Will Quantum Computing Kill Bitcoin? | Scott Aaronson & Justin Drake

If let's say there's only a few entities in the world that have scalable quantum computers, right? That allows those entities to mine a lot more Bitcoin than everyone else. Now, eventually, if you got to a world where, you know, just about everyone had access to a quantum computer, then it's kind of amusing what would happen.

Welcome to Bankless, where we explore the frontier of internet money and internet finance. And today we're exploring the frontier of quantum computing and its effect on our internet money. What's it gonna do? Our quantum computer is gonna take all of our Bitcoin? This is Ryan Sean Adams. I'm here with David Hoffman, and we're here to help you become more bankless. Guys, special episode. It's divided into multiple parts, I would say. The first part, we have Scott Aronson on the podcast. He is a theoretical computer scientist. He is uh foremost expert in quantum computing. We also have Justin Drake on the podcast for part of the first part, and he asks Scott Aronson some questions as well, particularly about the effect of quantum computing and our cryptocurrencies like Bitcoin and Ethereum. Now, because the subject matter goes very deep in quantum fundamentals, you might feel bankless listener like you're hanging by the seat of your pants, just trying to keep up with these big brains and some of the ideas propelled forward. So never fear. We have a final part of the podcast, a third part of the podcast, where it's just David, myself, and Justin Drake. And what we do is we try to synthesize everything we've learned. And that for me was one of my favorite parts of the episode because it was taking everything big brain that Scott Aronson said and applying it directly to Ethereum and Bitcoin, what could happen in the crypto sphere. So three parts to this episode, and you guys are welcome to skip to one of those parts if you get too lost in the weeds at certain sections.

Yeah, I would say the first part of this episode are two high schoolers asking a PhD about quantum computing and trying to get that PhD to really put it into simple terms. And I think I thought we did okay there. If you use 100% of your brain power, bankless listener, I think you'll kind of catch a vibe, you'll catch a direction for it. But it does get pretty technical pretty quickly. And then when Justin takes over, it starts to focus more and more on how this relates to crypto. And so the way that this podcast starts is, you know, what is quantum computing? How is it different? How does it work? How does it change and impact the world? And then as we move progress further into this podcast, it's how is this going to impact our backs? What's going to have to change? What are we going to have to change in Ethereum? How is Bitcoin going to have to navigate these changes? Which is an even more difficult conversation that I'm less optimistic about. And overall, I just learned a lot. It's an honor to have Scott on the podcast. He's a big deal Chad in this space of quantum computing. And I would also say how quantum computing relates to crypto is going to be kind of a microcosm for how it impacts the rest of society. Crypto is not the only industry that is going to be impacted by this. The rest of the world is going to be impacted by this. And like other examples, I think crypto is going to be a little bit of a spearhead, a canary in the coal mine. Because we're going to tackle this first because we see it coming and we're futurists and we pay attention to stuff like this, which is why we are doing this podcast.

Yeah, we are. And it's actually a bigger deal than I thought going in. Like it will have a more fundamental impact on cryptocurrencies than I thought going to this episode. So, guys, we appreciate it. Let's get right into the episode with Scott Aronson and Justin Drake. Thankless Nation, I am honored to introduce you to Scott Aronson. He is a theoretical computer scientist, and he's a chair at the University of Texas at Austin, where he directs the Quantum Information Center. He's an expert in quantum. And over the last two years, he was actually on leave. He was working on AI safety at OpenAI. So it's safe to say we have an expert in at least two domains of interest today quantum computing and AI. Scott, welcome to Bankless.

Well, thanks so much. It's great to be here.

Joining us because this is kind of an intimidating subject matter for David and I. We're gonna need help. We've got Justin Drake, you know Justin from the Ethereum Foundation as well. He's going to serve as technical co-host for a portion of this conversation. Justin, how are you doing?

Doing great. Thanks for having me and a real honor to be on a podcast with Scott.

Great to see you, Justin.

