Technology, Connected - How 12 Qubits Became a Billion-Dollar Quantum Computing Race

Episode Date: June 19, 2026

Can you spot real progress in quantum computing, or are you falling for the noise? In today’s show, Dr. Bob Sutor takes us on a masterclass through the hype and conflicting headlines to the reality of quantum technology today. By the end of the show you’ll know what’s fact and fiction, where the industry is heading and how to protect yourself from AI slop masquerading as insight.We also learn about:Why “quantum supremacy” and “quantum advantage” claims should be treated carefullyWhy quantum computing is still in its prehistoryHow engineering discipline is changing the fieldWhat IBM and Cleveland Clinic’s protein simulation work shows about quantum chemistryWhy money, sovereignty and technology are driving the quantum industryHow governments are funding quantum computing in the United States, France, the UK, Finland and elsewhereWhat China may be doing in quantum computingWhich industries are doing serious quantum researchWhy there are many quantum hardware approaches and no clear winner yetWhether helium-3 supply matters for quantum computingBob also explains why quantum computers will be useful for specific classes of difficult problems where classical computers struggle, especially once systems become larger, more reliable and fault tolerant.The conversation ends with practical advice on separating the quantum noise from the Thinking On Paper signal.Please enjoy the show.--Thinking on Paper is a technology podcast about AI, Space, quantum computing, science, and the systems shaping your life. 🏠 Buy us a beer on Substack🫵 Choose your own technology adventure 📺  Watch our beautiful faces on YouTube 🎧 Remember Steve Jobs on APPLE: 📺 Get clips and exclusive videos on Instagram (00:00) AI Quantum Slop(00:40) Welcome To The Show(06:04) When Quantum Computers Finally Become Useful(10:13) Why Governments Are Throwing Money at Quantum(15:53) Is China Ahead in Quantum? (18:48) Where Quantum Might Actually Matter(27:11) Can Quantum Help Fix Climate Change?(28:35) Why Battery Companies Care About Quantum(30:31) Why Quantum Doesn’t Belong in the IT Department (31:53) Who’s Doing the Real Work in Quantum?(32:40) Why Quantum Companies Need Real Customers(38:19) The Funding Problem Behind Quantum Progress(45:22) Does Quantum Computing Need Helium-3?(48:01) Will We Need a Quantum Computer Matchmaker?(50:38) How to Spot Quantum Hype Before You Share It

So you see something and it's about quantum. If it's obviously AI generated, stop reading. Just don't read it. Just pass it by or block or say you're not infinite. Just ignore all that because they almost all have egregious errors. Whatever the height, right, they have egregious errors. They often praise results.

There's this amazing one that keeps popping up about this stunning result in China that only happened four years ago. And I think we know a little bit about it. that. So throw away all that. Bob Soutor, welcome to Thinking on Paper. Thank you for thinking on paper with us today. Thank you. I'm happy to be here with you guys. In the past 12 months in all your world travels in the quantum realm, what is the most

impressive experiment use case or quantum computing nugget that stopped you in your tracks more than anything else? What's stopped you in your global travel? God, oh my God. Honestly, to answer slightly negative at first, sometimes I think people are aiming more for the history books than anything else. You know, here's the quantum supremacy du jour. Here's this week's quantum advantage and things like this. For me, I think it comes down to basic engineering, right?

So people who are really doing very high quality. work on this innovation. So science can be messy. Science, you know, you can't guarantee scientific results. I was a mathematician. You know, at some point my advisor said, on Monday, your dissertation will be finished. And this was Thursday.

I was like, oh, really? Okay. Amazingly it was. But now seeing the clean processes that people are using to advance a lot of what's happening in quantum. So bringing real, engineering, so fabrication, for example, trying out different vendors, A, B, testing on different elements of what goes into an actual quantum system. So we are a long, long way from not having

any more science. In fact, I will never end. But it's engineering discipline that I see when I travel that really gives me the hope that we're moving towards something solid. Can you talk us through what you mean by, you know, you reference science and then what you mean by engineering as it relates to what's going on in quantum. Sure. And there's the third element, actually, which is the mathematics. So for a long, long time, mathematics and physicists have usually happily joked with each other as to which is more important.

I'm a mathematician, so it's pretty clear, which is more important. But there's another interplay which is between scientists or physicists and engineers. And the physicists will say, well, this is important. impossible or this will take a long time until we figure this out. And engineers say, look at what I just did. It works. Right. So there's this natural tension between theory and implementation. And we see that all the time. We see that in some of the recent papers about the reduction in the number of cubits one needs to do Shores algorithm and things like this. These are all theoretical, right? Under the most perfect conditions of this and that and whatever, Yes, we believe that we can do this, but oh, by the way, none of this actually works today.

But then the headlines scream, it's done. It's happening Monday. Right. And so that is what is lost on people. It is not understanding saying the theory, this is how the pieces fit together. But again, interpret this in theory, right? But the actual building of it. And I think we can see this today because what is, what is? the number of cubits anybody needs to do anything. You know, it's thousands. It's tens of thousands, hundreds, millions. And people proudly say, look at my 12 cubits. We can extrapolate from here to a billion. It's like, you've got to be kidding me. It's like they're on one side of the Grand Canyon and say, oh, I've got 10 cubits, you know, or, you know, I can jump across that whenever I want to, you know, I've taken two tiny little steps here. So that's the, that's where a lot of the hype comes from as well, is people not distinguishing

what the honest innovation from the best people on, the science for quantum. Does that apply to IBM in their recent Cleveland Clinic trials with the protein simulation? Well, that's solid work, right? Because part of it,

the science part, is understanding how to actually represent the information about the chemistry in the quantum computer so you can compute with it. You know, I would challenge most people listening to this. Say, you got a big protein. Okay, putting it in a quantum computer.

