's ever discovered before. So that's sort of how it started. Yeah. Well, I want to drill in a little bit there because you've brought to mind a couple of images. One is from a Harry Potter movie in the Half-Blood Prince where he discovers that the potion stand book. But it's all torn up and they were fighting about who didn't get that book and it turned out to be... So there's you in a bookstore, somewhere between the sci-fi and the non-fi, shall we call it. And you're kind of melding the two

together. And I love how you say, I was accidentally exposed. Sounds kind of like radiation of some kind and you've turned into a scientist. A little bit more on that. This idea of quantum, because you mentioned Albert Einstein, there's quantum physics, quantum mechanics, now quantum computing. Do these all go together? I mean, what came out of what in that initial sort of exploration with you? Where did you start getting interested in the quantum of things? Yeah. So, it definitely started with relativity, not quantum. That was the first thing I heard about.

And I would say in a lot of ways, that's the easier one. I mean, they're both, you know, those are the two big revolutions in physics in the 20th century, relativity and quantum theory. And quantum mechanics is by far, at least for me and for many people, the harder one to get your head around because it is so counterintuitive. Quantum mechanics in some sense, or quantum theory in some sense, for most of what we experience in the world is down many abstraction layers away from what we experience in the world is down, many abstraction layers away from what we experience. What I find amazing is that the people who created,

discovered in quantum mechanics, they had nothing but the equations to guide them. They didn't really understand what they were doing. They knew that there were some holes or gaps in the fundamental theory, and they kind of stumbled into these equations and they gave the right answers and they just had to follow it.

You know, I was actually just a few weeks ago, I was in Arosa, which is a small Swiss town in the Alps. That's actually the town where Schrödinger discovered Schrödinger's equation. Yeah, 100 years ago, this summer. Amazing. Yeah, so Schrö, this summer. Amazing. Yeah. So, Schrödinger suffered tuberculosis, which eventually actually killed him much later

in his life. And so, he went into the mountains for his health. For his health, yeah, to a sanatorium to recover from tuberculosis. And while he was there in Arosa, he discovered his equation. And it's a remarkable story because that equation, he didn't even know what the equation meant. He just knew, well, particles are waves and waves have wave equations, because that's ultimately Maxwell's equation. You can derive wave equations for light waves and radio waves and microwaves, x-rays.

And he said, you know, there has to be a wave equation for this thing. And this wave equation needs to somehow correctly predict the energy levels in hydrogen. And he worked out this equation and then solved it, which is, you know, for that time period not entirely trivial. And he got correctly the energy levels of hydrogen, which people had, you know, the spectra, the different wavelengths of light that hydrogen emits. And lo and behold, it works. He had no idea why, no idea what it even meant, but knew that he was on to something.

And then remarkably, other people were able to build on what he'd done, were able to say, no, there must be a grain of truth here, if not the whole story, and let's build on this, and let's make something that is richer and encompasses more and try to understand the connections between this and other things. And Heisenberg was around the same time developing his what's called matrix mechanics, a different way of thinking about quantum computing. And then people realized the connections between those, like Dirac. So it's a remarkable story how scientists took these things they understood, imposed

on it a certain level of mathematical consistency and a need for the math to predict things that you could observe. And once you had sort of the internal mathematical consistency and the math and it was correctly explaining a couple of data points about the world, you could build this huge edifice based on that. And so that was really impressive to me as I learned that. And that's 100 years ago.

It was 1925. me as I learned that. And that's 100 years ago. It was 1925. And that's quantum mechanics. And you're probably going to say, well, how does quantum computing fit into this? And that's a much later development. People spent a long time just trying to understand quantum mechanics, extend it, use it to understand more things, to understand other particles. So it was initially introduced to understand the electron, but you could understand atoms, molecules, and subatomic things, and quarks, and positrons.

So there was rich decades of development and understanding, and then eventually it got combined with relativity, at least to some extent. So there was a lot to do there to really understand and build upon the early discoveries of quantum mechanics. One of those directions, which was kicked off by Feynman around, I think, 1982, and independently by a Russian mathematician named Yuriy Manin was, okay, great. Today's computers, again, there's many abstraction layers away from anything quantum mechanical

and in fact, it's sort of separated from the quantum world by many classical abstraction layers. But what if we built a technology that didn't do that? That's a choice. And it was a choice, it was a choice that was partially forced on us just because of the scale of the things we could build. But as computers get smaller and smaller, and the way Moore's Law is heading, at some point you're going to get very close to that point at which you cannot abstract away quantum

mechanics. You must deal with quantum mechanics, and it's part and parcel of everything. You are not in the fortunate case where out of quantum theory has emerged the classical world that behaves the way we expect it to intuitively. And once we go past that, that potentially is really catastrophic and scary because you're trying to make things smaller for the sake of Moore's law and for making computers faster and potentially more energy efficient.

