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- [00:00]
Host
Quantum A 101 why It Matters, what victory looks like to discuss, we have on Zach Yaru Shalmi, chief executive of Elevate Quantum, a public private consortium based in the Mountain west that I will let him explain. Co hosting as always, it seems almost Chris Miller. Zach, welcome to ChinaTalk.
- [00:24]
Zach Yaru Shalmi
Thank you guys for having me on. Total pleasure.
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Host
All right, Quantum 101. Why should China Talk listeners turn their light years to Quantum?
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Zach Yaru Shalmi
ChinaTalk listeners should, should care about Quantum because, you know, with AI on our doorstep, Quantum is the single biggest lever that we have to pull honestly as a society for about the next couple of decades. And particularly so candidly for the Chinese doc listeners, it's not just a big economic national security opportunity, but for such a policy oriented group of folks, I think it's one where because the margin of error is so thin, we have more at stakes here than any industrial program since the atomic. And so it's, it's multi layered there. It's maybe, it's maybe quantum, but that's why I think folks should care.
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Host
What, what do you mean by margin of error?
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Zach Yaru Shalmi
So you know, there are two folks that on this, call on this, on this podcast and spend a lot of time thinking about semiconductors. My observation there is in semicon we have literally decades of moat over China and other folks that we care about competing with. And that doesn't mean we sleep on semicon, that doesn't mean we forget industrial policy there. But quantum, Quantum is kind of fundamentally new for a lot of the capabilities we're trying to bring forward. And that by definition means that our moat is pretty darn small. And so whereas in semicon we can get things right and a little bit wrong. And in quantum, I think our margin of error is freakishly small. And so we have to get it right from the beginning.
- [02:21]
Host
All right, make the case for why it matters. But why is this the thing that could is the next door for humanity to open over the next half century?
- [02:32]
Zach Yaru Shalmi
So why it's so important goes back to actually the, the inception of the idea of a quantum computer. It came in 1981. This guy, Richard Feynman, he has this famous quote of, of effectively like nature is quantum. Damn it, I hope I can swear. And it's the realization that if we want to solve the problems that quantum mechanics governs, which are really a world of the atomic realm, not marvel, but the very small, the very cold. This is like drug discovery, this is catalyst, this is material science. This is all the things that govern the building blocks of the Universe, we can't use our classical approach and classical computers to go and solve that. And this gets a little bit into the math and could get a little bit dangerous. But the quantum world behaves in ways where even very small systems explode in complexity. So a standard two particle system, these systems explode at two to the N. So if you have a two atom system, that's two to the N states that you need to understand. If you add a third atom to that, it's two to the third. So you suddenly need to understand eight states, not one additional. By the time you get to 20 atom systems, you need to understand a million states in order to do that. And a 20 atom system is freakishly small. So it gets back to, why does it matter? Let's use a specific example, penicillin. Penicillin is 42 atoms. That is not a big molecular system to it. But penicillin is obviously pretty important. If we want to understand penicillin, much less where it falls short, then in order to do that classically using our kind of current computing paradigm, even with the world's best AI, we'd have to use something like 10 to the 86 transistors to do it. And just to double click on that, like I said, 10 to the 86 quickly. It's a shockingly large number. 10 to the 86 would be needing more transistors than there are atoms in the observable universe. Like in kind of simple terms, we could literally use the energy of the entire universe and we couldn't quite make a basic model physics based for penicillin. And so if we ever talk about living in the Jetsons age, right. Rationally designing all these things, our current paradigm, even with the world's best AI, is just never going to get it there. And so it gets back to that quote from Feynman in 81. He just went on this rant and it was really a thought experiment, which is nature is quantum mechanical. Damn it. What if we're just approaching this on the wrong terms? What if we built a computer that operated on the same principles as penicillin operates itself? It'd probably be more efficient. And it wasn't just a little more efficient, it was a reinvention of what a computer could be. Instead of needing more atoms, sorry, more transistors than our atoms, maybe the real universe, you need something like 186 of these quantum bits, qubits, which we're not there yet, but we're more or less on the cusp of. And if you can do that again, because these systems are exponential in nature. When you go from 186 qubits to understand penicillin to 187, just one additional system, it's not a little bit better computer. It is thinking about penicillin interacting with its neighbor. When you get to a thousand qubit systems, you're talking about rational design and much more complex systems. And so if we ever want to get to what I think, actually, even candidly, folks talk about from the AI world of curing cancer, solving climate change, addressing the material science of the world all around us, actually, really the only cowbell that we can really hit on for some of those problems is quantum.
- [06:21]
Chris Miller
Could I ask maybe the same question from an economics perspective, which is thinking about the kind of market for quantum computing capabilities? And it's an unfair question to ask because if you'd have asked people at OpenAI in 2019, what's the market for AI, they would have given you a very large number without much justification, because who knew exactly how it would have played out. But how do you think about where we'd like to be deploying quantum computing capabilities in 2035? Drug discovery is obviously the first answer everyone gives, and that's obviously a potentially huge market. But beyond that, how do we think about the economic impact of quantum computing?
- [06:58]
Zach Yaru Shalmi
Yeah, so there are two applications that folks go to. And the analogy that I think about is, you know, Chris, you guys, you cover this so well in your book. One of the really first killer apps of the semiconductor era was the hearing aid or a transistor. And so all of this is like forecasting the impact of this technology with, with a, a transistor, set of examples for what is possible. But the interesting thing about quantum is the transistor capabilities are worth hundreds of billions to trillions of dollars. So the couple of applications, the first would actually maybe not even be life sciences. It would be something around corrosion and accurate corrosion modeling, which is worth tens of billions of dollars to the global shipping and national security sector. And that's because it's a more simple problem and tractable to get around. There's some interesting things around nuclear chemistry. And as these systems get bigger, then you start to look at drug discovery, start to look at material science. Folks a lot talk about room temperature superconductors, which is a whole other, you know, YouTube wormhole to get into. But if we want cell phone batteries that never lose power. Right. It's the ability to rationally design these systems at the molecular level, at the atomic level, opens up that the second class of problems, from an economic impact perspective, candidly, is. Is in many ways maybe more near term and more scary, which is get back to the transistor analogy. Most of the classical algorithms we developed occurred after we had the computer. The only other big class of problems that folks know that quantum computers are useful for, aside from the molecular modeling piece or the physics modeling piece to it, is they can. They're really good at the hidden subgroup problem, which sounds a bit jargony, because it's a bit jargony. The big one there is factoring large prides. And for anybody aware for cybersecurity concerns, if you can factor large primes, you basically crack the code that underpins all of our existing cybersecurity infrastructure, which is why global governments, even putting aside the Jetsons nature of what we can unlock, are really worried, because all of their codes and all of our financial infrastructure and things like Bitcoin are underpinned by this problem that a classical computer literally takes age of the universe to solve. And a quantum computer looks at, laughs at, and, you know, steals your Bitcoin wallet.