What the heck does that mean? What does that mean? So, even we start talking about cubits and superposition and this and that and whatever and Hamiltonians and, you know, all this fancy stuff. It's just like, oh, yeah, I got it stuck a protein inside the computer and was amazing. It was great. And things like this. So a lot of those things, like, yeah, it's a very. nice achievement, but we're learning about how to do this. I'll also say we are in the prehistory

of quantum. Someday in the future, and I hope to be around, in fact, this is what I'm trying to do with everything I do is to speed things up. We will be in, let's say, the golden age of quantum computing, where the systems are big enough and, you know, high enough Fidelity, that mean they do what they're supposed to do without losing information. It's going to be fantastic. All these promises, right? And we can do this, that, and whatever, and you won't even know. We're not close to that. We are in this prehistory, this very noisy period. I think a lot of the things that people are doing today that they are loading, yeah, they're milestones, but they're going to be completely forgotten and ignored and thrown away. They're important to the innovation, the understanding of the science, the early stages of the engineering, but it's like going to be the ancient days. It's going to be like looking at a black and white television in 1950 compared to this screen we're looking at right now, you know, and saying in 1950, oh, we're so close. We're so close. Right. In one year, two year and three years, we're going to have flat panel displays with, you know, 4K and this. It's like, yeah. There are two different versions of

quantum coming to life right now. There are people harnessing what quantum can do right now and building businesses around that that are getting funded and that are doing pretty interesting things, but then they're also like a whole other sector that are pointing to the future with fault tolerance. What happens to the companies that are building businesses around today once we hit fault tolerance? Well, they plan to evolve, I would hope, right, because the systems today are the raw metal, if you will. They're really low level, right, at the hardware level. I gave a talk. I mentioned I was in Slovakia several weeks ago,

and I started my talk by saying the three most important things in the quantum industry today are in this priority order. Money, sovereignty, and then, oh, yeah, technology. Money is a huge bit. So if you're a startup, you need money. You have to survive, right? So, you know, you can ask this very question of a company,

what are you doing merely to survive, to keep paying the bills, to keep buying whatever hardware you need, right? Things like this, paying your people. We're seeing an awful lot of investments going on. We're seeing a few specs. People getting pretty good size Series A, Series Bs, and things like this. Lots of bets. FOMO, you're missing out. That seems to be flipping on and off month by month.

right? It's like, oh, everybody who's going to invest has invested. And then suddenly there's this great flurry and things like this. So for a lot of those little companies, right, they need to have something that works as soon as possible. They need to get paying customers so they can survive as companies. Now, unfortunately, a lot of the paying customers are universities. They're really kind of the esoteric research parts of enterprise. you know, there's not mainstream. It's not the part of the enterprise that does like the databases and the security. It's like the research hands. And that's not going to be the mainstream revenue. I mean, it's just like data centers and systems today. Eventually, it will be real use of a lot of these systems. And so another area of hype related to this are the huge market projections.

Quantum will be worth X billion trillion dollars. and so many years. B.S. Yeah, I'm sorry. You know, you take a lot of money, a lot of numbers which you've made up, multiply them by other numbers that you've made up, and you add them together,

and you confidently say, this is the market size. Yeah, right? You want a bigger number, I can find someone. You want a smaller number, I can find someone. Right. So money is driving, you know, is behind survival for a lot of this.

It's an investment. We saw the U.S. last week. say that they were going to do a lot. We saw France. They're saying smaller things in Finland, Romania, actually. It's going to spend $100 million. So money is so, so dominating everything that's happening here. And I think it's hiding a little bit of who are the best and who maybe will survive.

Maybe who shouldn't survive because they just don't have a hope of competing. You mentioned the second pillar being sovereignty after money. Could you just explain what you meant? by that first? Well, so why did the United States, for example, say that they were going to invest $2 billion, right, in nine quantum companies? Well, two of the nine IBM and global foundries, which get $1.375 billion, so the vast majority of this, is for foundry work related to this.

And so, you know, we can look at the past of what's happened with semiconductors and foundries and overseas requirements on the technology, things like this. And then they were to smaller companies, all which were mostly American, and then there was Dirac, which was Australian. But that's a special case because it's Silicon Spin, the least advanced modalities. France then turned around almost immediately and said, hey, we are giving this much money to French companies. You've got Prokure in the UK, which is yet another billion, which is a competition, which many people have pointed to as the right way of doing this.

Why are government officials picking winners and losers as opposed to going through rigid competitions of who is the best technology? The U.S. has done that in the DARPA benchmarking initiative and things like this. Finland spends an awful lot of time talking about how wonderful they are in quantum and things like this. Countries don't want to be left behind in whatever this huge market is going to be with quantum. It's also a national security thing. So if you are a, let me just say for the sake of, you know, a major country who can afford a billion-dollar quantum computer, where are you going to get that from? Do you want to be homegrown?