Yeah, it's great to have Scott interacting with the crypto community because we have a you know quantum intersecting crypto here, and that's kind of the genesis for this conversation. I think David and I have a simple goal for this episode, which is just to get crypto people up to speed on quantum computing, because I feel like we just don't know enough right now. We've heard the scary news that quantum might be used at some point in the future to break our cryptography and to steal our cryptocurrency, so that's kind of scary. And so what I want to do for bankless listeners is break this into two parts. Part one will be what I call the kind of the little brain questions. That's for David and myself. We're gonna ask you about the quantum 101, kind of the popular beliefs about quantum, make sure we have a good grounding and foundation. And then part two, Justin's going to lead. That's more the big brain side of things where you guys can talk about cryptography, quantum. Will this break Bitcoin? Will this break Ethereum? And if so, how?

Do our best to keep up.

Yes, thank you. You guys ready for this? Perfect. I got the head shake acknowledgement, which is just as good as the verbal. Let's get into the small brain, Quantum 101. Okay, so there was this thing that happened about a month ago. This was early in December. The CEO of Google tweeted something out. Sundar, the CEO of Google, he said, Willow, our new state-of-the-art quantum computing chip with a breakthrough that can reduce errors exponentially as we scale up using more qubits, cracking a 30-year challenge in the field. So introducing a new state-of-the-art quantum computing chip, Willow. And this, I think, broke mainstream news. It broke into crypto and started us talking once again about quantum computing and how it might affect cryptocurrency moving forward. So there's a lot of worries around this. I want to start the question with maybe this tweet the Google Willow chip. Is this a major breakthrough from your perspective? I mean, you've been working in quantum for 20 years. How big of a deal is this?

I mean, I would call it an engineering milestone. So it's not that it overturns anything that was previously believed or, you know, represents some great new discovery. I mean, this is stuff that, as theorists, you know, was predicted in the nineteen nineties, right? That once you get qubits that you can act on with a low enough error rate, then you can do these very clever quantum error correcting codes, right, that will protect your underlying logical qubits sort of even better than the physical qubits are being protected. And in principle, you could then preserve encoded qubits for arbitrary amounts of time. So this is a theory that's been in place since 1996 or so. But what's exciting is that like 30 years later, we are only now finally starting to experimentally demonstrate some of these predictions. So the milestone that Google announced in December was actually a paper that they had online since the summer. So it was sort of old news to us by the time that Google announced it in December. But you know, they have now built a chip with like 103 physical qubits, I think. That's what Willow is. Okay, it's superconducting qubits, you know, arranged in roughly like a 10 by 10 grid. And they use them to implement something called the surface code, okay, which is a quantum error correcting code. Again, as theorists, we've known about since 1997. Okay. But for the first time, they're doing it in a way where as they scale to larger and larger surface codes. So like from a three by three array to a five by five to a seven by seven and so forth, they are preserving an encoded qubit for longer and longer amounts of time. Right. So they've passed the threshold where going to a larger code gives you more and more of a net win. You know, it's kind of like the Fermi pile in 1942, you know, past the threshold where, you know, each nucleus decaying is causing more nuclei to decay, right? This is this is some kind of important threshold, right? So now, you know, it's still not good enough to do, you know, a full scalable, you know, fault-tolerant quantum computation. I mean, for one thing, you know, we're only talking for now about one encoded qubit, right, that is just sort of sitting there, right? You know, a next step would be to build multiple encoded qubits, have them interact with each other. So that hasn't been done yet with encoded qubits of this quality. And, you know, if you really wanted to, I mean, we'll get into this later, but if you really wanted to break cryptographic codes, then you'd probably be talking about millions of physical qubits in, you know, possibly in hundreds or thousands of dilution refrigerators, you know, all with interconnects. So long story short, we're not there yet. Okay, but you know, this is an important milestone, something that theorists talked about since the 90s. And it is exciting that just within the last year, you know, we've seen that cross, you know, and you know, there have been skeptics of quantum computing who have, you know, I think, you know, firmly predicted that, you know, we would never get this far.

Right. That, you know, like we don't really understand quantum mechanics itself, or you know, there are sort of sources of correlated noise that violate the assumptions of the theory of quantum fault tolerance. And, you know, when we try to build this, we're gonna see that it's gonna make quantum computing impossible. You know, and we haven't seen any sign of any of that. Right? You know, everything seems to be working just like the theory in the 1990s said it would. So I would say that's the main upshot.