- [09:23]
Host
All right, so before we go too deep into applications, Zach, what's your favorite analogy to give folks to start wrapping their head around this?
- [09:33]
Zach Yaru Shalmi
So there are two that come to mind. One is, one is something that myself and a guy, Corbin Tilburg, came up with. Another is Matt and Joni. The. The one that I think about is if. If classical computers are like a car, quantum is like a rocket ship. And very specifically there, classical. That's to say a couple things. First, we'll still use classical computers just like we use cars for certain applications. But for certain things of the world, a faster car is not going to more efficiently get you to space. You need to rethink that mode of transportation. And so for quantum, because of the nature of that problem set, you got to invent the equivalent of the spaceship to do that. And so the idea was, let's create a computer that acts on the same term as the systems that we want to solve. Now, how do those computers do it? The best analogy that I've seen is this maze analogy that folks should look up online by Matt Langioni. He's the quantum partner at bcg. And what he talks about is take a maze, right? Kind of hold it in your head. The way humans approach a maze is actually pretty similar to how classical computers do it. You walk in, you're faced with the decision to go left or right. You go right, you bump up against the wall. Afterwards, you kind of Walk back. And the time it takes for you to get to maze is the time it takes to make each of those decisions and then work your way through the maze. A quantum computer approaches a maze fundamentally differently. Quantum computer walks in the maze, looks at that decision to go left or right because it can operate in principles of superposition and entanglement and interference. It says, yes, it explores both paths simultaneously and not just at that single node. It looks across every single node of that maze and in parallel makes that same exploration of paths. And whereas again, that classical computer takes you the time to make each decision and then the accumulation in sequence of each of those decisions, a quantum computer looks both at that first and then each every other node and goes, yes, let's go for it. And so all of these questions of out there in the real world for applications, economic impact, those questions that look like the maze are those of the molecular world and worth, you know, again, the hundreds of billions to trillions of dollars. Because it turns out that whether it be chemistry or molecular science, design, they, they actually have a lot of similar characteristics, characteristics to what we just described.
- [12:07]
Host
So Zach sent Chris and I a background reading syllabus which we will put in the show. Notes There is a YouTube video by Three Blue One Brown. But what is quantum computing, which I confess I had to watch probably three and a half times before it started to settle in. You know, one of the interesting things about this is like, very quickly, you know, you try to make the analogies of like left, right, or like two dimensional space, and then they give you three dimensional space. But all of the examples are like, oh, no, actually, you know, these, these are 16 dimensions, 20 dimensions. And you know it, when they show the equations, it actually makes more sense than when they try to give you the analogies. So whenever they're trying, whenever these folks, they try to like, make it simpler by like giving you some spatial analogy for the thing that's happening. What I was kind of going back to were the YouTube or the, the parts of the YouTube video and the Wikipedia pages that just like had equations in them. And you don't have to like, you know, expand your mind like you're some like Tibetan, you know, enlightened Buddha or something. But you can just be like, okay, all right, I will take it for granted that these equations are the things that, you know, all of the particles are doing or not doing. But it was fun to kind of stretch my mind in a way which I haven't in a while. I contrasting that earlier today, listening to some like World War II fighter history where the, you know, the physics was like, okay, the plane's going this fast and you know, the other plane's going this fast. And they invented some little computer that did some like, very straightforward calculation to shoot, you know, 10, you know, 50 yards ahead so that it would hit your, the measure spit or what have you. And here I am with Zach going me down the, you know, deepest rabbit holes of the universe. But anyways, let's come back to, to this. Zach, why don't you want to. How about like, let's make the case for people spending that long weekend actually trying to sort of wrap their teeth around some of the more, you know, the, the, the physics fundamentals of this as opposed to just kind of jumping to thinking about all the cool applications
- [14:36]
Zach Yaru Shalmi
and what have you. I think the two things that come to mind and, and you know, Chris is really engaged here, so his take on why he's spending the, the long weekends, not necessarily coming from that. I'm, I'm super keen on two things. First is just cool, you know, there's. If you're like into listening to Neil DeGrasse Tyson and star talk, you know, heck, why not make a little bit of time for, for Quantum. But the, the second, I think is separating hype from reality in this, if it is so important, right, if that the, the case for Quantum from an economic, national security import perspective is that important. Having a base intuitive understanding of what a quantum computer does, what it doesn't do and where it's useful is absolutely essential for Chinatalk listeners because a lot of them are engaged on policy and prioritizing. Where do we make investments in order to actually have that lead. And this is true of any discipline that there can be a lot of smoke and rares and heightened reality and all those sorts of things. But in Quantum, almost because it is such a hard thing to grapple around, I find more of that. So I think that investment is super worthwhile. Chris, I welcome your take. Where was that sea change? What was the ROI calculation there for yourself?
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Chris Miller
Well, I think to me every next step in computing capabilities seems magical or impossible 15 years out and then it becomes possible and then normal and then we forget that it's happening. My analogy for where we are today vis a vis Quantum is that it's like 2015 in AI and all the researchers were saying progress is coming very rapidly. And everyone outside of AI said I don't know what this means and it's probably not real. And even if it's real, it's a long way away, so I'm going to ignore it. And then the world was surprised by 2022 when ChatGPT dropped in a big way. And it seems to me that a roughly comparable time horizon is where most of the quantum researchers think we're going to be in half a decade or so.