Do you want to buy it from the U.S., from China, from somewhere else? Is that going to restrict what you can do? We talk about these great things like chemistry. You mentioned IBM, a Cleveland Clinic. All this bit about national intelligence and cryptography and things like this. Do you think intelligence agencies aren't paying attention? Do you think they don't want large quantum computers to do fun things with? Of course they do.

So it's economics, which is what they always say, workforce, which is great, and then it's national security issues. And that's the sovereignty angle. And damn it, we're not going to have to pay somebody else to give us a quantum computer. What's happening with Chinese quantum since we're speaking about national security? I have tried to answer that question various ways through the years. And no matter how I answer it, somebody has a problem with it. We have no problem. No, I'm going to answer it.

I'm just telling you right ahead, and I'm warning your listeners, right? It is the most controversial take. So what I said, the last time I gave a talk, was from what we understand from public sources, and what we can imagine regarding the quality of science around the world, they're probably roughly speaking where we are. You know, that is the West, the United States, Europe and so forth and things like this. Okay, well, that's the contrary. No, Bob, they're further ahead.

No, Bob, they're faking it. They're not further ahead, right? You know, so, you know, it's like... They're wallowing in our dust. Well, all of you get on one side of the room and the other get on the other side of the room and then we'll deal with it. But the fact of the matter is we just don't know. And I was taught by a former CIA agent to always insert this from public sources. Because, you know, we don't know.

Now, what I have observed is occasionally there's an announcement of this computer, whatever, and the information is almost impossible to find. Right. So it shows up in some newspaper or news outlets somewhere. And maybe there's one professor talking to somebody. So is this really a company? Is it manufactured? Is it really just research and things like this?

So although I said there were 95 companies, how many of those are really serious, I don't know. But I think it's a mistake to say that they are behind where we are. Let's put it that way. That's probably a reasonable thing. We don't know if they are truly ahead. But we have some... We need some Chinese quantum dissidents to leave the country

and come and share their secrets. Well, does that happen? Is there any history of that? Well, they haven't talked to me. I don't know. Well, we do know is, I mean, and then this is true in general. It's not just Chinese. You know, people come to the United States.

They go to the best universities here and Europe elsewhere. And then sometimes they go back to whatever countries they happen to be from. China did bring a whole lot of expats back a few years ago. There were announcements of somebody from Australia, for example. who went back to China to lead. But there's always movement. And this is what I'm saying. It's hard to really say.

And are they making the progress? I will tell you a story. I gave a talk in Beijing, I spent seven years or so ago. And it was back before Quantum had this kind of international geopolitical thing. It was all just nice interest in science. And I gave a talk. And then I was sitting at dinner with a Chinese scientist, and he leaned over to me and said,

every time you people at IBM make an announcement, the government calls me up and says, when are we going to do that? Right. So maybe that's not the biggest indication, but they are certainly paying attention and we are paying attention as well. But I will say that we, what everyone call us, the West, whoever, boy, we can't help telling the world every little tiny thing that we do. Right. So if there were a country who had a great quantum development effort and was really doing superior work and decided, we're just not going to tell the rest of the world, because why should we tell the world we're close to having a system that can break encryption? Whereas the United States is out there. Oh, yeah, we just did this. We increased it by one cubit. You know, our science is like this. This is the modality. Here's a paper on exactly. Yeah. So it's a real strange contrast. We just don't. No. Are humans going to be the bottleneck to the adoption of quantum when you think about our ability

to ask new questions that quantum is capable of answering? It's like a whole new lens that opens up. Could we be the bottleneck to that? Some people are concerned about the actual breadth of algorithms that people are or may use with quantum. That is, in classical computing, you know, we have tens, hundreds of classes of algorithms and then thousands of different limitations. Quantum, the number is much, much smaller. If your listeners want to go and look at them, there's a website called the Quantum Zoo, which lists the algorithms and the variations and things like this.

This is one area, as we spoke about before, that I think some of the methods that we use now on these noisy computers, the variational methods, those who are largely, I believe, be thrown away once we have fault tolerance. Quantum is not, you know, as we've been saying for 10 years, quantum is not a replacement for classical computing. You know, to control the bits and the pixels on your phone, we don't need a quantum computer, right, to have a crystal clear display, right? But for certain types of problems that are large enough, we think quantum will win.

So if it's not one of those problems or if it's a small problem we can do otherwise, don't waste your time doing quantum. So there's this threshold, right, where quantum will make sense for certain types of problems. Otherwise, the classical methods we do now are just fine. You mentioned that there are hundreds of classes of classical algorithms. Could there be one day hundreds of classes of quantum algorithms? Or is quantum limited by nature? Well, it is nature. Well, that's what I mean.

The fact that it is nature is it limited down, so it doesn't need all these many algorithms because it could just have a few because nature is more perfect. I suspect quantum is going to be a little bit more like AI just on the algorithmic front. So think of all that, you know, the chat GPTs, the Anthropics, the this is whatever. But first of all, people are far more expert than I am saying, you know, in 10 years, we won't be doing anything the way we do it now in AI. We'll be smart. But nevertheless, it's all about the models. It's all about the training.

