Technology, Connected - How Quantum Computing Could Help Solve Climate Change, Medicine and Energy

Episode Date: January 21, 2025

Kathrin Spendier, Technical Prize Director at XPRIZE, joins Thinking on Paper to explain how quantum computing could contribute to climate science, medicine, energy and materials discovery.She also discusses the $5 million Quantum XPRIZE, a three-year global competition designed to move quantum algorithms beyond theory and towards practical applications. The competition brings together hundreds of teams working on problems that could eventually benefit from quantum computation.In this episode, we discuss:How quantum computing could support climate modelling and sustainabilityWhether quantum algorithms can accelerate drug discoveryHow quantum systems could help develop new materialsThe potential role of quantum computing in energy researchWhy natural processes such as photosynthesis are relevant to quantum scienceHow Google Quantum AI is supporting the Quantum XPRIZEWhat hundreds of teams from dozens of countries are trying to buildWhy competition and technical constraints can accelerate innovationWhat makes useful quantum algorithms difficult to developWhich practical quantum applications may emerge firstKathrin explains the difference between theoretical quantum advantage and applications that could create measurable value in the real world.This conversation examines whether quantum computing can move from laboratory progress to useful tools for science, industry and some of humanity’s most difficult technical problems.Please enjoy the show.Timestamps(00:00) - Welcome Disruptors and Curious Minds(02:10) - Meet Kathrin Spendier: XPRIZE Technical Prize Director(04:42) - Quantum vs. Classical Computing: What’s the Difference?(07:26) - What Is XPRIZE and How Does It Drive Innovation?(14:50) - The Challenges Tackled by the Quantum XPRIZE(17:20) - Quantum Algorithms(27:14) - Who’s Competing in the Quantum XPRIZE?(29:18) - The $5 Million XPRIZE Purse: Why That Amount?(32:17) - Hot Buttons: Rapid-Fire Quantum Insights(33:44) - Are We Living in a Simulation? (33:58) - Top Quantum News and Industry Updates(36:00) - Scaling Quantum Computing(38:12) - Google’s Willow Chip and the Importance of Error Correction(47:16) - Carry-Over Question: How Can Technology Serve Social Good?(49:42) - Mark & Jeremy Reflect: Backstage Thoughts on Quantum and XPRIZE--Watch on YouTube: https://www.youtube.com/watch?v=SJi7aP5B69A&ab_channel=ThinkingOnPaper

Disruptors and curious minds. Welcome to another episode of Thinking on Paper. This is your personal guide to understand how emerging technologies are affecting business, society, and the human condition in general. My name is Jeremy. This is Mark with me. Mark, what are we talking about today? What are we getting into? Today is a story of getting quantum computing out of the lab and solving humanity's biggest.

challenges. So it's a small subject, a small show today. Yeah, if you're working or learning about quantum, if you've been following thinking on paper for a while, you'll obviously know about quantum's potential. We had Google announcing last year that Willow solved a calculation that would take a supercomputer, a billion years. We had IBM on here not long ago talking about 600,000, dare I call them, hobbyist developers in their sandbox. But we need to get the quantum. We need to get the quantum out of the lab and into the real world. We need to harness nature, don't we? We need to replace the Haber-Bosch process. We need to energize the world. We need to put the damn fires out, don't we? So, yeah, we, in today's guest, is going to explain about a competition

that's, I believe, sponsored a bit by Google that's going to incentivize scientists, entrepreneurs, and builders to do exactly that. Guys, keep in mind, too, it's a brand new year. We've got new segments. We've got new segments. We've our hot button questions. We've got our thinking on paper news. We have our debrief at the end. Hope you guys are enjoying that thus far, but we're excited to keep this thing moving and keeping it valuable for you. So let us know how we're doing too. And on the hotburns, there's a really good hot button question today. So you want to stay tuned for that because I think you'll want to answer it as well, Jeremy. I'm in. So who are we talking to today? We're talking to Catherine