But if you get down to this place where the momentum and position of things, of the electrons or of the currents that you're relying on for your computation. If they're not simultaneously well-defined, how are you going to compute with that? It looks like this is all going to break down. Right, right, right. And it looks like a real crisis. But what they realized and what Feynman realized was, you know, actually it's an opportunity. It's actually not just a crisis, because if you do it the right way, then actually it

gives you way more computational power than you would otherwise have. And so, it's rather than looking at it as a crisis, it's an opportunity, and it's an opportunity to do something that would be otherwise unimaginable. Chaitin, you mentioned a bunch of names there. I have to say I feel sorry for Dr. Schrödinger, because most of what's known for to people outside your field is a cat. A mysterious cat in a box, meme after meme. But you've mentioned a number of really important scientists in the field of quantum everything. I wonder who are your particular quantum heroes? Are there any particular sort of modern day 21st century or 20th century people that have

influenced you in such a way that it's like, I really want to go deep here? Well, definitely, you know, the one person I mentioned, Feynman, who is, you know, is later, so he's the second wave, you could say of, okay, so the first wave is like Schrödinger and Heisenberg, and you could say Einstein was the leading edge of that first wave and Planck, but the second wave maybe you'd say is, I don't know, Dirac's first or second wave. You might say Dirac's second wave and potentially Landau, a great Russian physicist, second wave, and then maybe Feynman's the third wave, I guess.

I'm not sure if he's second or third wave, but anyway, he's postwar and was really instrumental in the founding of quantum computing as a field. He had a famous statement, which is, you know, in his lectures, there's always room at the bottom. And, you know, what he was thinking about there was, you can go to these extreme conditions, like very low temperatures, and in some cases, very high magnetic fields, and new phenomena emerge when you go there, phenomena that you wouldn't otherwise observe. And in a lot of ways, many of the early quantum theorists, to some extent, were extreme reductionists because they were really trying to understand smaller and smaller things and things that

in some ways are more and more basic. At the same time, some of them, if not all of them, at the same time held in their mind the idea that actually more complex behaviors emerge out of simple constituents. Einstein famously, in his miracle year of 1905, one of the things he did was he discovered the proposed theory of Brownian motion, which is an emergent behavior that relies on underlying atomic theory, but it's a, you know, it is several layers of abstraction away from the underlying atoms and molecules, and it's a macroscopic thing.

So, Schrodinger famously, among the other things, you know, he's the person who came up with the concept of entanglement. Yes. You know? In understanding his theory. And for that matter, Schrodinger's cat is a way to understand the parent paradoxes that occur when the classical world emerges from quantum mechanics. So they were thinking a lot about how these really incredible complicated things arise or emerge from very simple constituents.

I think Feynman is one of those people who really bridged that as a postwar scientist because he was thinking a lot about quantum electrodynamics and the basic underlying theory of electrons and photons and how they interact. But he also thought a lot about you know, thought about liquid helium, you know, and ultimately about quantum computing. And he was, you know, motivation for him in quantum computing was you have these complex systems with many underlying constituents, and it's really hard to solve the equation. The equations are basically unsolvable. You, you know, they're complicated equations.

You can't just sort of solve them analytically. Schrödinger was able to do that with his equation because it was one electron, one proton. Okay. But when you have, for a typical solid, you'll have Avogadro's number of electrons and ions inside something like that. There's no way you're going to solve that. And what Feynman recognized, as others did, really, coming back to Schrodinger's observation

on entanglement is you actually can't even put it on a computer and solve a problem like that. And in fact, it's not just that with Avogadro's number you can't, you can't put it on a computer and solve it with a thousand atoms. And actually, you aren't even gonna to be able to do it with 100. And when I say you can't do that on a computer, it's not that, well, data centers are getting bigger, we're going to have gigawatt data centers, and then that's the point at which you'll be able to...

No, the fact is that the amazing thing about quantum theory is if you go from let's say you're trying to solve a problem with 1,000 atoms in it, if you go to 1,001, you're doubling the size of the problem. As far as if you were to store it on a cloud, just to store the problem on the classical computer, just to store the answer, I should say, on a classical computer, you'd have to double the size. So there's no chance of getting to 100,

even if, you know, with all the build out of data centers that's happening at this amazing pace, which is fantastic and is driving all these amazing advances in AI, that build out is never going to lead to a classical computer that can even store the answer to a difficult quantum mechanical problem. Yeah, so basically in answer to the who are your quantum heroes, you've kind of given us a little history of quantum computing computing kind of the lead-up and the questions that prompted it

So we'll get back to that in one second because I want you to go a little bit further on where we are today But before we do that, you've also alluded to something that's super interesting to me Which is in light of all the recent advances in claims in AI, especially generative AI That are making claims like we'll be able to shorten the timeline on scientific discovery and things like that. Why then do we need quantum computing? Why do we need it? Great question.