- [16:53]
Zach Yaru Shalmi
Chris, I welcome your take on this, but a thought occurred last week where, taking the AI analogy, if you got attuned to AI as a government, as an investor, as a policymaker, when ChatGPT hit, it was actually candidly a bit too late. The time to really be attuned to it was probably, what is that? You know, attention is all you need. Like the birth of the LLM, which I think was around 2017. What was wild about Quantum last year, 2025 is, you know, the chat PPT is. It's not there. It was not. It was not there last year. What I'd argue though is it was the birth of the LLM for the industry. And that's because with the Google Willow paper that came out and a couple other breakthroughs from others, it went from a technology domain where, as I alluded to before, in order for these things to be useful, you have to add a certain number of these qubits. It turned out that up until last year, every time you added a qubit, the entire system got less stable, which is probably bad news from like, these things are going to be useful perspective where that sea change happened. This came out with Google's paper was they found out a way through this error correction where you added a unit, a quantum bit to that and the entire system got more stable. And that in my head shifts from a little bit like the LLM, where at that base architecture it becomes more of a when do these capabilities come online in a way that changes the world around us instead of an if. And it just reinforces that, why don't be a tune now? Because we can't afford to wait until ChatGPT hits.
- [18:31]
Host
And I think that's kind of another argument for actually spending the time to understand the fundamentals here. Because reading the results of all those AI papers, even for someone who isn't a computer science PhD, has been relatively straightforward. It's like, okay, there are certain benchmarks and these are like legible to human beings, like labeling images or you can talk to the models and kind of feel how good they are. And that is something that even a layperson has been able to follow without really understanding the transformer architecture or what have you for a long time. And I think like, in order to separate sort of hype from reality, when Microsoft or IBM or Google comes out with a paper, it requires being able to digest more kind of, you know, second technical, secondary commentary than just, oh yeah, sure, try the model out for yourselves. It's not like we all have quantum computers in our backyard yet that we're trying to model penicillin with, and all of a sudden it'll work, but maybe we'll get there one day. Well, let's stay on, let's stay on the, the sort of industry history piece. Zach. So where were we? I guess if 2025 is our turning point, where were we before? Where were the past? Where was 2014-2024?
- [20:02]
Zach Yaru Shalmi
So quantum computer started as a thought experiment in 1981. The entire industry was kind of born in that moment when Feynman was like, you know, let's build something on nature's own terms. In the. It was 2009, that 2009, the first two cubic gate operation, which was an important great breakthrough that actually happened in Colorado as a kind of unitized thing for how you would create a quantum processing unit. And we've had not just steady, but I'd argue, exponential progress in the capability of these systems since that time. You had, what was it, 2019, 2020, that the quantum supremacy paper came through with Google, and then it was last year that you had the sea change where I'd argue it's a shift from the more fundamental side of R and D to something where you're really just focused on the engineering of these systems to a sufficient scale that these problems that keep the NSA up at night and then the rest of us dreaming from a new capability perspective, really on the cusp. And then if we look at industry timelines, even a couple of years ago, folks would have said that quant, a useful quantum computer is about 10 years out last year that went from, you know, 10 plus years out to, with these breakthroughs, it's on a, you know, three to five year basis that folks think will get these, these systems that are capable of, of, you know, whether it be the cryptographical capabilities that we're, we're worried about, candidly, or on the other side, some of the, the material science capabilities that open up new economic opportunities. So it's happening fast.
- [21:58]
Chris Miller
So I think, I agree that we've shifted from the realm of science experiment to the realm of engineering. And the question that brings up is how should policy change? I think for the fundamental research, you support academics and they do their studies and they push the frontier of, of knowledge and in physics and other fields. But for engineering, you need different tools for different problems. And scaling up is as much an economic problem as it is engineering problem. So can you walk us through how you think the types of policies that we should think about in the quantum sphere ought to be changing given the shift from solving the science, which we've made a lot of progress on, to addressing the engineering, which is where we are right now?
- [22:49]
Zach Yaru Shalmi
Yeah. So two things that I would say, and I've said this before, but your take on it and learning from other industries I think is essential. That's what I'm always trying to figure out. The first is we use the computing history as a lesson. You can't just put down the fundamental R and D to that. If we from a computing age got to vacuum tubes and put our tools down and set jobs on, we would have missed out on the transistor. So that engine of growth and innovation, of thinking what is the next generation and maybe even a reinvention of what people are thinking from a basic architecture is essential. And then the second piece of that. So fine, how do we look at the industry now that there's this sea change? The method model that I always use for quantum is biotech. And in biotech you have drugs, maybe a B small molecule, or you have car T or these different sorts of modalities. You have that for Quantum itself, and those are the things that you know will cure cancer. You also have the tools for addressing that. And as we cross into this chasm right now, what I would think about from a policy lens is we've kind of entered phase 1 clinical trials. And so what the things we need to do are from a policymaker perspective is actually it looks like a similar tool set to how we foster the right environment for biotech itself. The important distinction though is in biotech, again, back to that we have a wild moat, right? The cluster, both commercial and scientifically around Boston is so globally dominant that we can screw up a bunch of stuff. And so now it's a what are the tools we need to do given the same sort of architecture? Right. Material, technical risk, you need to get commercial payout, you need to do these things. But now we need to do that with a much earlier stage industry. Well, let's talk about, let's do a
- [24:48]
Host
little bit of industry, Macman, because you do have these very shiny releases by some of America's largest publicly listed companies. And you've got some startups doing stuff and you got the Government labs and you got academia. Let's start with the giant companies. I mean, is this just like everyone wants to be Bell Labs? Like, why is, why is Microsoft and Google spending time on this sort of thing? They got data centers to build with their CapEx, right?
- [25:15]
Zach Yaru Shalmi
Yeah, a lot to build with their CapEx. And there are weird R and D tax incentives in the state of California that we won't get into. Why do they have to focus on this? It gets back to, you know, it's funny, it gets back to they can't afford strategic surprise. Right. Your comment of this is such an important thing. Them spending a couple billion dollars on it a year to make sure they have their finger on the pulse so that there's not a Sputnik moment for them as a company is just worth the roi. And ultimately they have big quantum programs, arguably some of the largest, but relative to everything else, and it's rounding errors on their balance sheet. And I'd argue the same about the U.S. government.
- [26:02]
Chris Miller
Right.
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Zach Yaru Shalmi
DARPA, indicative of how important this is, DARPA's largest public program in the agency's history is quantum. It's called the Quantum Benchmarking Initiative. And it almost gets a little bit to your question, Chris, on now that we're in this new era, what are the policy levers that we need to undertake to get this right? That's canonical. And it all comes back to what are the right commercial. Because they're commercial structuring. That's what makes biotech motor on. And then what are the right technical levers that you need?