It's about the parameters. While we keep improving the raw infrastructure of the implementation, it's the numbers that feed into these algorithms that adjusts the outcomes. So it's like you have a framework that you then pump in a lot of numbers. and depending what those numbers are, your answer will come out. I think quantum has some of those characteristics, and that's where I think quantum and AI will intersect. Well, one will help the other one. That is, how do you set up the context

in which the algorithm can run and deliver your eventual result? Those are the parameters of the numbers that I'm talking about. But so far, you know, I mean, Dancing with Cubas, which, you know, my book, which you mentioned, yeah, it has a dozen standard algorithms. And you look at other quantum books and they're all the same 12 algorithms, right? And these don't go back that far. Whereas you pick up a book on classical algorithms, it's seven, 800 pages that can go on and on, this variation, that variation, and things like this. So I think it's a different class, let's put this way, of kind of algorithms. But always reminding us all that computing is computing and get the job done,

use whatever algorithm makes sense on that type of processor. If you're using a GPU, you darn well better be using GPU appropriate algorithms and not single-threaded CPU algorithms and things like that. In one of your recent reports, you mentioned 16 real world applications of computational categories. We've spoken about Cleveland Clinic and IBM. That's the most famous. It's the most well-known. Could you, what are your favorite?

From those 16, some lesser-known domains, some lesser-known research and experiments which are happening? One of the three general areas. Let me first start by saying, these are the three main. areas where we think of applying quantum computing, and then I'll spread them out a little bit more. So the first area we say, well, artificial intelligence. Why do we say that? Well, from the earliest discussion, there's a lot of money than there artificial intelligence. You better be working there, right? But really, down deep and a lot of AI and machine learning, it's a part of mathematics, a linear algebra. Quantum computers are essentially linear algebraic supercomputers.

Okay, so therefore, the type of math that quantum computers naturally do very well, applies itself very well to machine learn. Well, what else? Well, computational fluid dynamics, CFD, which I think is a great phrase. And what this means is a liquid or air. Imagine you're driving your car and the air is flowing over your car. And you want to be, quote, aerodynamic. Well, you design the car in a certain way. It's not random, right?

That science of understanding how the air flows over is computational fluid dynamics. It turns out it's very complicated because air gets turbulent and, you know, it hits one surface and bounces onto another and things like this. Well, so a lot of people are looking at quantum computing to use CFD, and now we bring in the automotive companies. We bring in the aeronautic companies. Uh-huh. Okay.

So we've gone from, uh, looks like AI, to a lot of other areas of water around ships. Ah, maritime. Right, so you can kind of see how this is growing out from the core application area. We mentioned chemistry a little bit. This is apples to apples. So nature is quantum.

You are quantum. You are a quantum app, if you will, right? So, and there is an area called quantum chemistry, right? So therefore, we're going to start with chemistry. The great big hope is drug development, you know, personalized medicine. We're talking about the history of computing. Every single big disruptive thing that has come along in the last 30 years is going to give us that personalized medicine, right? We know it.

And quantum's the next one to do it. Okay. But the idea is to saying, can we actually do the chemistry in the quantum computer as opposed to in the lab? Okay. Well, it's a drug development material science. New alloys. How about batteries? Lithium sulfide.

Well, lithium and sulfur are both pretty small elements in terms of the molecules, in terms of the atoms and working together. Therefore, that should be amenable. Small projects, small things will work faster. Ah, batteries. Ah, energy industry. Ah, saving energy. Ah, electric cars.

Ah, okay. So we've gone from our quantum chemistry, and now we've enlarged once again into the various, right? Energy networks, where are you storing this information? You're just describing it like the last 10 episodes of thinking on paper, Bob? Well, you know, I mean, this is how you know you start at the core and you start saying, well, where do we use this? And the final area, which I'll highlight, there are a couple of smaller ones, is just optimization types of problems. So the sorts of problems, combinatorial optimization.

These are the things where you say, you can start with a relatively small amount of data, but as you start considering this, the problem just blows up, blows up. The traveling salesperson example, people tend to know, I give you 50 cities, you have to compute the shortest path going through each city once and every.

Once, only once, and then return to where you came from. Well, that's a great example. Quantum doesn't actually help you at all, but it's a great example. Anyway, but there are lots of these types of combinatorial problems. And in fact, many of them are in finance. Many of them are related to like pricing financial derivatives.

Risk assessment is another one, right? You want to build a factory. You'd like to have it built in three years. What are all the possible risk factors? Well, you have to weigh them all. There's a 10% chance of this happening, a 20%, right? You have to have an environmental study. What's the risk of that slowing down?

So many interacting factors, and they're not independent, right? One can affect another one. And just alone, risk assessment is like, well, that's every business in the world. Because everything you do is a risk, right? And things like this. So it's in these areas. But once again, I do want to emphasize, they're only problems that are truly big enough

that our classical methods can't just solve them anyway. Is anybody working on climate change specifically? Oh, they say they are. I know. But are they? I don't know. Honestly, it's really hard to say what it really means. A reason why I'm being a little hesitant about this

is because seven or eight years ago, people were claiming they were doing it. And as far as I could tell, they were doing absolutely nothing. It just sounded really good, right, as an application area. The fourth area, I said there were three, but here's a secret.

Fourth one is differential equations, which, of course, all your readers, you know, you remember from your course and partial differential equation. Is there always partial to one of those? Well, you know, things that change over time and here you want to think about the weather, right? and weather prediction.