Spendier, who is the technical prize director of X-Prize. Welcome to the show, Catherine. Thank you for being here. Hi, Jeremy. Hi, Mark. Thanks for having me. It's going to be a lot of fun, I think. I originally learned about X-Prize when I read a book by Peter D. Amanda's called Bold that was talking about just these really big ideas, you know, how to push the boundaries of what you think you're capable of, what society thinks they're capable of. And, you know, part of that led into this whole idea of XPRIZE and incentivizing and creating challenges, getting really smart, curious, interdisciplinary people together to stir the pot. And I think it's amazing. So we want to get into this. What do you tell your friends, your family, you know, at dinner when you pull

them aside and you want to give them like a little bit of insight, a little bit of a head start into what quantum computing could mean for our society? What do you, what do you tell them? So typically when we have that dinner conversation about quantum computing, I do have it sometimes with my kids. They try to understand it. And I tell them that, you know, calculations take time. And when we solve really tough problems, it takes us a long time for the computer to solve problems, sometimes days and weeks. And quantum computing will be able to solve some problems faster. So we don't have to wait for days and weeks. So we can have answers right away. imagine you go to a doctor, you have a disease, and they try to figure out how to help you with the right medicine, for example. They could do like analysis on your symptoms very, very fast by looking at a lot of data, right? So it's kind of like this kind of like aha moment that we don't have to wait to get answers for a long time.

But I tell them it's not going to be, you know, for everything, we're going to apply it. It's going to be very specific problems because the quantum language is complicated. and you have to talk in the language the quantum computer understands. So that's pretty much it. So we want to really speed up the tough problems where right now we either take a really long time or we can't even solve it exactly because the classical language actually does not really understand the problem well. So we actually want to use quantum language to solve that problem.

Could you give us a couple of examples of what those kind of problems might be that a classical computer just simply can't handle? Yeah, one is, as I think people in the field know a lot about, it's kind of like material science, you know, quantum chemistry calculations. Everything is based on a shrewdinary equation that is purely quantum mechanics. And because of that, the electronic states and the way, you know, you have different energy levels, that language is encoded in a quantum mechanics. So it turns out that the classical compute right now, we have to do estimates. We can't solve it exactly at the moment.

We live with approximations, and sometimes we say, okay, it's like 80% okay or 70% okay. It's good enough for us to, for example, start a clinical trial or a drug or like develop materials. But in principle, you want to solve it exactly because you do want to like make that leap of solving bigger problems. One follow-on question on that. And I'm not a math guy. I'm math-adjacent, math-curious maybe.

Is it true that calculus is actually a lot of approximation, just approximated very specifically? Oh, it depends on what you do. So when we apply, I have a physics background. I have a physics PhD. And when we solve certain problems, we use calculus, differential equations, partial differential equations. And we are building the equations out. We are trying to put in the different terms, like force terms, friction terms, whatever it is, energy terms.

Like we have to estimate a potential energy, for example, has a certain. shape. And then we put that in, we estimate that, and then we try to calculate it. And typically, we can't do a lot of stuff, analytical. Then we have to go numerical. And once you are doing numerical analysis, everything is just encoding, right? So how do you encode like an integral, right? You have to slice it up in small pieces. It's never exact. It's discrete. So in any case, this is kind of like, yeah, I agree with you. If you apply calculus, you have to kind of make these approximation. and kind of put it in in your math. Anyone who's wondering about coding and integral,

I didn't get it either, so don't worry. If you're listening and you want to get into rabbit holes about quantum mechanics and, you know, that we have tons of episodes that Mark will post in these show notes that dive into what quantum mechanics is, because we live and we think in a Newtonian world where quantum mechanics is very opposite that, and that's why it's hard to get our head around things like quantum computing.

What is X-Prize and what are you trying to solve with the friends? So Express is an amazing nonprofit foundation. And it's around 30 years and it's kind of started with the Ansari Express where we tried to get the community like make space travel, you know, open for anyone. So make it available for private sector. 30 years ago, space travel was only a government sector, right? And we really wanted to bring the space travel to the public, right? And then Sari Prize was launched and it took 10 years to get funded.