So, at least, you know, AI is, AI and machine learning, at least so far, is only as good as the training data that you have for it. So if you train AI on all the data we have, and you train AI on problems we can solve, which at some level are classical, you will be able to solve classical problems. Now protein folding is one of those problems where the solution is basically classical, very complicated and difficult to predict, but basically classical and there was a lot of data on it. Yeah. Right. And so it's clearly a big data problem that's basically classical. As far as we know there's no classical

way to simulate or mimic quantum systems at scale, that there's a clean separation between the classical and quantum worlds. And so, you know, that the quantum theory is the fundamental theory of the world and there is no hidden classical model that is lurking in the background behind it and people sometimes are called these things like hidden variable theories, which Einstein actually really was hoping late in his life that there was, that there was hiding behind quantum mechanics some hidden classical theory that was just obscured from our view, we didn't know enough about it and the quantum thing was just our best approximation.

If that's true, then yeah, maybe an AI can actually discover that classical theory that's hiding behind the quantum world and therefore would be able to discover it and answer the problems we need to answer. But that's almost certainly not the case. There's just so much experimental evidence about the correctness of quantum mechanics and quantum theory and many experiments that really kind of rule out many aspects of such a classical theory that I think we're fairly confident that there isn't going to be some classical approximation or underlying theory hiding behind quantum mechanics.

And therefore, an AI model, which at the end of the day is some kind of very large matrix, a neural network is some very large classical model obeying some very classical rules about taking inputs and you produce outputs through many layers, that that's not going to produce a quantum theory. So now, on the other hand, if you have a quantum computer and you can use that quantum computer to train an AI model, then the AI model is learning, you're teaching it quantum mechanics,

and at least within a certain realm of quantum problems, it can interpolate what we've learned about the quantum mechanics and quantum problems, it can interpolate what we've learned about quantum mechanics and quantum problems to solve new problems that you hadn't already solved. Actually, like I said, in the early days I was reading these books and flipping through these bookstores and I'd sometimes figure out my own ways to solve problems different from how it was in the books and then eventually I ended up solving problems that hadn't been solved. Well, that's sort of what an AI does, right?

It trains, you know, off of the internet or off of playing chess against itself many times. You know, it learns and then takes that and eventually by learning its own way to do things, it learns things that we as humans haven't discovered yet. And it could probably do that with quantum mechanics if we're trained on quantum data. But without that, the world is ultimately quantum mechanical. It's not classical.

And so something classical is not going to be a general purpose substitute for quantum theory. Okay, Chaitin, this is fascinating. And as you've talked about pretty well everything so far, that's given us a really good sort of background on quantum history, as we know it in our time. Talk a little bit about where we are now, particularly, and we're gonna get into topology in a minute, topological stuff, but I wanna know where you feel like the science is now,

and be as concise as you can, because I really wanna get to your cool work that we're gonna talk about. And this question includes, what's a Majorana and why is it important? So, okay, unfortunately it won't be that concise an answer. Okay, so, you know, early 80s, ideas about quantum computing were put forward. But I think most people thought, A, this is going to be very difficult, you know, to do.

And I think it wasn't clear that there was enough motivation. You know, I think Feynman said, yes, if you really want to simulate quantum systems, you need a quantum computer. And I think at that point, people weren't really sure, hey, is that the most pressing thing in the world, simulating quantum systems? It's great to understand more about physics, understand more about materials, understand more about chemistry,

but we weren't even at that stage. I think that's the limiting thing that's limiting progress for society. And then secondly, there was also this feeling that what you're really doing is some kind of analog computing. This doesn't feel digital, and if it doesn't feel digital, there's this question about error correction

and how reliable is it gonna be. So Peter Shor actually did two amazing things, one of which is a little more famous in the general public, but one of which is probably more important technically, is he did these two amazing things in the mid-90s. He first came up with Shor's algorithm, where he said if you have a quantum computer, yeah, great for simulating quantum systems,

but actually you can also factor large numbers. You can find the prime factors of large numbers. And the difficulty of that problem is the underlying security feature under RSA. And many of these public key cryptography systems rely on some certain types of problems that are really hard. It's easy to multiply two large primes together and get the output, and you can use that to encrypt data.