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Chris Miller
Do you want to explain what the, what DARPA's Quantum Benchmark Initiative actually is? Because it actually seems to me exactly like the clinical trial analogy that you just mentioned.
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Zach Yaru Shalmi
Yeah.
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Chris Miller
So can you dig into that?
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Zach Yaru Shalmi
Yeah. So the Quantum Benchmark Initiative, as I alluded to, largest public program in the agency's history. I think this year alone, they're looking to spend $600 million on that. The frame there is, you know, they don't say this explicitly, but US Government can't afford to be surprised that China can break its codes. And so they stood up this program that is effectively about how to learn about the capabilities of the leading players in the world sans China, because China's not going to talk to the US Government about its quantum capabilities. And then the model that it took to do that and huge respect for the team, there is actually a kind of reconstructed and advanced market commitment for what you see in drug discovery, but applied that to quantum. And so it's effectively like A grand challenge prize structure. The first phase, you get a little bit of money. It's like a million dollars here or there and it's really just a table stakes thing. The next phase is 15 to 20 million dollars. And that's if you pass a certain scientific threshold. But then things really get going by the third phase of the program where you can get paid $300 million if your system is deemed credible to that stage and you build a demonstration scale capability for it. And then the implied thing is after that, right, you get through that third phase of darpa, it's kind of your FDA approval, some government agency is going to buy one of these things because it's going to investigate parts of the cryptographical world that they would be very interested in. And actually if we get back to it and it gets back to that frame, those payouts map really well to the different sorts of commercial staging you'd see in biotech. $300 million prize, that some phase one, two stage and then a billion dollar prize if you get past that. And that's super important, not just because these companies want to earn money, but because if you want a virtuous cycle going, you need to get the venture investors and the private markets excited. And those payouts are big enough in order to create the incentive around it.
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Host
All right, so let's go to startups.
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Chris Miller
Yeah.
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Host
Why do they exist? Are they real yet? What are they doing?
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Zach Yaru Shalmi
Yeah, yeah. So startups exist here just like startups exist in biotech. Right? Big old companies, IBM is putting in billions of dollars into this thing. And IBM is, it is the flagship program in Quantum. But big companies face, they can do a lot of innovating. They have important programs, important distribution. But just like Novo Nordisk doesn't do all of innovation in the world around, you know, new ways to discover, develop drugs, you also need early stage players, disproportionately coming from academia that are coming up with a new paradigm that could disrupt what is going on in this technology space. And so the innovating and in this market is like you see in biotech, where you get the big tech players, the air driving programs, they have amazing access to supercomputer. But there are newer stage programs, newer stage approaches that could be incredibly disruptive. And those are typically pioneered by startups. If those really take off, then typically the big tech players swoop in and either do an investment or buy that out. And we've seen that quantum. There are a couple of approaches in quantum computing called realities. The historic one is called superconducting What's a solid state of hard bit Superconducting qubits. This is the thing that John Martinez won the Nobel Prize for recently. That's gotten 60 to 80% of the investment to date for the industry. But there are newer approaches. The most prominent of them is probably neutral atoms that instead of using almost a synthetic qubit quantum bit, they use atoms themselves as the qubit. And this was kind of science fiction literally three, four, five years ago of people who thought that this idea was a total joke. And it was a bunch of startups who like you see everywhere else in the world, grabbed the mantle, said no, I did my postdoc on this, this is not a total joke, and ran at that. And there was a sea chain probably two or three years ago and it's now one of the leading approaches and nobody would have bet on that little time back. And now it's one of the things that could really have a shot. A marker of this is Google as one example. They pioneered and continue to push forward superconducting as their core approach. But they're so worried and interested in neutral atoms, they just gave Quera $250 million because they think that approach could work. That's so that's the role that startups play is the classic disruptive innovation and driving forward what folks thought couldn't be possible because the risk reward wouldn't make sense for an existing company.
- [31:55]
Host
I think now is time for a little elevate detour. Why don't you tell the folks out there what you do all day, Zach?
- [32:04]
Zach Yaru Shalmi
So elevate is the US government's quantity. So we're the first and only major place based investment that the US government has made in the quantum industry. Now they did that ultimately because one reason had nothing to do with us. The Mountain west cluster is the largest quantum cluster on the planet. It's almost, it's probably close to half of US quantum jobs, half of deployed capital. It's massive by quantum standards, right? Small industry, but still massive by quantum standards. But then the second what I do and it gets back actually to the policy measures that I'd be keen on, our mission is dramatically accelerating commercialization of quantum and we do that as a public private partnership. And the doing of that is focused on specific, typically technical bottlenecks that are market failures that other players aren't well placed to solve. So we look at things like fabs, we, we look at things like packaging, we look at things like certain shared use equipment that for a whole bunch of reasons national labs and universities aren't well placed to address. And then startups might not have the capital or they might not have the expertise, or they might not have the time in order to go and solve for those. And that's ultimately what elevate which we have 140, 150 members in our consortium. Everybody usual suspect between the national labs and the universities and the Mountain west, but then also every big tech player with a quantum program. That's what we dive in to solve with lots of friends.
- [33:38]
Chris Miller
Okay, so Zach, so we're in this quantum race. China's a major competitor. What does winning actually look like?
- [33:50]
Zach Yaru Shalmi
In my head, it's getting there first and maintaining the best capability as a nation long into the future. Getting there first is building the first. Folks will throw out the word fault tolerant, but think of that as a commercially useful system. Something that, whether it be the cryptographical use cases or the material science use cases or any of those things, does something that drives commercial value. This is like building the first useful computer with a vacuum tube or something like that. But then the second is continuing to have the best capability in the world or really ultimately, I guess maybe access to that. That's what I think winning means. The tricky part is what's the lead indicator? Right? Like we can't look at price for that. But the thing that I'm trying to figure out is in absence of price, what's the, what's the lead indicator on us winning?
- [34:54]
Chris Miller
When we get to our first commercially useful quantum computer at scale, do you think there'll be one company that Nvidia like dominates the market? Or should we expect multiple different paradigms to be relevant perhaps for different things that they're better or worse at?