And again, we have pretty good classical methods, supplemented by a lot of sensors. Is it raining over there? What's the wind? Now, 10 miles over here, we can kind of predict. Differential equations actually for many, many things in the universe describe how they evolve. And so how the weather evolves and therefore how ultimately

maybe the climate can evolve. That is an area that I would say within the last year has gotten rejuvenated in quantum. and people are showing some interesting results. So, yeah, it might work out that way. But I would be careful about kind of the incredibly broad climate change as opposed to dropping down two or three levels what might be causing it, right?

And as quantum can tell you anything there. Let's dive into one of those particular examples and let's just say a battery company comes to you and they're seeing all of this stuff in the market like talking. Basically, they're hearing people shouting from the mountaintops about the new equivalent of transistors and how they work and how electrons flow transistors and it'll help them make better batteries down the road. How do you help someone like that balance the minutia of what they're hearing in the market and the application of, man, how do I take the first step to take advantage of something to make a better battery? So the people who would know about this are the chemists and the chemists who are working on batteries today. You know, how does it may same?

You occasionally do have the CEO or somebody or a CTO saying, hey, we should be working on this. We have had automobile companies working on this in the past, Volkswagen, I remember just for one several years ago, because they want to have the best, ultimately the best electronic vehicles, and they would love to have their own proprietary battery technology will last longer, things like this. So you have to get the attention of the scientists who may be working,

non-quantamly to think of you know that is not with quantum computers you have to kind of pull them in and now more and more of them over time are actually seeing the work that's being done so they are enlarging their scientific and to some extent they're engineering

breadth of knowledge about what to do that then it happens you know in any particular company I mentioned Volkswagen but in any automotive company how do you convince the people up high that they should be investing in the research on this, right? And so you have to take it from there. You have to prove the hard numbers of what you think you can improve, when you can improve it.

Do you build it yourself? Do you partner and all the usual types of things? So when someone thinks about a new technology, a new computing technology, you would think that that would, the proper place for that to reside would be under the technology org, the CIO, the CTO, that sort of thing. it would seem to me as we're talking through this, quantum is a resource. If you were to stand up a quantum center of excellence to use a traditional bureaucratic business term, that would almost seem likely to live under like the science and research arm in Oregon, not even mess with IT until it gets to a point where it becomes data, right?

Oh, yeah. I think that's absolutely right. I mean, while people in IT might be experimenting, and here I'll get in trouble again, people aren't solving really big, really important problems with quantum yet. I'll get in trouble because some people say, oh, yes, we are. Yes, we're doing it. Okay. Except those people who are listening who are.

But for everyone else who's not actually doing anything. Yeah, it's research. And that's why in Banks, a lot of the people are the quants and the quants in the research, the same people who started investigating using, well, years ago, improving the algorithms for quantitative trading, right? Then they started doing AI, and now these are the same people, right, in FinTech, who are looking at where quantum may be applicable. If you're in day-to-day operations in a bank or in trading, you may be curious about quantum,

but you're not using it and you're not investigating it. And again, I'm sure there's one or two people who are, but as a general thing. Let's call them out. the parato, the 80-20 principle, who were the 20% who are doing the big heavy lifting in quantum? Which are the companies which are really behind doors pushing it onwards? So most of the pharmaceutical companies are doing some serious work and they're starting to engage and it varies.

I have one of the reports I do end users. So I actually list them which pharmaceuticals and things like this, which are a motive companies and things like this. Many main banks are looking at them. So J.P. Morgan was an early pioneer in doing an awful lot of this. And, of course, I'm going to immediately blank on the names of them. But those that you would imagine having the richest research departments, HSBC is another one that has done some good one.

Whose hardware and software are they using? The recent announcement they did with IBM. But that's actually, that's a key question because more than the end users, it's the big company. So if you're an IBM, if you're an IonQ, if you're an inflection, whomever, you need users. Okay, so this is your bonus cat for them. Yes. I warned you. That's Chester.

You can answer the question, what is a cat cubit later? What is a catcubit? Yeah. If I make quantum computers, I need users. I need users who are making breakthroughs. So I am going to go and cut some sort of deal with the car companies, with pharmaceutical companies, whatever, to please, please, please try to do something with this.

Please write some papers with us so that others will want to use quantum computers. But there is, again, a research of scientific angles, that the people in the industries are the domain experts. And the people in the quantum computing companies, while some of them may come from a particular industry, they know the computing technology, and you need to mix them well. The situations that, by the way, that don't work well are,

for example, I'm a quantum computing software company, and I have an algorithm, and that's all I know. And now I start going, this is my hammer. And I try to hit every industry over the head with my hammer. like, oh, I can solve your problem. I know nothing about what you do, that I can solve your problem, right? So the proper blending is the experts on the hardware and software working with the domain experts in a particular industry. Is it frustrating for domain experts like scientists, chemists, whomever, to now have to, because they have to get in deep on quantum computing, I think, too, in order to make,

You almost have to understand it enough to build it then to figure out how to use it. Are they going to have to be experts in two things now? The people who would do it are already computational experts. Right. It's not like they never use a computer for anything. And so they know the best algorithms on the best high performance computing systems to get as far as they can. So this is the next natural step. And I would say that for those type of people, it is intellectually fascinating to be able to do this because it is so much closer to the actual chemistry, the science itself.