But to decrypt it, you need to know those two numbers and it's hard to find those factors. What Peter Shor discovered is that ideally, a quantum computer, an ideal quantum computer would be really good at this. So that was the first discovery. And at that point, what seemed at the time an academic problem of simulating quantum systems, which seemed like, you know, in Feynman's vision, that's what quantum computers are for, that seemingly academic problem all of a sudden also, it turns out there's this very important, both financially and economically and national security-wise, other application of a quantum computer.

And a lot of people sat up and took notice at that point. So that's huge. But then there's a second thing that he discovered, which was quantum error correction. Because everyone when he first discovered said, sure, ideally that's how a quantum computer works, but quantum error correction, this thing sounds like an analog system, how are you going to correct errors? This thing will never work because it'll never operate perfectly. Schrodinger's problem with the cats going to happen is that you're going to have entanglement,

the thing is going to just end up being basically classical and you lose all the supposed gains you're getting from quantum mechanics. And quantum error correction, that second discovery of Peter Shor's really suddenly made it look like, okay, at least in principle, this thing can happen. And people built on that. Peter Shor's original quantum error correction, I would say, it was based on a lot of ideas from classical error correction, because you have the same problem with classical communication and classical computing.

Alexei Kataev then came up with a new set of quantum error correction procedures, which really don't rely in the same way on classical error correction. Or if they do, it's more indirect, in many ways rely on ideas in topology and physics. And those ideas, which lead to quantum error correcting codes, but also ideas about what kind of underlying physical systems would have built-in hardware error protection, led to what we now call topological quantum computing and topologic qubits, because it's this idea that, you know,

just like people went from early days of computers from vacuum tubes to silicon, actually initially germanium transistors and then silicon transistors, that similarly, that you had to have the right underlying material in order to make qubits. And that the right underlying material platform, just as for classical computing it's been silicon for decades and decades, it was going to be at one of these so-called topological

states of matter. And that these would be states of matter whose defining feature, in a sense, would be that they protect quantum information from errors, at least to some extent. Nothing's perfect, but in a controllable way so that you can make it better as needed, and good enough that any subsequent error correction that you might call software level error correction would not be so cumbersome and introduce so much overhead as to make a quantum computer impractical. I would say there were these, the field had a reboot or rebirth in the mid 1990s and pretty

quickly those ideas in addition to the applications and algorithms coalesced around error correction and what's called fault tolerance. And many of those ideas came freely interchanged between ideas in topology and the physics of what are called topological phases and gave birth to this, I would say, to the set of ideas on which Microsoft's program has been based, which is to look for the right, to create the right material and qubits based on it so that you can get to a quantum computer at scale. Because there's a number of constraints there and the work that we're really excited about right now is about getting the right material and harnessing that material for qubits. Well, let's talk about that in the context of this paper that you're publishing and some

pretty big news in topology. You just published a paper in Nature that demonstrates with receipts a fundamental operation for a scalable topological quantum computer relying on, as I referred to before, Majorana zero modes. That's super important. So tell us about this and why it's important. Yeah, great. So you know building on what I was just saying about having the right material, what we're relying on is super, to an extent, is superconductivity. So that's one of the, you know, really cool, amazing things about the physical world that

many metals, including aluminum, for instance, when you cool them down, they're able to carry electricity with no dissipation, no energy loss associated with that. And that property, the remarkable, that property, what underlies it is that the electrons form up into pairs, these things called Cooper pairs. And those Cooper pairs, their wave functions kind of lock up and go in lockstep, and as a result, actually, the number of them fluctuates wildly you know in any low in any place locally and that enables them to you know to move easily

and carry current. But also a fundamental feature because they form pairs is that there's a big difference even in odd number of electrons because it's an odd electron that actually there's some electron that's unpaired somewhere and there's an energy penalty associated energy cost to that. It turns out that that's not always true. There's actually a subclass of superconductors called topological superconductors or topoconductors as we call them.

And topoconductors have this amazing property that actually they're perfectly okay with an odd number of electrons. In fact, when there's an odd number of electrons, there isn't any unpaired electron floating around. But actually, topological supernumbers, they don't have that. That's the remarkable thing about it. I've been warned not to say what I'm about to say, but I'll just go ahead and say it anyway.

I guess that's a bad way to introduce something. No, it's actually really exciting. Okay. But since you brought up Harry Potter and the Half-Blood Prince, Voldemort famously split his soul into seven, or I guess technically eight, accidentally. But he split his soul into seven horcruxes, so there was no place where you could say, well, that's where his soul is. Oh my gosh.

So Myron and Zero modes do kind of the same thing. Like there's this unpaired electron potentially in the system, but you can't find it anywhere. Because to an extent, you've actually figured out a way to split it and put it, you know, sometimes we say like you put it at the two ends of the system, but that's sort of a mathematical construct. The reality is there is no place where that unpaired electron is. That's crazy. Tell me, before you go on, we're talking about Majorana.