But in any case, we solved the tough problem of, you know, the teams did of traveling to space for like once with a rocket, right, and come back and do it again within two weeks, I believe. It was a $10 million price burst. But like teams spent millions of dollars, 100 million dollars on the solution. The solution then turned into Virgin Galactic and so on. So pretty much, I think that kind of explains with XPRIZ. It's trying to find something where we as humanity kind of got stuck or something. didn't change for a long time when we're kind of like going with a very old technology or we haven't really like solved a big problem and then we're trying to with XPRIES we try to have an

audacious goal but really push ourselves to like solve something really really tough so that our life on earth is better right so it's an impact for all of us a positive impact for all of us and then we kind of incentivize that with a very nice cash price that would help right yeah that helps right So pretty much we're trying to crowdsource a solution to a really tough problem that helps humanity to make big leaps that we need to make in order to have a fulfilled living in order for us to have, you know, everything we need, an abundant life, you know, for all of us, where we feel like we're, like we're just, what we just, we want to better the world, right? That's why we're here. And X-Prize is trying to solve these tough problems to better the world. Did you say that it's a virgin Galactic came from the original XPRIZE? that? Yeah, the IP from the original express.

This isn't new in terms of competition pushing humanity to better things. I believe, like Phileas Fogg, that was a competition, the old geography institutes in London. A lot of the early clubs of London were built on competition, on challenging explorers, on challenging scientists to break new ground. It's not new. It's been replicated now. What is it about the competition that drives these advancements more quickly than non-requiry? competition. I was a downhill ski racer for many, many years. I tried to become an Olympic downhill

ski racer. And competition just is where to get the best out of yourself. If you were in a field, you were really connected to the price and it resorts for you and you really want to solve the problem and you are competing against others. It just takes you out of, takes you into a certain mindset. And all of these competitions are more than just you solving a problem. It's about making an ecosystem. You have to develop a team that has different expertise, right? For example, like we also have a wildfire excise right now, right? So we have our wildfire specialists have to talk to people who have the technology who can actually, for example, detect wildfires faster and put them out.

So you have to talk to engineers, right? You have to talk to people who understand better patterns. Like you have to bring in different people from domain expertise, the engineers, and then you have to maybe put it together, like even like an entrepreneur, right, who knows how to like implement something quickly. So this is about XPRIZE. X-Price brings these people together because we have a global competition. And we have done it for so many years. We have a very good, I think we're 30,000 individuals that joined the competitions over 30 years. So we have a huge network. And then once the solutions are developed through these milestones, we also bring in investors that can help that the teams develop the technologies, right?

And as we do that too, we're also trying not just to say, oh, they have achieved a goal. we're also trying to come up with the metrics that define, you know, that kind of, what defines to, like, reach this XPRIZE, right? So what metrics do you have to, like, check the checkbox, right? And that's actually, we have to talk with experts in the field. That's not easy. And benchmarking a field is typically very difficult. Sometimes we have a lot of different benchmarks and no one agrees. And we hope through a price we can actually pull everyone together and they all agree, yes, that's a good benchmark. That's a good metric. trick. And then we try to push that moving forward. I love that answer. And I think the one thing that I would add to that, I mean, I think all of us innately have a little bit of competitive vibe, although my wife would argue that she doesn't have a competitive bone in her body, but I think there's a little bit in there somewhere in all of us. But tell me about what you think the power of constraints does within the prize environment, because I'm assuming there's a certain, hey, this contest starts today and we go for this amount of time because a lot of big thinkers

can can like stay up in the air and you know, you got to land the plane at some point, right? So tell me about the power of constraints in this. Yes, thanks for bringing that up. It's definitely like so depending on how audacious prizes and how important it is to like reach a milestone as humanity, we define the price timeline, right? So some prices are for three years. Summers for five, some are for seven, right? Depends on how audacious applies and what milestones you have to do.