- [35:11]
Zach Yaru Shalmi
Yeah, the short answer is folks, part of that. I suspect that at least for the foreseeable future, we're going to have pretty purpose built machines. That comes from the previous computing era where you had purpose built machines based on application. My instinct is that's what this is going to look like. That may at some point converge on a transistor architecture for quantum, something that everybody converges on and uses. But that's not yet as a system though. And it gets back to, you know, car versus rocket ship. I was talking about earlier what most suspect quantum will play as a, as a role. And, and Chris, I think you talk about this as like the, the free paradigm model for computing and I, I'm getting the words wrong, but you have classical, you have CPUs, those will stay useful. You have GPUs AI accelerated compute. Those will stay useful for a certain set of problems, but then you're also going to have quantum processing. And these will work in tandem in order to solve some of the biggest problems that we care about from a science and cryptography dimension.
- [36:25]
Chris Miller
And that's something that I think a lot of people who are new to the field don't get. There's a common assumption that quantum will replace classical, and that's obviously not the right way to look at it overall.
- [36:36]
Zach Yaru Shalmi
Yeah, yeah. Just like GPUs didn't replace CPUs, you know, these are Turing complete machines. They technically can do all the computation. They just won't be efficient at a lot of the problems that you'd want to computationally solve out there.
- [36:53]
Host
So one of the remarkable things about AI is how fast the learnings at the frontier diffuse to the firms that are trying to catch up. We just were recording this on February 23rd. We just had a fun story come out today, anthropic saying that Deepseek, Drippoo and Minimax were all, you know, doing millions of queries to try to get data that they could then feed into their models. You know, there's, there's this whole story about, oh, like, you go to enough parties in San Francisco and you'll hear about the cool new training things that you can then bring back to your own lab. Given the. To what extent do you think, do you see sort of like frontier breakthroughs leaking out into other firms who are trying to do the same thing and I guess spilling across borders as well,
- [37:51]
Zach Yaru Shalmi
that part of Quantum, it's still driven by academic researchers in a big way. And so publication is a big thing in Quantum. And so just like you see publications come out there, something thrown on an archive, and then it diffuse that, that very much happens in this industry. There is an interesting caveat that, and this is, you know, mainly received wisdom that the Chinese government is actually, they keep publication on lockdown. They typically wait for a breakthrough in one of the firms in the west, and then they'll allow for their researchers to publish something similar. And that's not something to look past. This isn't, you know, bad copying or the kind of, the kind of hackneyed stereotype about Chinese innovation that that's not the mental model to deploy here. And so you see a lot of it. The one caveat, the big difference with AI is Quantum is very much a hardware sport. And so that means the iteration times are much longer. And then a lot of that diffusion, I guess you do see this AI but it's, it's a lot of like received wisdom and deep knowledge of like how to, how to affix optics to a breadboard. And how do these systems behave in these different ways? It's a, it's a very different science from, from that domain of zeros and
- [39:26]
Host
ones which presumably would make the, the sort of learning more frictionful across firms
- [39:35]
Zach Yaru Shalmi
and a lot more friction.
- [39:36]
Chris Miller
Okay, maybe the analogy is in AI the algorithms diffuse rapidly. The know how about how to produce the chips hasn't. Maybe the analogy is the same that the algorithm layer, the software layer might diffuse rapidly, the research layer, but the manufacturing know how also does totally and
- [39:58]
Zach Yaru Shalmi
it's a much earlier stage. So each of these paradigms, there's not this transistor, so this thing that you can base all these understandings off of. Each of the ways that you build a quantum computer themselves have deep expertise built around them. But, and again, you know, everything is double edged because the earlier stage nature of that, that does mean that if there's a real breakout in China around a particular domain, it's going to be much harder to transmit that knowledge over to the us that moat is just much stickier.
- [40:36]
Chris Miller
One of the uses of quantum computing that we've known about for a long time commonly discussed is breaking encryption. Now we've got post quantum encryption standards that have been released by nist, although I think it's unclear how widely or rapidly they're actually being deployed. Probably not rapidly enough. Walk us through how you see us reaching a point in which all of our 2010 era encryption is easily broken by a quantum computer.
- [41:04]
Zach Yaru Shalmi
Yeah, it's actually, it's pretty scary because NIST, I think it's all government systems by 2028, 2029, but then the rest of consumer systems by 2035. But you know, that recommendation I think came out early last year. By the end of last year folks are talking about three to five years from last year. So 2030 that will have these systems online that can break these standards. That freaks me out because typically you want 10 to 15 years as an upgrade cycle for traditional security protocols and we have three to five. So that's a bit worrying. Why these systems can do that is through this algorithm to discovered by Peter Shore. Nothing to do with the original idea behind a quantum computer. I spoke about it before. They are good at the hidden subgroup problem and the two prominent techniques of the hidden subgroup problem that classical computers suck at, which is why they're used as a basis for all these encryption standards. One is factoring Large primes. So if you have 15 out there exposed as a public key through a weird fact of math, it takes a normal computer a really long time to figure out that you could break that into three and five. And the bigger that number gets, the longer that computer takes to get it. And the other one is, it's something called elliptical curve cryptography, which is actually kind of a similar problem. But it, it's really cool math and it sounds like what it is. It's basically using elliptical curves as a way to find a hidden subgroup. And that is the basis for a lot of other types of cryptography, like to help secure the signature for Bitcoin. And so a normal computer looks at these things, has a really hard time again, age of the universe in order to break that down and understand it. Whereas a quantum computer, because it has this exponential speed up on a three to five year timeline, would be able to solve that hidden subgroup problem. And so we face the cryptographical standards that we have. The thing that worries me as well, it's not just that we have a wildly short time to move to a new cryptographical standard. It is that lattice based encryption, which is the standard that folks are saying we should move to, which put out by nist. And the theory behind that is, is very good. And folks think that a quantum computer would have a really hard time addressing those encryption standards. The issue there is theory is very mature, implementation is really not mature. And so we both have to move faster than we really ever have to a new encryption standard. But the one that we're moving to, it's wrong to say the kinks haven't been worked out, but it just really hasn't been deployed at any real scale. You put those two things together and it's something I worry about. It's something a lot of people I think worry about.
- [44:23]
Chris Miller
I think on the encryption part, I think the thing that I haven't fully thought through is for AI, there's this AI race, but the fruits of it are kind of far out. It's productivity enhancements. Whereas for DE encryption, the fruits are immediate if you get there. And so how do governments, both in US and China, think about if we're six months away, we're never going to be six months away from the fruits of AI because it'll always be constantly bearing fruit. But if you're six months away from DE encryption, like at what point do you just say all quantum computing resources must be devoted to this task? Defense Production act style, you know, it seems highly plausible China would Do that. And it seems possible we'd do that too if the, if that were the stake. So there's interesting game theory around that dynamic. Like bomb the data centers was like the sort of not serious or maybe some people thought it was serious. I thought it was a not serious kind of meme from 2022 or 2023 about like what if AI gets of control but like, you know, it starts to become a little bit more plausible in the, in the quantum space if the stakes are that all cybersecurity disappears.