Right. And so people like that always get excited. Oh, there's something new to learn. That's great. I can think about new things. That's what it went into the first place. I kind of feel sorry for the chemists and the biologists, but also very envious because the quantum computing industry is flirting for their attention. The AI industry wants their attention.

We spoke last week about microgravity. The space industry wants their attention. Everybody wants them because they are the piece that makes everything work in any particular domain. How do they choose? Yes, they love the curiosity. They love pushing themselves. but they can't do everything. Well, that's what the CTO does, right?

I mean, that's why you... Yeah, to choose. That's why you have to work. If I am a, I mean, I'll call it rank-and-file chemist, just meaning that I'm actually doing the chemistry, maybe I'm a manager, I lead a group or something like this, but I'm actually doing the chemistry. I'm not doing it randomly because it's interesting.

I have goals, I have projects I'm working on, and those are determined, at least in commercial settings, by people higher up to saying, this is our business, this is what we're trying to do, this is our strategy and things like this. If they are being pulled by their management in lots of different directions, then that's a management problem. I have seen that happen as well.

Like every new exciting thing that floats by the senior office, oh, let's do that too. You know, a little discipline is a good idea. Well, and a lot of these investigators are working under grants and they're working at universities that, have specific obligations to them. So the university might say, well, here's your tool set. We want you to use this, that they've negotiated with one of these, you know, system vendors.

And then the grant is specific to a specific technology. And then it gets, yeah, you almost need an orchestrator or someone in the middle. And I think you said, like the CTO or VP provost of research or whatever, trying to figure out those tool sets. Well, in academia, there usually isn't a CTO per se, right? and grants rule, right? You got to get somebody to give you a whole lot of money to fund your lab, to fund your students, your graduate students, your postdocs,

and things like this. The good news is there's more money going into this, although let me just simply say there have been some unusual things in one or two governments cutting scientific funding. leave it at that. You don't have to leave it at that. Jeremy won't. Well, you know, a country is not going to accelerate and advance if you kill basic research,

as should be clearly obvious to everybody. So there is in certain parts of the world, including in the United States, there's less money sometimes, right, from the government. So you have to find alternative sources. And they could come from, let's say, the pharmaceutical companies are remote. That is, they could be grants from industry to do these sorts of things. But on the other hand, the competition is picking up more and more and more. I have this Quantum Daily Update that we publish.

I say five days a week, but it was seven days last week because there's so much news. I started it myself about a year ago, just so I would stay abreast. And now I just, I bought and made it so much of it. And, you know, it used to be, it's like, here are the five quantum news items today and I'll skip tomorrow. You know, two days ago, there were 32 really good, high-quality links of things happening around the world laid to quantum in applications and algorithms and error correction and sensing and communications and networking and this and that and whatever. So there are a lot of people who are now looking at this. there's more competition. There are more people looking at

because maybe there is more money doing this. I certainly would encourage, by the way, if you are a university researcher, to go out to these quantum companies, to these small quantum companies, and see if you can work out some sorts of arrangements to do research together. With the caveat

that some universities are extremely difficult to work with regarding intellectual property. Yeah, TTO's Technology Transfer Office. It gets complicated. There is many a startup in lots of areas that totally messed up because of the cost and the difficulty of getting intellectual property rights from universities when they started. So that's not a problem you can ignore.

So you have like 16 different, let's call them forces. and you have different subcurrents under those forces. You have currents, right, which are more of a specifically applied version of that. And they're all intersecting in this giant soup. Could quantum computing help model or help visualize the complexity of those interactions? Well, here's a definite maybe. In the sense of, we have to ask some technical questions about your your forces describing them, are you describing them discreetly? Are you describing them continuously? How much

math is there behind there? How much is there for a lot of these sorts of things? You have to actually map it to physics in some ways. A lot of these optimization sorts of problems because they evolve over time. And so even when we look at some of the financial services applications, you may start in the language of finance, but by the time you're running it on a quantum computer, it's an honest-to-goodness physics object that you are computing, and you're looking at how it changes over time, and ideally, when you stop looking at it, you get the answer and things like this. So it's the complexity of how you would imagine modeling it,

just as if, you know, forgetting quantum, let's say you wanted to do this using AI. What would that model look like? What is the data? What are you observing? And again, is it continuous? Is it, you know, points in time, things like this? A lot of people have been trying to do this. A researcher, after IBM won Jeopardy.

You remember, it seems like ancient history. One of the researchers left to join, I think, a hedge fund because he was going to model the entire world, you know, with what we knew then of AI years ago. And that's, that is always the hope. you know, to see everything and it's usual by people who want to make a lot of money, too. I'm just reminded of the Nexus by Julio Tino and the modeling of, it was like three square meters of Pacific Ocean off Canada and just how impossibly chaotic and complex that three square meter piece of ocean was.

And the hubris, the arrogance to say, I'm going to model the earth and everything in it. It's just, I don't know if it's very ambitious, but is it crazy or is it genius? I don't know. Well, we have to keep doing it. We have to keep understanding, and we have to understand what the simplifications are. Right?