For our health span price, you have to do actual, like, you know, critical trials. So that takes time to do, right? We want to accelerate it, but we still have to have time for that. So that's why the price is a little bit longer. Wildfire, X-Price wildfire is four years, right? If that price would have started, you know, four years ago, maybe we could have helped, you know, now the fires in Los Angeles, right? But this is very urgent.

We have to get to the solution as soon as possible. So there are experts. We are consulting in the field that we have identified. It kind of got stuck. It needs something that comes out in order to accelerate whatever technology we have. And then we define a timeline. It also depends on the price purse. It depends on how much testing there is and so on.

So it's a little bit of a mix of what's possible, what's needed, and how urgent things are. How long is the Quantum X Prize competition running for? So the Quantum X Prize started March of 24. It's almost running now, you know, a year. And it's until 27, early 27. So it's pretty much a three-year price. You mentioned constraints. And there's also constraints on what you're actually looking for in this competition.

So novel algorithms, new applications, enhanced performance. So could you speak through? each one of those constraints and how they apply to the prize? So the XPRIZEQAICATIONS price, where I'm the technical price director for, and it's pretty much a three-year competition, sponsored by Google Quantum AI and Jester, Geneva Science and Diplomacy, anticipator out of Geneva.

And pretty much it was designed based on the fact that at the moment we don't have hardware that, you know, can do very powerful calculations. it's still kind of sandbox stage, but we are like making big leaps. But in 2030, we are thinking that we're going to have hardware that's starting to get exciting. So, but unfortunately, even though if you would have the hardware right now, we don't have many algorithms that can actually solve medium problems. We have a certain, like, two handfuls of algorithms, show algorithm, Grover's algorithm that have shown a quantum speed up, right,

quantum advantage, theoretical. But really, the application is limited, right? So really what we need, we need more algorithms that solve meaningful problems that all of us care about and actually improves our life, right, has social impact. And pretty much now with Express Quantum replication, we are crowdsourcing that algorithm. The reason why we have multiple ways of competitors to join the competition, as you said, we have developed a novel new algorithm for an application of social social. the goal, good. Take an algorithm we already have, but show that he actually can apply it to a meaningful problem for society, or take an algorithm we really know that has the application, but try to reduce the resources needed, right? So come up with a better algorithm, so the

calculation is more near term. And since we don't know what's out there, because we have like one or two algorithm every decade, so we haven't been very good. doing algorithm development in quantum computing. Is it okay for our listeners to think of quantum algorithms as software upon the hardware? You mentioned hardware. When you mentioned, should we be thinking of algorithms completely differently to software in a traditional sense? Or is that an okay analogy to use? Imagine like a stack, right?

On the bottom you have your hardware and the top you have your application, right? So you have to take the application and you have to encode it into the language of the quantum computer. like at the first level, right? Just using quantum mechanics, right? Using kind of like gate-based quantum computing or annealing or whatever you do. And that encoding from the problem to that whatever hardware you're using, kind of like first layer, that is the algorithm. And then you still have to, once you have the algorithm, right,

you still have to, for example, the algorithm tells you you have to run a certain number of gates, a certain number of cubits. then you still have all the software layer that has to compile the algorithm into the language, the quantum computer understands, and then you have the control layer where you have to tell the quantum computer, if you have trapped ions, for example, if you tell them how to move around on the chip, if you can control them, right, and then you have to read it all out. So it's kind of like different layers, and you have software, hardware, controls, you have compilation, everything placed in it.

But for me, the algorithm is really, you take the problem. and you do the first kind of algorithm, how you encode it, is the kind of the starting point of the stack. That's a great question, a great explanation on that. So going back to the fact that we haven't had a lot of algorithms come out over the last 10 years or so. What do you think the main reason for that is? So in my opinion, if you are looking, if you start studying quantum information science, you learn the algebra and then you go into like,

kind of like probability and you go into like trying to like encode your problem into waves. That's pretty much what you're doing. You work with a Hamiltonian, right? How important is it to know what Hamiltonian is? If you want to develop a quantum algorithm for express quantifications, it's very important that you know what a Hamiltonian is. It's energy. Like you're trying to like describe energy landscape.