I had to look it up. That's a guy's name, right? Yeah. So do a little dive into what this whole Majorana zero mode is. Yeah. Yeah. So, Majorana was a Italian physicist, or maybe technically Sicilian physicist.

He was very active in the 20s and 30s, and then just disappeared mysteriously around 1937, 38, around that time. Wow. So, no one knows exactly what happened to him. You know, but one of his last works, which I think may have only been published after he disappeared, he proposed this equation called the Majorana equation.

And he was actually thinking about neutrinos at the time and particles, subatomic particles that carry no charge. So he was thinking about something very, very different from quantum computing, actually. So Majorana didn't know anything about quantum computing, didn't know anything about topological superconductors, maybe even didn't know much about superconductivity at all, was thinking about subatomic particles. But he wrote down this equation for neutral objects or some things that don't carry any charge. And so, when people started in the 90s and 2000s looking at topological superconductors,

they realized that there are these things called Myron and Zura modes. So as I said, and let me sort of explain how they enter the story. So Myron and Zura modes are, I just said that topological superconductors, there's no place you can find that even or odd number of electrons, there's no penalty. Now superconductors, they do have a penalty and it's called the energy gap for breaking a pair, even topological superconductors, you take a pair,

Cooper pair, you break it, you have to pay that energy cost. And it's like, it's double the energy in a sense of having an unpaired electron, because you've created two unpaired electrons and you break that pair. having an unpaired electron, because you've created two unpaired electrons and you break that pair. Now, somehow a top-lodged superconductor

has to accommodate that unpaired electron. It turns out the way it accommodates it is it can absorb or emit one of these at the ends of the wire. If you have a top-lodged superconductor wire, at the ends it can absorb or emit one of these things. And once it goes in to one end, then it's totally delocalized over the system

and you can't find it anywhere. You can say, oh, we got absorbed at this end, and you can look and there's nothing you can tell. Nothing has changed about the other end. It's now a global property of the whole thing that you actually need to somehow figure out, and I'll come to this, I'm gonna figure out how to connect the two ends

and actually measure the whole thing collectively to see if there's even even odd number of electrons, which is why it's so great as a qubit, because the reason it's hard for Schrodinger's cat to be both dead and alive is because you're gonna look at it and then you look at it, photons are gonna bounce off and you're gonna know if it's dead or alive. And the thing is, the thing that was slightly paradoxical

is actually a person doesn't have to perceive it. There's anything in the environment that, if a photon bounces off, it's sort of like if a tree falls in the forest. I was just going to say that. It still makes a sound. I know. It still makes a sound in the sense that, you know, a short-ears cat is still going

to be dead or alive once a photon or an air molecule bounces off it because of the fact that it's gotten entangled with effectively the rest of the, you know, many other parts of the universe at that point. And so the fact that there is no place where you can go and point to that unpaired electron means it does that even or oddness, which you call parity, where there's something even or odd is parity, and these are wires with 100 billion electrons in them. And it's a difference between 100 million and 100 million and one, because one's even an odd number. And that difference, you have to be able to, like the environment can't detect it, so it

doesn't get entangled with anything. And so it can actually be dead and alive at the same time, unlike Schrodinger's cat. And that's what you need to make a qubit, is to create those superpositions. Okay. And so, Myronos zero modes are these features of the system that actually don't actually carry an electrical charge. But they are a place where a single unpaired electron can enter the system and then disappear. And so, they are this remarkable thing where you can hide stuff.

So how does that relate to your paper and the discoveries that you've made here? Yeah, so in an earlier page, so now the difficulty is you have to actually make this thing. So, you know, you put a lot of problems up front is that you're saying, okay, the solution to our problem is we need this new material and we need to harness it for qubits, right?

Great, well, where are we going to get this material from? Right, you might discover it in nature, nature may hand it to you, but in qubits, right? Great. Well, where are we going to get this material from? You might discover it in nature. Nature may hand it to you. But in many cases, it doesn't. And this is one of those cases where we actually had to engineer the material. And so engineering the material is,

it turns out, to be a challenge. People had ideas early on that they could put some combination of semiconductors and superconductors. But for us to really make progress, we realized that it's a very particular combination. And we had to develop and we did develop simulation capabilities, classical. Unfortunately, we don't have a quantum computer, so we had to do this classically with classical computers.