- [45:31]
Zach Yaru Shalmi
So.
- [45:32]
Host
Well, it kind of seems harder to bomb a quantum computer though, right, because it's just like one room, you can
- [45:39]
Chris Miller
put it anywhere and the know how presumably continues even if you destroy the physical device.
- [45:47]
Zach Yaru Shalmi
I am trying to figure out, you know, there's like stuff around nuclear chemistry which is really scary for, for quantum computers. It's one of the many reasons that folks care about them. And again, none of this is on the high side. The what's the game theory of. Do you think it's that different from we're six months out from AGI?
- [46:13]
Chris Miller
Well, I think if, if you think that AGI is like a, a threshold that before you have nothing and afterwards you have super intelligence, then I think the game theory is similar. But I don't think we really believe that there's a sort of AGI threshold that has a dramatic before and after relative to a ongoing gradient where you get better and better capabilities with more and more productivity. And I think for most kind of economic applications of quantum, it looks like again, that's, I don't know, steady is the right word, but a trend over time. But decryption is this threshold dynamic where if you're on the wrong side of it, the stakes are high.
- [46:52]
Zach Yaru Shalmi
So the one thing I'd say there, yes, absolutely, stakes are super high. Global Western governments, they committed something like $23 billion to Quantum in the last three years. And I'm sure they're really excited about molecular modeling, but they're probably mostly really scared about the encryption side of it. The one thing that I would call out is the first computer that gets there is not going to be very efficient at breaking those codes. It could literally depending on the architecture, take like a month or many months in order to break one code. Which means you gotta choose your wallets very, very assiduously. And that's depending on the architecture. But I do think it is a little bit more akin to your AI model of just because you got there doesn't mean that there's more juice to squeeze. I think the thing that I worry
- [47:49]
Chris Miller
about but isn't the first code that you break, but isn't the first code you break like the Chinese nuclear codes. It's a pretty high value code, super high. I feel like there's some pretty high value codes you could break right away and justify thinking about it as a pretty important. I mean, I don't know anything about the Chinese nuclear system, but that seems like exactly where one would go if one was going to think about the high value code.
- [48:09]
Zach Yaru Shalmi
Yeah, there are people with clearances far above my pay grade that probably know how much concentration there is for that Chinese high value code. I don't have a good sense there. I think the thing that I worry most about from a quantum computer, aside from the code breaking, is the recursive nature of these systems and how they, how they improve existing material science stuff. So take the example of high temperature superconductors or things in nuclear chemistry. Right. If you had a system that could rationally design superconductors, a system that could rationally design things in the chemistry world, you would use that and lock down IP space and know how in a way that would block out adversaries and competitors. And so that's I think, where from a moat perspective, it's not just how you build the system but how you lock down on the things that that system invents. And because, you know, AI is a lot like stabbing the dark, right. It's still really, really, really good, fancy curve fitting. Quantum computers are not that. They're not, they're not guessing, it's ab initio. They're solving these problems from a first principal basis and locking in on the right solution for that thing. And if I'm thinking of like, you know, silent mushroom cloud esque sort of analogies to it, that's, that's where my brain gets going a little bit scary, I think.
- [49:39]
Host
Well, let's talk about like, okay, that, so that first computer, I mean the, like, it won't be an engineering challenge like a Manhattan Project that ends up costing a hundred times more than any other past one, presumably. Right. Is it more likely to be some sort of, sort of engineering breakthrough or is money a thing that you can like brute force your way into having that quantum computer that's actually useful to help you break the nuclear code.
- [50:15]
Zach Yaru Shalmi
Yeah. So, you know, honestly, especially because we crossed that threshold where every qubit that you add is now making the system more powerful, you can just throw money at the problem. You'd build a wildly inefficient wildly expensive computer, but it could like break RSIA a couple of times. So that would be super bad news. How you spend the money is super important. I think that. And this is maybe to your. Your point, you could spend a trillion dollars on quantum. There. There aren't. Because you need somebody, you need a human to like wire the refrigerators and the optics tables that you need to operate this stuff. The bottleneck is really around talent. And so that's where the prioritization of where you're spending the money, to my mind is everything. Because actually you could throw money at this all day long, to your point, and maybe you need it, but you'd overfeed and you wouldn't get there anyways.
- [51:18]
Chris Miller
It's sort of like AI talent is the problem. $100 million salaries from Meta for every quantum researcher.
- [51:25]
Zach Yaru Shalmi
It is talent. And this is the only thing. If I was to create a metric to follow for this, I think of everything in terms of like iteration loops or cycle times. There's a commercial cycle time, right? Which is how long does it take for you to sell the company for lots of money. That's super important because that gets venture investors excited, new startup founders with great ideas excited. But then there's the other cycle time around technology. How long does it take for you to go from an idea to a widget to a product and test that in a relevant environment? And the reason I focus on that is actually a lot of the bottlenecks there. Some of it's talent, right, that can be a real bottleneck to it. But actually a lot of it can be access to certain technical services and certain capabilities like a superconducting fab, certain capabilities like certain types of 3, 5 fab, certain capabilities like scaled cryogenics, that it's actually not necessarily bottleneck by talent, it's bottlenecked by the right sort of policy. So that's the model that I have for it.
- [52:37]
Chris Miller
Well, this gets back to the discussion of if we've moved from science phase to engineering phase, not discounting the future science that has to happen, but do we have the right institutions for that scale up? And I think in the semiconductor space there is agreement that it's gotten way too hard and expensive to take an idea and translate into a prototype, because prototyping is expensive and you need all this exquisite equipment and materials and whatnot. The same is true, I think basically in the quantum space, that idea to prototype is hard because prototype is expensive and needs this unique tool set. So to talk to us about what has happened and what else needs to happen to facilitate that scale up process?