You are adding different waves of different amplitude and different phases. And just imagine you have to encode. your problem into that kind of language. And then the right solution should have an interference of your waves that are constructively interfering, high amplitude, and the solutions you don't care about will destructly interfere and have a low amplitude. And that's just very complicated, right, to think about that. So having the math, understanding how you can, like, you know, massage that Hamiltonian

with the right terms to do what you want it to be doing is very difficult and you have to really understand it. And the algorithm is not just the classic, like a quantum algorithm from the start to do it, finish. It actually kind of has a lot of classical computer. And then there are some parts of the algorithm, some primitives in the algorithm you're going to take out, and then you're going to speed them up with a quantum way of solving something, and then you go back. So a quantum algorithm is not just pure quantum, actually, has a classical workflow, and then you have your quantumness in it, for example, and then you keep going. And that's difficult. Catherine, I'm going to create some t-shirts and I'll be sure to give you a part of the revenue.

The t-shirt's going to say massaging the Hamiltonian. Let me try and use an analogy here and shut me down if I'm going the wrong way on this, Catherine. But like you mentioned the idea of quantum algorithms needing to look at multiple wavelengths, multiple paths of a potential thing happening, right? when we're in traditional math, we just look at, okay, this, this thing's going to happen if I do this thing. Instead of that, we look at, it's more, are we in the, like, path integral area of, like, I have to look at all the possible paths and consider a calculation upon all of those possible paths instead of just between A and B. Is that close? Yeah, so I have to be honest with you. I'm not a quantum information scientist, you know, expert. So it is very subtle how you do that.

And you really have to understand how kind of like the mathematics works out with this interference. Then you have entanglement, which is on top of, you know, understanding interference. Bullse even you have obviously, you know, different energy levels, the way you encode your problem. So it's fairly complicated. So it depends on the problem you're applying and so on. Just you have to be an expert. Yeah. So for XPRIZ quad applications, you can do any, any quantum computer, any modality.

Pretty much very wide application space, as I explained to you before. Because we are really looking. We really need to, like, improve the number of algorithms we have. So we, out of these, all of these algorithms, we got some pretty cool tools, quantum tools, like a quantum free or transform, for example, a quantum phase estimation. They came out of these, like, algorithms. and we're trying to reuse them for different problems, but we really haven't really, like, improved our toolkit.

Ideally, you want hundreds of algorithms, right? And we're just not there. So we've talked about this in our quantum season kind of thread a little bit, but nature does quantum really, really well. Like nature's awesome at quantum mechanical processes, right? You know, we're talking about photosynthesis, right? for instance is it, you know, has that piece in it. Is anyone out there like, and this is so silly,

but is anyone out there like looking at photosynthesis and how to maybe repeat how it does it and apply what you learn to quantum computing? Is that even a thing? Quantum is now going more into biology. That's for sure. I actually taught a course when I was back at the university before, you know, I joined.

the industry and now a nonprofit. I taught a course on photosynthesis, like in my biophysic course. And we actually don't fully understand how photosynthesis works. There's some tundling part to it. It's fairly complicated regarding like the different, you know,

players during photosynthesis. People say that possibly quantum computing could, you know, help with that. But there's no algorithm at a moment. I am aware that described. types and the process. Well, it's kind of one of those things like quantum computing could help figure out photosynthesis, but photosynthesis could also figure out how to be a better quantum computer in a way, kind of, right?

Well, there's this idea of using nature, right, going down to the atomic level and use atoms to do the computation. But that's what trapped ions is, for example. So trapped ions, quantum computers, you're using an ion, right, for computation. And that's like, you know, that's nature you're using for computation because you're starting with something very pure, right? And it uses quantum mechanics, right, in order to have changed the properties over time. So it's a very natural, like, modality for quantum computing.

And I think people are exploring these different modalities. Photons, for example, is another one where we actually want to use nature to do the computation in the quantum computer. So, yes, the space is wide. And we need more people to ask these questions. What else can we use for compute? What are the most interesting things bubbling up from these prize groups? They're not necessarily the prize groups, but maybe some of the activity that's come out of this research.