We had to classically simulate various kind of materials combinations to find one or find a class That would get us into the topological phase and it turned out lots of details mattered there. Okay, it involves a semiconductor Which is indium arsenide that's a it's not silicon and it's not the second most common semiconductor It's gallium arsenide or gallium nitride, which is used in LED lights. It's something called indium arsenide It has some uses as an infrared detector, but it's a different semiconductor, and we're using it in a non-standard way, putting it into contact with aluminum and getting kind of the best of both worlds of a superconductor and a semiconductor so that we can control it and get into this topological phase.

And that's a previously published paper in the American Physical Society Journal. But that's great. So that enables, that shows that you can create this state of matter. Now we need to then build on it, we have to harness it, and we have to, as I said, we have to make one of these wires, or in many cases multiple wires, qubits, etc., complex devices, and we need to figure out how do we measure whether we have 100 million or 100 million of one electrons in one of these wires?

And that was the problem we solved, which is we made a device where we took something called a quantum dot, you should think of as a tiny little capacitor, and that quantum dot is coupled to the wire in such a way that the coupling, that an electron, it's kind of remarkable, an electron can quantum mechanically tunnel from, you know, this is that an electron, it's kind of remarkable, an electron can quantum mechanically tunnel from, this is like an electron, you don't know where it is at any given time, its momentum and its position aren't well defined, so it's an electron

whose let's say energy is well defined, actually there is some probability amplitude that it's on the wire and not on the dot. Even though it should be on the dot, it actually can kind of leak out or quantum mechanically end up on the wire and come back. And because of that fact, the simple fact that its quantum mechanical wave function can actually have it be on the wire, it actually becomes sensitive to that even or oddness. Interesting.

And that causes a small change in the capacitance of this tiny little parallel plate capacitor effectively that we have. And that tiny little change in capacitance, which is just put in numbers, the femtofarad, so that's a decimal point followed by 14 zeros and a one, so that's how tiny it is. That tiny change in the capacitance, if we put it into a larger resonant circuit, then that larger resonant circuit shows a small shift in its resonant frequency, which we can detect. And so what we demonstrated is we can detect the difference,

that one electron difference, that even or oddness, which is, again, it's not local property of anywhere in the wire that we can nevertheless detect. And that's kind of the fundamental thing you have to have if you want to be able to use these things for quantum information processing, this parity. You have to be able to measure what that parity is.

That's a fundamental thing because ultimately the information you need is classical information. You're going to want to know the answer to some problem. It's going to be a string of zeros and ones. You have to measure that. But moreover, the particular architecture we're using, the basic operations for us are measurements of this type, which is a very digital process. I mentioned sort of how quantum computing looks a little analog in some ways, but it's

not really analog. Well, that's very manifestly true in our architecture that our operations are a succession of measurements that we turn on and off, but different kinds of measurements. And so what the paper shows is that we can do these measurements, we can do them fast, we can do them accurately. And the additional announcements that we're making right now are work that we've done extending

and building on that with showing additional types of measurements, a scalable qubit design, and then building on that to multi-qubit arrays. So that really unlocked our ability to do a number of things, and I think you can see the acceleration now with the announcements we have right now. So Chetan, you've just talked about the idea of living in a classical world and having to simulate quantum stuff. And tell us about the full stack here and how we go from, in your mind, from quantum computing

at the bottom all the way to the top. Okay, so one thing to keep in mind is quantum computers are not a general purpose accelerator for every problem. You know, so people sometimes say, well, quantum computers are just gonna be like class computers but faster. And that's not the case. So I really want to emphasize the fact that quantum computers are an entirely different

modality of computing. They, you know, there are certain problems which quantum computers are not just faster at than class computers, but quantum computers can solve and classical computers have no chance of solving. On the other hand, there are lots of things that classical computers are good at that quantum computers aren't gonna be good at,

because it's not gonna give you any big scale up. Like a lot of big data problems where you have lots of classical data. You know, a quantum computer with, let's say, let's call it 1,000 qubits, and here I mean 1,000 logical qubits, and we come back to what that means but a thousand error corrector qubits can Solve problems that you have no chance of solving with a class computer even with all the world's computing But in fact if it were a thousand qubits you would have to take every single atom in the entire universe

Okay, and turn that into a transistor and it still wouldn't be big enough. You don't have enough bytes even if every single atom in the universe were a byte. So that's how big these quantum problems are when you try to store them on a classical computer just to store the answer, let's say. But conversely, if you have a lot of classical data, like all the data on the internet which we train our AI models with, you can't store that on a thousand qubits. You actually can't really store more than a thousand bits of classical information on

a thousand qubits. Many things that we have big data in classically, we don't have the ability to really truly store within a quantum computer in a way that you can do anything with it. So we should definitely not view quantum computers as replacing class computers. There's lots of things that class computers are already good at and we're not trying to do those things. But there are many things that class computers are not good at at all. Quantum computer, we should think of as a complementary thing and an accelerator for

those types of problems. It will have to work in collaboration with a classic computer that is going to do the classical steps, and the quantum computer will do the quantum steps. So, that's one thing to just keep in mind when we talk about a quantum computer. It is part of a larger computing framework where there are many classical elements. It might be CPUs, it might be GPUs, it might be custom ASICs for certain things, and then quantum computer, you know, quantum processor as well.