Well, that does seem significant from the perspective of kind of the theories being worked out now in engineering. And so this is an engineering milestone, as you said. So a big question then is like, how much will this accelerate moving forward? Right. And are there any analogs? I mean, are we looking at kind of the transistor and and Moore's Law? Are we looking at something as explosive as AI, which just seemed to you like we went from transformers and then suddenly there was you know GPT, and now we're seeing monumental gains? Like, how fast could this accelerate moving forward?

Yeah, I mean you can always try to look for historical analogies, right? I do that as well. I do it all the time. It's also hazardous, right? Because each situation is not quite the same as the previous ones, right? In this case, you know, I think my main caution would be, you know, some people just, you know, they hear all these exciting things about quantum computing and they expect that, okay, then this must just be the next frontier that is going to replace all of our existing computers, right? It will just revolutionize everything. And you know, the hard part with a quantum computer is that you know, in order for it to be useful, you have to beat a classical computer.

Right. Classical computers already exist. They are, you know, one of the triumphs of civilization. Okay. And we can get into this later, but it is mostly for certain very special tasks that we know how to get a huge advantage with a quantum computer over a classical one. Okay. And for many, many other tasks, many, you know, I'd say the majority of what we do with our computers on a day-to-day basis, a quantum computer would probably help you little or not at all. You could use a quantum computer to check your email or to uh play Candy Crush, but it would be like using the space shuttle to taxi people around the parking lot. Right. It would just not make sense. Okay. So, you know, you really have to look at these specific applications where a quantum computer promises an improvement, right? And even once you know we achieve the full promise of quantum computing, I mean, those are, you know, I think it's going to be certain specific industries where we're mostly going to see the effect. Okay, so that's, I think, the first thing for people to understand, and that really differentiates this from AI, for example. I like to say that the difference is with AI, you don't have to, you know, beat anything that humans can do. It is enough to achieve parity with a mediocre human. And that already changes the world.

Right. With quantum computing, you really have to beat classical computing. Right. And it's a miracle that that ever happens. But, you know, it's mostly for certain specific problems where it does. Okay. So, you know, the types of problems where quantum computers can help or not help, you know, that we can discuss in as much detail as you like. Right. Because in some sense, we know a great deal about that. And the timeline, how long this is going to take, that we know less about, right? Or rather, you know, if I did know a lot about that, then I wouldn't be a professor. I would be an investor.

So, you know, all I can do is just sort of, you know, look at scatter plots, you know, look at, you know, what promises were made over the last 20 years by, you know, the various quantum computing efforts and how on track are they in delivering on those promises. And if you look at that, what you see is that, well, it seems like we have come an incredible distance since where we were when I entered this field in the late 1990s. It's been more than 20 years now, right? But in the 90s, it would have been amazing to get just two qubits to talk to each other with, say, 50% fidelity, you know, 50% accuracy, right? And then, you know, we knew that, okay, if you could get that really, really close to one, like to, you know, 99.999% or something like that, then quantum error correction starts to kick in. And then you can push the effective error all the way down to zero. But you know, that just seemed like so far off from where people were. Okay, but you know, over 25 years, what happened was that that 50% fidelity became 90%, became 99%. And now in you know, the latest systems, such as you know, those of Google or Quantinuum or Quera, you know, it's 99.8% or 99.9%. And in the meantime, the quantum error correction methods have also improved, right, so that they can cope with larger amounts of error. And so we are now at or very, very near the threshold where in principle quantum error correction does become a net win as you scale up. Okay, so you know, that's not to downplay the sort of enormity of the engineering work that is ahead of people, right? But, you know, if you just look at the error rates, right, as a function of time, you know, that looks pretty good.

Mm-hmm.

Right. And it looks like if people wanted this badly enough and were willing to spend enough money, right, I certainly can't rule out that, you know, within the next decade that they could, you know, get useful quantum advantages. I mean, you know, it it's sort of like, you know, asking a nuclear physicist in the nineteen thirties, right, you know, how long until we're going to get a critical man. Right. And like Niels Bohr, for example, was asked that question. And he said, well, it's not going to happen for, you know, in any foreseeable future because you would have to convert an entire country into a uranium enrichment factory, basically. Right. It's just fanciful. Right. And then, you know, apparently like in 1943, he toured the Manhattan Project. And then he said, Well, I see that that's what you've done. Wow. So, you know, at some point it just becomes a question of, you know, how much is someone willing to spend? You know, how badly do they want this? Right. And so the time frames, you know, depend on all sorts of things that, you know, I, as a theoretical computer scientist, you know, am not.