What are some of the things that are bubbling up that are getting you really excited related to quantum computing and applying it to something? As people say applicable to making the world a better place? Yeah, so for X-Price-Quine applications right now, we just started the competition, so we haven't, you know, the teams have not submitted their solutions yet. They will have an interim report, which is kind of like the first status check-in.

But like phase one submissions will be in August of this year. And then by the end of the year, December, we're going to have up to 20 finalists that will share the $1 million price purse because the price is $5 million overall. So the first milestone may be $1 million. dollars, the 20 teams, up to 20 teams will share that. We'll know that in December. And then we hope that, you know, we're going to be able to answer the questions. At a moment,

what teams have shared with us is kind of like, you know, 20% of the teams want to like solve some sustainability problems, for example, of the UN SDGs, which are, you know, really like tough problems and there's a roadmap for that. So yeah, so it's a typical, at the moment there, what we know from our cohort, it's a typical application space, much, you know. science, you know, drug development, optimization, for example, that you can apply for different applications. One of them kind of is, you know, kind of like what you would expect. But again, we only have had a little bit of a small, you know, a view of it, and we will know more at the end of this year. Amazing. And you said 24 teams? So up to 20 teams will be finalists and move on

to phase two, which starts in January, 2018. But right now, we have 300 teams from 46 countries that are in the competition. And it's like anyone from a student or like an individual that starts quantum computing, so some individuals that are in it for decades, we have like, you know, startups, we have universities. We have like established startups. We have universities. We have people that just starting their journey to build a company, right? They want to use XPRIES to do that.

So we have physicists, engineers, chemists, biologists, people from healthcare, from finance, they all come together, right, and form very more disciplinary teams and inspiratory teams. So it's just very exciting. And then we have 46 countries. So we do have like kind of like all the regions in the world. The U.S. is mostly like represented in Canada and then Europe, but we also have like, you know, Africa is, for example, in it. right so we also have you know Australia in it New Zealand you know we have some like Asian representation

so we also have South America in the mix you know it's very exciting reading through the comments on YouTube there's quite a lot of people asking a question that essentially when 40 billion is being invested in quantum every year 5 million divided by five winners and it doesn't seem that much. Is that by design, is that because by that way you maybe get the smaller students involved,

you get people who wouldn't normally be involved? Is this a constraint in you'd like to offer more money for the prize? Could you speak through that to that? Yeah, of course. Of course I can. So it's a five million price purse. So then after the $1 million, up to three minutes will share $3 million, and then some runner-ups at the end will share $2.5 will share $1 million when we announced

the finalists in 2027. But the price purse is kind of like we had like an IBM Watson, which is similar timeline with a similar price purse. This price is purely theoretical. So I want to say that it's a thinking of paper price because it's purely about developing an algorithm on paper and mathematically prove that you have quantum advantage by, you know, then also comparing to the best classical algorithm. So that being said, there's no testing involved and there's, you know, other things that normally typically X price has where teams have to,

like, develop like some technology and you have to test sick facilities and such. It all costs money. But this is purely like an algorithm development price. And it's in the right size for other algorithm development prices. And that it's, again, it's a, good incentive. And you know, it's, it's money that we give you without any strings attached. So, you know, you obviously have to, you know, pay taxes on it, but then you can do whatever you want with that money, which is great. And then really for people that you're starting in a quantum ecosystem, like $1 million or $3 million without strings attached, can, it's a lot of money for young startups. It's actually like the right amount of money for software or like algorithm

development startups, not the right money for hardware, but it's not a hardware price. it's algorithm of price. I think that's a good clarification because if you're not deep into this and you read the website and you read about XPRIES, you might think, okay, I want a finished product that's solving some credible challenge to humanity

and I want it here on a plate and there's your money. So it's not actually asking for that. It's the theoretical algorithmic proof rather than something more. It's the recipe, not the steak dinner. There you go. All right. Time for the first.

fan favorite segment hot buttons five questions with Mark Fielding what's the best book on quantum our listeners should read by Anton Salinger dancing with the photons are we living in a simulation yes or no no Einstein or Boar Einstein AI Quantum's Master or Quantum's Assistant Assistant What's the most pressing challenge for humanity that Quantum will solve Clean Water great job Catherine that was awesome Have you read Dancing with the Wooley Master? No, but now I need to.