Is that called a QPU? A QPU is a quantum processing unit, exactly. You know, so we'll have CPUs, GPUs, and QPUs. And so that is, you know, at the lowest layer of that stack is the underlying substrate, the physical substrate, and that's our topoconductor. It's the material which we build our QPUs. That's the quantum processing unit.

The quantum processing unit includes all of the qubits that we have in our architecture on a single chip. That's kind of one of the big key features, key design features that the qubits be small and small and manufacturable on a single wafer. And then the QPU also has to enable that quantum world to talk to the classical world. Because you have to send it instructions and you have to get back answers.

And for us that is turning on and off measurements because our instructions are a sequence of measurements. And then we ultimately have to get back a string of zeros and ones. But that initially is these measurements where we're getting phase shifts on microwaves, which are in turn telling us about small capacitance shifts, which are in turn telling us the parity of electrons in a wire.

So really, this is a quantum machine in which you have the qubits that are built on the quantum plane. You've then got this quantum classical interface where the classical information is going in and out of the quantum processor. And then there's a lot of classical processing that has to happen, both to enable error correction and to enable computations. The whole thing has to be inside of a cryogenic environment. It's a very special environment in which A, this is kept cold because that's what you need in order to have a topoconductor, and that's also what you need in order just in

general for the qubits to be very stable. So that, when we talk about the full stack in general for the qubits to be very stable. So that when we talk about the full stack, just on the hardware side, there are many layers to this. And then of course, there is the classical firmware that takes instructions and turns them into the physical things that need to happen. And then of course we have algorithms and then ultimately applications. Yeah, so I would say, Chayden, that people can probably go do their own little research on how you go from temperatures that are lower than deep space to the room you're working

in and we don't have time to unpack that on this show. And also, I was going to ask you what could possibly go wrong if you indeed got everything right. And you mentioned earlier about what happens in an AI world if we get everything right. If you put quantum and AI together, it's an interesting question what that world looks like. Can you just take a brief second to say that you're thinking about what could happen to cryptography, to just all kinds of things that we might be

wondering about in the post-quantum world? Great question. So first of all, one of the things I want to emphasize is ultimately a lot of, when we think about the potential for technology, often the limit comes down to physics. There are physics limits. If you think about interstellar travel and things like that, well the speed of light is kind of a hard cut off, and actually you're not gonna be able to go faster

in speed light and you have to bake that in. That ultimately, if you think of a data center, ultimately there's a certain amount of energy and there's a certain amount of cooling power you have and you can say, well this data center's 100 megawatts and then in the future we'll have a gigawatt to use it. But ultimately then that energy has to come from somewhere and you've got some hard physical constraints.

So similarly, you could ask with quantum computers what are the hard physical constraints? What are the things that just, because you can't make a perpetual motion machine, you can't violate, I think, laws of quantum mechanics. And I think in the early days, there was this concern that, you know, this idea relies on violating something. You're doing something that's not going to work.

You know, I'd say the theory of quantum error correction, the theory of fault tolerance, you know, many of the algorithms have been developed, they really do show that there is no fundamental physical constraint saying that this isn't gonna happen. Somehow you would need to have either more power than you can really generate or you would need to go much colder than you can actually get, that there's no physical no-go result. So that's an important thing to keep in mind. Now the thing is, some people might then be tempted to say, well, okay, now it's just

an engineering problem because we know this in principle can work and we just have to figure out how to work. But the truth is, there isn't any such hard barrier where you say, well, up until here it's fundamental physics and then beyond this it's just an engineering problem. The reality is new difficulties and challenges arise every step along the way. One person might call it an engineering or an implementation challenge or one person may call it a fundamental, you know, a barrier or obstruction. And I think that's a, people will probably profitably disagree, you know, agree to disagree

on like where that goes. I think for us, like it was really crucial, you know, as we look at a scale to realize quantum computers are going to really make an impact. We're going to need thousands, you know, hundreds to thousands of logical qubits. That is error corrected qubits. And when you look at what that means, that means really million physical qubits. That is a very large scale in a world in which people have mostly learned what we know about these things from 10 to 100 qubits.