So what can we safely ignore? So let's say we're looking at the current flow through that stretch of water. Does the salinity of the water affect the current? I suspect it does a little bit somehow, some way, but is it such a small thing that we can safely ignore it? So just like an AI, right, you imagine the feature set, you know, which of the inputs are high value, which are lower value,

which can you statistically reduce and things like this to get a good enough answer. I think people should realize, you know, they see it every day using like chat GPT or cloud or whatever, you know, they're looking for answers that are good enough. They're not looking for exactly perfect answers. Maybe in some computations you would, but good enough. And in the same way, a lot of what happens in quantum computing is approximations. And is it a good enough approximation that you can do something useful with? It's not necessarily, this is the perfect answer, particularly when you're modeling, as you've described.

Can you get close enough so that you can infer something interesting and then act on it in some way? I love that idea of what can we ignore. I think we should hold on to that because that's pretty powerful stuff. How many quantum companies did you say they were in the world? I might count there are 95 quantum computing hardware companies. And so these are the people who somehow harness cubits, right? So they may manufacture them, as is the case with IBM and Google, several other ones, Alice and Bob, or capture them, meaning neutral atoms or ions, the inflections of the world, the continuums of the world. and they're about 95 and I'm sure

I'm sure there are a few more little ones maybe I mean new one seems to pop up every two weeks and some of these maybe aren't that real but that gives you a ballpark and last question and it's over to Jamie I can't have a quantum conversation anymore without speaking about helium three Bob you've spoken to all of these companies

you've been around the world speaking to and about quantum how many times are you coming across Helium 3 as a bottleneck. Is it being used? How is it being used? Is it a real thing? Or should we forget about asking that question? If they are worried about it, they're not telling me about it.

And they're not planning one expeditions either. There are only certain types of modalities, as we call them. There are nine ways of making quantum computers. And, you know, some of them advertise as being room temperature. except when they're not room temperature. It's like, it's my refrigerator room temperature. Well, sort of, right. The big systems like IBM, the superconducting systems,

the silicon spin systems, yeah, they need sort of really super cooling down. Other systems like neutral atoms will eventually need a little cooling, but not nearly that much. So you shouldn't look at an IBM and say, all 95 companies will need it. this much helium-3 or lipid nitrogen or whatever and the hardware to do it. It depends. Some of the systems like the photonic systems, they're going to need a lot of it, and we will see. And here I'm going to, by the way, unasked, insert, we don't know who the winner is going to be.

People always want to know what's, you know, what is the one? Right now, there are four with a bonus fifth, that seemed to be the best contenders, and they will vary a little over time. We saw this huge surge in neutral atoms. February inflection was the first company that went public to do that. We also saw Google, which did superconducting, say, hey, we're doing neutral atoms too.

And they had previously invested in neutral atoms. So it's like, you know, every week, it's like, oh, there are two more ion companies and things like this. So we don't know. The best way of thinking about it, those were all the applications we've talked about is there will be some things that some of these computers will be better for,

even though the fundamental principles are better. There are also going to be certain types of constraints. So you might say, hey, you know, I want the most accurate answer possible from the quantum computer I use. Or I want a good enough answer at the lowest possible price. Or that uses the least amount of energy, right, and things like this. So they're going to be dials you turn. If I'm a hedge fund, I might say, I want that answer in 10 minutes.

If I'm a chemistry company, I might say, yeah, I'm willing to save money and get the answer in two days. So just thinking there's going to be one type that just blast out for every type of problem that's going to be ideal that that isn't going to happen. So do you think there's like, there's a place for a quantum solution aggregator that can help select almost to the same point of like, here's my question. And like in chatbot models, there's like, hey, use this model, use this model, use this model, and they're all good for different things. Is there room for aggregation to help people figure out what to use? There are. I mean, in the field of optimization, there are algorithms that sit on top of optimizers

that decide the best optimizer to use for certain types of problems. There is serverless cloud computing, which means, hey, I don't know where this thing is going to run. and maybe I don't even know on what type of processor. It could be a GPU. It could be a CPU. It could be something else. That's your job. This is the problem.

I'm going to state it. Here's the data. You go off. Figure out where to run it. Problems aren't, the solutions to problems are not monolithic. Here's my problem. Here, quantum computer.

Give me the answer. No, it's like, well, first of all, you might have used a laptop to submit it. You use the network to get over there. You've got cloud computer. You've got things that convert digital to analog signals. I mean, these systems are very complicated, and there's workflow and there could be AI here.

So it's, we should be talking more about the workflow that involves quantum computing isn't simply quantum computing. And it may determine, in fact, hey, this problem is too small. Don't waste your money running it on a quantum computer. We can do it classically better, right? But ah, it's crossed a certain threshold in terms of complexity, complexity, now we're going to run it on that. So yes, there are going to be layers, just as there are now in classical. It's a fun area, by the way, if I haven't given that. It's a microcosm of all of computing

that at least we can keep in our heads all at the same time still, although that's, time seems to be passing pretty quick. Just a shout out to anyone who's listening that we have interviews with many of the companies that Bob has mentioned, inflection, D-Wave, I and Q, IBM, and video, Horizon, check out, Thinking on Paper. Dot X, Y, Z. Our paths first crossed because, at least in my world, you're known as debunking the quantum hype. So to finish the show, let's debunk together.