Able to predict very well. Okay. But, you know, we'll get into this shortly, but I would certainly say that, you know, people who have encrypted data that they want to stay secret for the next decade. Yeah, you know, if I were such a person, then I would probably already be, you know, looking to migrate to post quantum or quantum resistant methods of encryption.

Well, I think that really helps us place ourselves in history as it relates to this quantum arc development. We are somewhere in the inflection point of going from research and theory into practicality, and it's kind of just becoming a matter of time of willpower and expense. And Scott, I do kind of want to return back to something you were saying earlier about the differences between quantum computing and classical computing, because I think this is really the first big aha moment that I want listeners to really integrate into their brains. The metaphor that I've had to understand this for me personally that I think worked very well is trying to get people out of the idea that quantum computers is not just a faster classical computer. Yes. For example, you know, there's an arc of automobiles that we can say. First we had the Model T Ford, and now we have, you know, Ferraris and Toyotas that work very well and they're very dependable. And that's a coherent directional arc of progress of that technology.

I mean the speed hasn't really increased all that much, certainly not exponentially, but

Right.

Yeah.

Yes.

They certainly look sleeker.

But what we're not doing with quantum computing is we're just making a better classical computer.

Right.

It's much more like something where we're actually making a boat and we're going off into a different frontier that cars were not able to explore or navigate. Doesn't matter how good the engine you made and put it into a car, it's not going to help you on water. And what quantum computing is like, well, we're actually changing the shape of the frontier that we're navigating. We're going into a different uncharted land. And now we are able to explore a different field of mathematics, and there's different applications, there's different utility out there. That was a really helpful metaphor for me. Maybe you can extend that metaphor and run with that and help explain that a little bit.

Yeah, I mean like most metaphors, that one has both good and bad to it. Right.

Mm-hmm.

I mean, you know, a quantum computer would really harness nature to do computation in a fundamentally new way, right? It's the first device since Alan Durant, really, that changes, you know, the basic rules of what is efficiently computable and what isn't, right? And it does that because it is exploiting the laws of quantum mechanics, right? So quantum mechanics famously says that systems can be in what are called superposition states, right? So you know a quantum bit, what we call a qubit, can be in a superposition of the zero state and the one state, okay, which means that you know you have some number which is called an amplitude, which is attached to the possibility that the qubit is zero, and you have another amplitude that's attached to the possibility that the qubit is one, right? And so it's not definitely one or the other. Now, if you look at the qubit, if you measure it to ask you know which one it is, then you'll get a definite answer, right? It will tell you, you know, either that it's zero or that it's one. And the probability of each possible outcome will be related to the amplitude by a very famous rule in physics called the Bourne rule. Basically says you take the square of the absolute value of the amplitude to get a probability. Okay, but the key thing is that these amplitudes are not themselves just probabilities. Right? What is a probability? Right? It's a number from zero to one, right? It's uh you could talk about a 30% chance of of rain or of you know someone winning an election, but you'd never talk about a negative 30% chance. That would just be nonsense. Okay, but amplitudes can be positive or negative. In fact, they can even be complex numbers.