To project out from that to a million, you know, it would surprise me if the solutions that are optimal for 10 to 100 qubits are the same solutions that are optimal for a million qubits. And that has been a motivation for us, is let's try to think, based on what we now know, of things that at least have a chance

to work at that million qubit. Let's not do anything that looks like it's gonna clearly hit a dead end before that. Now obviously in science nothing is certain and you learn new things along the way but we didn't want to start out with things that looked like they were not gonna be, work for million cubits.

That was the reason that we developed this new material, that we created this, engineered this new material, these topo conductors, precisely because we said we need to have a material that can give us something where we can operate it fast and make it small and be able to control these things. So that, you know, I think that's one key thing. And, you know, what we've demonstrated now is that we can harness this, that we've got a qubit.

And that's why we have a lot of confidence that, you know, these are things that aren't gonna be decades away, that these things are going to be years away. And that was the basis for our interaction with DARPA. We've just been signed a contract with DARPA to go into the next phase of the DARPA US2QC program. And DARPA, the US government wants to see a fault tolerant quantum computer because they do not want any surprises. There are people out there who said, quantum computers are decades away, don't worry about

it, but I think the US government realizes they might be years, not decades away, and they want to get ahead of that. And so that's why they've entered into this agreement with us and the contract with us. And so that is the thing I just want to make sure that, you know, listeners to the podcast understand that we are, you know, the reason that we fundamentally re-engineered, re-architected what we think a quantum computer should look like and what the qubit should be and even the under, going all the way down to the underlying materials was, which is high risk, right? I mean, there was no guarantee that any of to the underlying materials was, which is high risk.

I mean, there was no guarantee that any of this is gonna work, A, and B, there was no guarantee we would even be able to do the things we've done so far. I mean, that's the nature of it. If you're gonna try to do something really different, you're gonna have to take risks, and we did take risks, by really starting at the ground floor and trying to redesign and re-engineer these things.

So that was a necessary part of this journey and this story was for us to re-engineer these things in a high-risk way. What that leads to is potentially changing that timeline. And so in that context, it's really important to make this transition to post-quantum crypto, because the cryptography systems in use up until now are things that are not safe for quantum attacks if you have a utility-scale quantum computer. We do know that there are cryptosystems which, at least as far as we know, appear to be safe from quantum attacks.

That's what's called post-quantum cryptography. They rely on different types of hard math problems, which quantum computers aren't probably good at. And changing over to a new crypto standard isn't something that happens at the flip of a switch. It's something that takes time. You know, first, you know, early part of that was based around the National Institutes of Standards and Technology aligning around one or a few standard systems that people would implement which they certified would be quantum safe.

And you know, those processes have occurred. And so, you know, now is the time to switch over. Given that we know that we can do this and that it won't happen overnight, now is the time to make that switch. And we've had several cryptographers on the show who've been working on this for years. It's not like they're just starting. They saw this coming even before you had some solidity in your work. But listen, I would love to talk to you for hours, but we're

coming to a close here. And as we close, I want to refer to a conversation you had with distinguished university professor Sankar Dasarma. He suggested that with the emergence of Mayurana Zero Modes, you had reached the end of the beginning and that you were now sort of embarking on the beginning of the end in this work. Well, maybe that's a sort of romanticized vision of what it is, but could you give us a little bit of a hint on what are the next milestones on your road to a scalable, reliable quantum computer and what's on your research roadmap to reach them? Yeah, so interestingly we actually just also posted on the archive paper that shows

some aspects of our roadmap, kind of the more scientific aspects of our roadmap. And that roadmap is kind of continuously going from the scientific discovery phase through the engineering phase, okay. Again, as I said, it's a matter of debate and even taste of what exactly you want to call scientific discovery versus engineering, which will be hotly debated, I'm sure. But it is definitely a continuum that's going more from one towards the other. And I would say at a high level, logical qubits, error corrected reliable qubits are the basis of quantum computation at scale. And developing, demonstrating, and building those logical qubits and logical qubits at

scale is kind of a big thing that for us and for the whole industry is, I would say, is sort of the next level of quantum computing. You know, Jason Zander wrote this blog where he talked about level one, level two, level three, where level one was this NISC, noisy intermediate scale quantum era. Level two is foundations of reliable and logical qubits, and level three is the at scale logical qubits. I think we the, at scale, logical qubits, I think we're heading towards level two. And so, in my mind, that's sort of the,

the next North Star is really around that. I think there will be a lot of very interesting and important things that are more technical and maybe are not as accessible to a big audience. But I'd say that's kind of the, I would say, if you're, you know, a thing to keep in mind as a big exciting thing happening in the field. Yeah. Well, Chetan Nayak, what a ride this show has been. I'm going to be watching this

space and the timelines thereof because they keep getting adjusted. Thank you for taking time to share your important work with us today. Thank you very much. My pleasure.