Anyone listening to this, the next 18 months, they're going to be bombarded with hype and noise. And amongst that, there's going to be real signal, real value, real thinking on paper episodes. How should people think about that? What filter should they be using to judge quantum announcements, let's say, over the next 18 months? So let's fix ideas and say you're looking at LinkedIn and whatever other social media I would. You know, it's similar. So you see something and it's about quantum.

If it's obviously AI generated and you can usually tell by the beautiful graphic, right? and five word sentences separated by blank lines, right? You know it when you see it. AI slop. Stop reading. Just don't read it. Just pass it by or block or say you're not interested. Just ignore all that because they almost all have egregious errors in them.

Whatever the hype, right, they have egregious errors. They often praise results. There's this amazing one that keeps popping up about this stuff. this stunning result in China that only happened four years ago, right? And I think we know a little bit about that. So throw away all that. Now, when you see a press release by somebody, what I suggest is irreverently you chuckled to yourself and say, yeah, right.

Okay? That is, don't be cynical. Well, maybe that is cynical. But say, you've now got to prove to me that you've really done something useful. and you just haven't wordsmith this thing so much that it sounds amazing. So beware, quantum supremacy, quantum advantage, and things like this, right? Any sort of result, which is going to change the future of computing, don't immediately repost to give it a few days, right? Let some other people sort of weigh in on this.

In fact, there's a result that's going back and forth between D-Wave and the Flatiron Institute. from a D-Wave paper last year, and Flatiron responded saying, no, we can do this on a laptop. And D-Wave said, no, you can't do all of it on a laptop. So there's this ping-ponging back and forth. That's interesting to say.

Just don't, you know, consider it as steady progress. Don't get overexcited. Don't do these things like the future of computing is now. We've moved from theory to reality, that's such. You know, we've moved. It's only an engineering problem. The science is finished.

That's wrong. Right. So I'm trying to give a sense of, you know, if it just smacks of being silly and over, just ignore it, right? There's plenty of other content that is out there. And always go to the original source, if you will. So if you're reading an article and they mention, oh, here's the press release or here's the paper.

Go and glance at that. See what they really said. All of this said, there's wonderful work being done. right the hype comes in for so many reasons people you know want to get attention to themselves people want to be social media influencers you know um find a few trusted sources starting uh in fact this last january on my sub stack i started doing once a week um quantum follow Fridays and i did a couple of after that and i listed people with their linkedin saying follow these people These are real people.

They say, I think by the time I was done, I did a dozen each week. So I did about 60 and I'm due for another one. But those are at least good, smart, thoughtful experts or people who analyze what's going on and start with those. But please, please, please don't just repost AI slot. That's good advice just in general. No, I think that's great. Bob, thanks so much for the context there. One thing, too, I want to remind people of.

of is you had a great quote a few years ago, just setting the stage for all of this stuff and just the complexity of what quantum computing is trying to do and the complexity of trying to make that into something useful that can help humans. Like, we got to the moon faster than quantum computing has come together, right? I think you said it was like eight years. It took us to get from this rock to that rock.

Like, if you put it in that perspective, we're in it, we're in a good spot. So thanks for that. I thought that was well played. Yeah, it's a, I was saying people have to stop using the phrase moanshot. Because we, and now we're 10 years on, you know, at least, right? And it was only eight. So it's, the quantum's going to be with us.

So another quote I, I have, so it's a book called Quantum Supremacy by Michio Cacao. I always mispronounce his name. And the book has its critics, but I like it because I'm quoted on the bottom on the first page. And what I say is quantum computing is going to be the most important computing technology of this century. And I still stand by that. I think that's right. AI will change. It's important.

Yeah, that's not questioned. But as a fundamentally different thing that most of us have not seen in our lifetime, quantum is going to be it. You're a much better guest than Mitchukakut. We did an eight series book club breakdown chapter by chapter breakdown of quantum supremacy. And he never returned our emails. Yeah. Well. Did you know him?

I don't know. I don't know. It covers a lot of topics. You know, as I said, I'm quoted. And he lists my book in the back for further reading. So, you know, my mom always used to say, you know, if they spell your name, don't complain. There you have it.

Thanks for joining us today. This has been a pleasure. Great insight, great peek into the quantum world. I would love to stay in touch as you continue to investigate and uncover some stuff. We're doing the same. So yeah, let's keep it going. Appreciate you being here today. Mark, closing thoughts.

Thank you. Bob Soutor, thank you for thinking on paper with us today. Thinking on paper to X, X, Z for all our quantum episodes, our book club episodes at AI. Jeremy, I'm going to leave you with a final thought, and it's about the travelling salesman. Have you heard about the new travelling salesman problem? I know the old one.

I didn't know there was a revision. Tell me about it. Well, there's a new one that people are brandishing around, and it links to our guest in the next couple of weeks from AstroForge. And it's about essentially the traveling salesman, but for asteroids, to find the most optimal path for mining asteroids.

So you go out into the Kuiper belt and you have to find the optimal path to go jump to each asteroid to check, measure, see what's available to mine it. Don't forget to pack your quantum computer. Yeah. Well, you need a quantum computer. Well, no, because Bob said the quantum computer wasn't very good at that. So maybe you just need a classical computer to solve the asteroid mining salesman problem. But yeah, in a few weeks, we'll be speaking to AstroForge about that. Until then, be disruptive, stay curious.

Keep thinking on paper.