So this is the key, right? This is the key thing that we learned about reality, you know, in 1926, that somehow under the hood, nature is using these numbers that are closely related to probabilities, but they're not because they're complex numbers, right? They're these amplitudes. Okay. And so now that's already interesting if I talk about, you know, a single qubit, you know, which could mean like an electron that could be in one of two locations, or that could be, you know, spinning either clockwise or counterclockwise about some axis, you know, has some little degree of freedom. Okay, but it's even more interesting when I talk about multiple qubits, okay, because the rules of quantum mechanics, you know, which have been experimentally confirmed, you know, over and over, you know, thousands of times for the last century, right? They are unequivocal. That if I have, let's say, two qubits, now I need four amplitudes. Okay, I need an amplitude for both qubits to be zero, so for the state zero, zero, and then I need an amplitude for the first qubit to be zero and the second to be one for zero, one, and then I need an amplitude for one, zero and an amplitude for one, one. Okay. If I have three qubits, now I need eight amplitudes, right? One for every possible three-bit string. If I have, you know, a hundred qubits, two to the hundred power amplitudes, right? And if I have a thousand qubits, now that's actually more amplitudes than could be written down in the entire observable universe. Okay, it's two to the thousand power, right? So, in some sense, ever since we've known quantum mechanics, like we've known that nature off to the side somewhere is storing this vast scratch paper, you know, with this unbelievable number of parameters, you know, just to keep track of the states of, you know, rather small numbers of particles, like a few hundreds or thousands, right? And every time something happens to those particles, nature has to cross off all of those numbers and replace them with new numbers. Okay. Now, it's true that we never directly see those numbers, right? You never directly see an amplitude, okay? But we need them to calculate the probabilities of the various outcomes that we do see.

Okay, so this is the basic story, right? So chemists and physicists have known about this for generations, this sort of exponentiality that is at the core of quantum mechanics, you know, because of this sort of explosion of amplitudes. Okay. They've known about it mostly as a practical problem, right? That if you're trying to simulate chemical reactions or simulate materials using a classical computer, you know, you have to solve uh what's called the Schrdinger equation, right, which is the central equation of quantum mechanics, and which basically just tells you how the amplitudes are changing over time when a system is isolated, when your qubits are isolated from the outside world, like when no one is measuring them. Okay. And it just says that they change over time by a linear differential equation.

that preserves the property that the probabilities of all the different outcomes will always add up to what?

Okay, that's all it says. I just, you know, maybe the most important equation in physics, right? So in principle we understand all that. It's even a very simple looking linear differential equation. The trouble is just, you know, how many damn amplitudes there are.

Right. And so as soon as people started trying to simulate, you know, let's say lots of entangled electrons, you know, on computers to calculate, you know, the properties of chemical reactions, they ran into that exponential explosion. Right. And so a lot of what chemists and physicists have been doing, you know, since the 50s and 60s has been, you know, inventing heuristics, you know, approximations, hacks, you know, that let them avoid that exponentiality in various special cases, you know, by being clever, right? But in the early 1980s, you know, a few physicists, most famously Richard Feynman and David Deutsch, had this remarkable idea that if nature is giving us this computational lemon, like why don't we try to make lemonade out of it?

Right. So why don't we build a computer that would itself take advantage of that same exponentiality? Okay, they called that a quantum computer. You know, of course it it was just a thought experiment at the time. Okay. But

you know, they immediately faced the question well, supposing that we built that device, what would it be good for?

Right. And at the time they really only knew one answer to that question, which was it would be good for simulating quantum mechanics itself.

And you know, I think

more than forty years later, you know, the truth is that is still the economically most important application of quantum computers that we know about.

Right. That you know, they would give you this general purpose, you know, way to cut through this sort of exponential, you know, explosion in amplitudes and thereby simulate, you know, whatever quantum material, whatever high temperature superconductor or photovoltaic or protein you might care about, and you know, possibly uh get a you know a much better simulation, uh more accurate simulation in a shorter amount of time than a classical computer could give you. Okay. But that was not the discovery that really put quantum computing on most of the world's radar.

Right. As long as it was just a device for simulating quantum mechanics, it was mostly just this idea kicked around by, you know, a few strange physicists and computer scientists. Right. And what really captured people's attention was the discovery in the mid 1990s that a quantum computer could also achieve dramatic speed ups for at least a few purely classical problems, problems that have nothing to do with quantum mechanics. The most famous example there is the problem of finding the prime factors of a huge number.

Okay. And some of your listeners may know this happens to be the problem that underlies the security of a large fraction of the encryption that currently protects the internet. Particularly anything that's encrypted with RSA, right? Uh depends on the belief that factoring is a hard problem.

Okay. And in nineteen ninety-four, Peter Shore showed that if you could build a large quantum computer, then there would be a fast method for factoring large numbers. Okay. You could factor an n digit number using a number of steps that would scale only roughly like n squared. Okay. Whereas the best classical method takes a number of steps that grows exponentially with n, actually with the cube root of n.