- [53:16]
Zach Yaru Shalmi
Yeah, it's a good question. I was actually chatting with Constanza about with her quantum supply chain paper. The short answer is I think we're, we have the cards that we have in terms of the institutions in the US the western world behind it. You have fundamental research and there's, we should be thoughtful about the sorts of things that we incentivize with that. You have the free market and the private companies that are racing at this. I think if I could create one institution it would be an imec. And if folks aren't familiar with that, it's canonical in the semiconductor industry of having institutions that are public, private, nonprofit. And they focus on this liminal intermediate phase after fundamental R and D and pretty competitive with the market. And they just get good at that middle TRL phase. And it turns out that you need to, I think have institutions that all day long they just build that as a craft. And that's both an expertise, that's a capital structure, that's capital, that's, you know, physical capabilities themselves. You need specialized instrumentation that's only good at that phase. And so from an institutional basis, I think that's the one area that I, I focus on for this as a missing potential piece.
- [54:50]
Host
Well, given that I think Costanza is going to be our next in our Quantum series, why don't you sort of tease and pitch her work a little bit.
- [54:58]
Zach Yaru Shalmi
Zach, the teaser for this is and, and Chris I think is going to be the MC for the release of the report so that there's more than one person that can sing her praises. Constanzenza Buccamonte is, I think on every dimension the leading quantum policy researcher out there. She's at cnas. She did a definitive study on quantum sensing which everybody on the planet should read. It just went breadth and depth, the best out there. And then what she did as a follow up is a lot of folks focus on quantum computing. That's great. Right back to the drug discovery analogy. You need to focus on the individual drugs that cure cancer or whatever they do. But she wanted to drill down and look at the quantum supply chain. So what are the things that enable us to develop these quantum computers themselves and use as a framing the how to stay competitive from how do we lock down a capability in the supply chain? And I think the report is coming out in March. Again, Chris, you're, you're actually closer to this than I am. But it is a must read for anybody that cares about advanced technology, policy and these sorts of competitive advantages.
- [56:18]
Host
All right, Zach, so before we sign off, I would love you to make a pitch for the syllabus that we're going to put in the show. Notes we already talked about the but what IS Quantum Computing YouTube video as well as Costanza's recent reports. What about the Quantum Classical Divide? Systems engineering bottlenecks?
- [56:45]
Zach Yaru Shalmi
There are a number of them. Quantum Classical Divide is just fun weekend reading on us being on the cusp not just of these fault tolerance systems, but really a better understanding of how the atomic world adds up and meets the classical world that we're all used to. Probably not the most important from your from a policymaking decision standpoint, but pretty cool as a, as a human being. The System Engineering bottlenecks I actually I think is one of the best breadth and depth deep reviews of quantum computers as a system and where they fall down and where we need to prioritize. I I would say it's more with an a, a research academic lens to it. And so Constanza's report is a really nice complement because it goes a little bit more with the policy dimension or policymaker dimension on what do we have to prioritize? There are a couple others which I think are fun and very kind of talk ask which is when we cease to understand the world. Quantum breaks your brain a bit. This book is probably the best that I've come across at what it's like to be in the mind of somebody whose brain has broken because of quantum and probably last and again, not very germane. This is more weekend reading, but it felt like a very chinatalk recommendation because of the social history of the Machine Gun is effectively that sort of approach applied to the early history of nuclear fusion. Again, fun read. It turns out that you know, through weird and wacky accidents, those can be the difference between, you know, life or death for some of the most important programs of our time. And I just love that little lens of the world.
- [58:47]
Host
Are there good Quantum podcasts?
- [58:51]
Zach Yaru Shalmi
My favorite out there. I'm just pulling up my podcast list now. There are lots that can be more or less advertisements for companies which are great and we love these companies. Podcast is new Quantum era.
- [59:11]
Host
So Zach, what can you give us a little anthropology of quantum researchers? I mean, what brings you down this path? What kind of personalities do you get relative to other fields?
- [59:28]
Zach Yaru Shalmi
I'd say one of the biggest learnings in my career is that the people it takes to solve every particular chain of an innovation cycle, you need a different personality for every single type. There's a kind of infamous distinction between theoretical physicists and experimental physicists. Theoretical physicists are like, you know, locked away in some room with a bunch of chalkboards and their dopamine hit is, you know them with chalk and paper or whatever it is. Experimental physicists are different, then again, because they work with teams. And I think a mindset shift, right? What determines all of this is where you get your dopamine hits from. And if you are a fundamental researcher, you don't get your dopamine hip from reliably solving a problem. Right? Because the definition of fundamental research is you don't know when you're going to go and solve that problem. You get your dopamine hit from asking an interesting question and finding something out interesting about that. Now that we're transitioning into an engineering field, it's a very different mindset because engineers often get their dopamine hit from solving a very specific problem that folks have solved before. And it's all about working through it. What I would say for me, oddly enough, it's. It's different. I'm not coming from that. From a science angle, a lot of it is trying to find the right mental model. It's being curious, right? And finding the thing that I can never fully scratch the edge of curiosity on. And then it's trying to find the right mental model to meet that moment. And the last is this really transitions to a totally different phase is folks with a sales discipline. And there it's about winning deals. And so I think the fascinating thing about really anything that you're trying to do that's a team sport, but particularly so with Quantum is you need to align folks with not just wildly different expertise, right? Just like rote knowledge, you need to align folks with wildly different passions and motivations and get them all to work together. Because you have to solve things all the way from the fundamental physics through making a killer deal and a lot of money. And that's cool, but that's also hard.
- [62:11]
Host
But this is not the path of least. Or is this the path of least resistance? If you're a physicist and you want to do cool stuff nowadays, I mean, like, do you see talent being drawn to the field thanks to the breakthroughs in recent years?
- [62:29]
Zach Yaru Shalmi
Yeah, just not fast enough. There's a famous stat that for every three quantum posted jobs, you only get one qualified candidate. That's hard. That's because there's a lot of demand for this stuff. We need to, we need to address that badly. Now the issue with addressing that, especially on These timelines is it takes five to seven years to get a PhD. And so if we're going to surge resource to this, it gets back to like you can spend infinity money, you can't compress the timeline for a PhD from seven years to two. So we actually have to address this in a very different way from a talent perspective.
- [63:14]
Chris Miller
So what would you train more of today? Because I think you said, which makes total sense, like you need salespeople who can sell quantum capabilities just as much as, or not maybe just as much, but alongside the people who can actually do the fundamental engineering. If you could train x thousand number of people in discipline A or B, what would that look like?
- [63:40]
Zach Yaru Shalmi
Quantum system engineering would be wildly important. The other bottleneck would be technicians. Found out something fascinating kind of recently that if you are a technician or an undergrad, even masters, if you're trained in quantum, it's all theory based because the physical systems that you need to do, the training are so expensive and so exquisite. Nobody's going to give a bunch of students access to this thing that if they break it costs a couple million dollars. I get that. And also that means that when you graduate you get hired by a company and you kind of have to get trained from scratch because you haven't had access to the physical system in the first place. So I'd be prioritizing those roles. And then things like physical access. It's actually a good news story because we can, we can do something about that. You can get access to these physical systems. You can spend the money and solve your problem. I love those sorts of problems.
- [64:37]
Host
So Claude tells me post Willow, the realistic all in cost for a RSA breaking Quantum computer is 10 to $50 billion.
- [64:50]
Zach Yaru Shalmi
So you know, so cost per calculation is super important. It's one of the factors that they're that the government is trying to assess just on will architecture. Absolutely. But there are some leapfrog capabilities that would wildly bring that down.
- [65:11]
Chris Miller
Yeah.
- [65:11]
Zach Yaru Shalmi
But back to the biotechnology. It costs what like 1 to 4 billion dollars per approved drug. So you know, 5 to 10 billion dollars for the first computer that can break RSA kind of makes sense. The human genome project took how many billions? So it kind of makes sense in
- [65:30]
Chris Miller
my point about cost of computer is only relevant if you also know cost per calculation seems very important. How do we think about the spectrum of outcomes and time horizons on cost per calculation going to change over time?
- [65:46]
Zach Yaru Shalmi
It's hard. It's really early to say. One of the things that the QBI is really trying to look at is across these modalities, how much does it cost in order to do that calculation and does it make sense for that problem? I'm making this up, but if it costs $10 billion in five years to do a physics based simulation of penicillin, probably not worth it, right? But if it's an architecture that it costs, you know, a week and a million bucks and suddenly it starts to look very different. And there are some architectures that have a hope of reaching that cross profile and there are some that just don't. And so that's one of the cool things to assess. The one thing I'm curious on is Chris, we alluded to this before and the take that I would love from you guys, actually maybe two is first is a stand in for cycle time, a useful metric as a North Star for industrial competitiveness. I can get more color on that. But I'm curious for your take on
- [66:58]
Chris Miller
that cycle time means cycles per version of a computer?
- [67:05]
Zach Yaru Shalmi
No, I'm thinking of it slightly different cycle time as how long does it take for you to make your product? Basically got it.
- [67:13]
Chris Miller
Well, I would think about the. That's like one input into a broader rate of innovation, rate of improvement, but only one input into it. So I guess it depends also on, you know, cycle time is important, but also the differential between cycles is important. And so if you've got a long cycle time but a huge improvement between cycle that's probably okay. So like it takes you a year at TSMC to move to the next node, but your next node gives you a big bang in terms of improvement. Maybe that's fine if you're. The longer your cycle times though are it's problematic if your differential is smaller. I think those are the two key ways I would think about that.
- [67:51]
Zach Yaru Shalmi
So I'll give a specific example. So in order to build one of these systems for one of the main modalities, you need something called a photonic integrated circuit. Constanza talks a little bit about this in her report. I think a lot. And today think of that as the photonic equivalent to an integrated circuit. So to build scaled systems, you're really going to need this thing for some of the largest players out there because of what they need. It's not just about availability. They can actually get access to a pickup. The cycle time on that can be 12 to 18 months. Whereas if you are next to one of the fabs that make this and have good availability, your cycle time is like a matter of weeks. And I think. And again this could be very Wrong. But if I look back at the advantage that China had in 5G and a bunch of these other photonic technologies, the defining advantage was that they could basically build and test products order of magnitude faster than the American equivalent. So Chris, I love the characterization of there's like both how, how fast can you make a thing and test it and then the second is how capable are you to learn from that thing. But where I see the kind of fundamental shortfall in the US we're really good at the second one of learning because that's, you know, we have a bunch of smart Americans and we can leverage brains across the world, but the cycle time is where we are wildly far behind. And it's not just an availability thing, it's also just a how quickly we can get the thing. And so for these games like Quantum, where we have a really small margin of error, I think my, my, my pitch or the thing that I'm focused on is how long is that cycle time? Because if we can think of, you know, the Euler diagram of life of it's important and we can do something about if we could wildly reduce the cycle time, then that's the best kind of engineering esque measure that we could have in order to cement a certain competitive advantage. It just gives us like a non market but very clear signal on where do we prioritize, like what's holding us back from building this system fast and how do we. It's not fun.
- [70:12]
Chris Miller
What's the limit to cycle time today? Is it that um, the companies that are producing component X or Y don't see Quantum as core to their business. They've got other customers as well that are buying at higher volumes and therefore they don't want to prioritize it because it's still kind of science project in size versus commercial customer. Is that the main reason?
- [70:35]
Zach Yaru Shalmi
Yeah. Or there, there is almost no market clearing price that would make it valuable for them to do that thing, you know, like what we're building with this federal ward. It costs us, you know, 40 to $50 million to just stand up the fab for that. And we hope and pray that we're going to make about a million bucks a year. And there's, there's kind of no. Like on the one hand if I was a commercial company or I was a vc, putting money into that thing, that would be, you know, big no go. But because this is a nonprofit, then doing that is really, really valuable. And so I think looking across, it's stepping back and it's saying, hey, Is there just a short term market that needs to be met for these? And if so, the private market can go take off and take advantage of that. But if not, do you need something, an institution that continues to focus at that, that place, that intermediate TRL stuff and in semiconductors, basically every leading semiconductor ecosystem that I can see has institutions that operate in that public private partnership that on a VC basis don't make any sense, but on a national competitiveness and economic competitiveness for the companies they work with make lots of sense. And IMEC is there and cast over in Korea and Taiwan has their kind of equivalent of an imac to it. China has loads of these. That's the thing that, at least on current trajectory, that's the one area that I, I worry about getting overlooked if we don't have cycle time as our defining metric.
- [72:17]
Chris Miller
No, I totally agree with that.
- [72:19]
Zach Yaru Shalmi
Yeah.
- [72:20]
Host
All right, well, thanks to the Hudson Institute for bringing out this episode. Thanks, Zach, for, I don't know, getting us started with our quantum journey. That's a nice way to kick off 2026. All right, more to come.