Okay. So that was an exponential speed up over the best known classical algorithm. Okay. And variants of that, as it turns out, could break most of the other public key encryption that we also use to protect the internet, okay, including Diffie Hellman, which is based on a problem called discrete logarithms and even elliptic curve encryption. Okay, that would all be broken by quantum computers. Okay. And so then that really got people's attention. Okay. But unfortunately, what happened like 30 years ago was that like a certain narrative took hold, you know, about how a quantum computer would do all of this that's been really, really hard to dislodge, you know, even though I've been trying for 20 years on my blog, right? And the narrative basically says, well, the way that a quantum computer would do this is it would just try every possible divisor of your number in parallel, right? It would try everything in superposition. And it would basically just be like a massively parallel, exponentially parallel classical computer, right? And you know, I think that caught on because it sounded really good. You know, anyone could understand why that would be useful, right? And, you know, it even had some relationship to something true. Okay. But unfortunately, that's not how it works, right? It's false in a very important way. Right. And so now I think we can really get to the heart of how a quantum computer is different from a classical one, right? So it's true that with a quantum computer, you can create an equal superposition over every possible solution to your problem, even if there are exponentially many of them.

You know, that's even an easy thing to do with a quantum computer. The trouble is that for a computer to be useful, you know, at some point you have to look. You have to measure, you have to get an output. Okay. And if you just did that, you know, to an equal superposition, not having done anything else, then the rules of quantum mechanics, you know, this Born rule are very clear that all you're going to see will be a random answer.

Right. And if you just wanted a random answer, you could have just flipped a coin a bunch of times. You know, you could have just picked one yourself. You could have saved yourself all the billions of dollars of, you know, building this quantum computer. Right. So really the only hope of getting an advantage from a classical computer, you know, compared to just a classical computer with a random number generator, right? Is to exploit the way that these amplitudes being complex numbers work differently from conventional probabilities, right? And with every algorithm for a quantum computer, you know, including the famous Schor's factoring algorithm, okay, the trick is that you're trying to choreograph a pattern of interference in such a way that for each wrong answer, so like each number that's not a prime factor of your number, like some of the contributions to its amplitude are positive and others are negative, so that on the whole they cancel each other out. Whereas for the right answer, you know, you want all the contributions to its amplitude to be pointing in the same way.

So that they reinforce, so that they add up.

Right? And if you can arrange that, then when you measure your qubits, you're going to see the answer you want, in the case of Schor's algorithm, the prime factors of your number with a high probability.

And you know, if you don't see it, you can always just repeat the quantum computation several times, you know, until you do. Okay. But the whole game is to use this interference between positive and negative amplitudes to try to boost the probability of seeing the right answer, you know, to higher than you could get with a classical computer. Now, it's very

tricky. It's like nature is giving you this really bizarre new hammer. Right. It's not obvious a priori that there's any useful nails that that hammer can hit, you know, other than just simulating quantum mechanics itself. Right. That's why it took people like Peter Shore to figure this out.

Right. It wasn't obvious. Okay. Because you have to, you know, arrange all this interference, even though you yourself don't know in advance which answer is the right one. You know, if you already knew what would be the point, right? And you have to do all of this faster than the fastest classical method.

Right? Or else, you know, again, why not just use a classical computer instead?

Okay, so this is the game with quantum computing, and this is why you know the applications of a quantum computer have been more specialized than some people would like. So to go back to your boat analogy, right? Okay, in some sense, anything that a classical computer can do, you know, a quantum computer can also do. So maybe it's less like a boat than an amphibious vehicle. But it just for most of what we do with classical computers, there's no point to using a quantum computer because it's not any better, right? It's only better to the extent that you can take advantage of this interference phenomenon to concentrate more amplitude on the answer you want faster than a classical algorithm could do the same thing.

I think the intuition that I'm getting is that uh quantum computers are good at very large number management. Scott, maybe I can ask perhaps our last fun, dumb question before we hand things off to Justin Drake here.

These are not dumb questions.

Oh, good, good. I'm glad, I'm glad. The simple question is the pictures of the quantum computers that I've seen, why do they look so weird? Yeah. Like, why like I'm used to chips that are these like very small, flat, square, you know, metal things that fit into like my motherboard. And that is not what I'm looking at right here. What's the deal with this?