That was the first quantum book that I can't remember. We'll have to post this in the nose. That was the first quantum mechanical book that I read and I was like, whoa. And that like jumped me in. Well, surely you must be joking. Mr. Feynman was another one. But all right, guess what time it is. It's time for the news.

All right. We're in Thinking on Paper News. A couple things that still happen. We're still in the wake of CES. The CEO of NVIDIA is obviously very outwardly spoken, stating that quantum is, he's betting about 20 years away. What are your thoughts on his view of the timeline for what they call quantum utility? So it's really like hard to say, right? But if you really say like a quantum advantage problem with real word impact, you know, clean water, for example,

I do believe we're going to need decades for getting there to have that kind of computational power. But for, you know, more near-term applications where you can have an algorithm that is, you know, lose this resources. It could happen in, you know, two, three, none, like five fish years or less. It really depends on our hardware providers, how quickly they can do things. Yeah. It might be niche applications first, probably very niche applications that are fitting quantitative. on compute very well. I think so. And I think also like when people, people think about what a new industry is just the general public. And general public, if I'm off the mark, hit me in the comments and

let me know. But a lot of times when new industries come out, the general public tend to evaluate how that new industry is doing, but by what Wall Street is saying and by what investment is saying and that sort of thing. And this is not a, this is not a blink and we're here kind of thing. this is changing the way that computers think, changing the way that we ask questions for computers to solve, right? So it's a bigger play. And maybe, dare I say, we require a little more patience. What do you think? Yes, I think patients that are, we know exactly what the pathway is. We know what kind of hardware we need, and it's about the scaling because we need going to be thousands of almost perfect cubits, like very good cubits. And we're still right now trying to

build one, you know, logical qubit that is at that range of error. So once we have one of them, we have to have thousands or millions of these qubits to solve the problems. That's why it takes some time to build the hardware. But we, during that, during that road map or that path, there's going to be problems we can do on the way. They're going to be very interesting. Celebrate the wins. So this goes into our next question, our number two news item. This week, you know, we talked about error correction a little bit. I think a lot of our listeners have heard about this Willow announcement that Google put out there. Can you help us, even from a high level, kind of unpack what that announcement.

I knew it was largely around some error correction stuff. I mean, speed of calculation, but can you break that down what the importance of the Willow announcement was from Google? Yeah, for me personally, the announcement was really an error correction, as you mentioned. It showed that as you are having more physical cubits work together, as you increase the number of physical cubits, you decrease the error of the logical cubits. And it's predictable and it's scalable if you can just build bigger and bigger devices. And that's a milestone. That's amazing to show, like show an actual hardware that this scales. So now you can extrapolate

and now you know how many physical cubits you would need in order to make a logical cubit, right, of a certain error rate. So obviously they need to still improve the hardware, but they have now shown that it's scalable and that as to increase the number of physical cubits, the larger cubit area doesn't get worse. That's the cracks of it. So it's better. That was the main announcement in my opinion out of that bit. Mark, you and I have talked about this on previous shows.

And Catherine, I love your thoughts on this. The idea of like more cubits so you have redundancy or building a better cubit to not need the redundancy. I've been hearing kind of two schools of thought. Is there any truth to that? And what do you think, in your opinion, what's the best approach, at least from the information that you have today? I believe that we still need to have better qubits. So the fidelity of qubits has to improve.

So right now we make one error out of like a thousand. We should get to like one era of 10,000. And then when that it happens, then we can think about this sample. That's my opinion. But it's more like an engineering problem, right? So how many cubits can you put in a fridge? How big can you make a delusional fridge, right, under a size constraints to it?

And then maybe not science, may it just power constraints because you have to cool a really big delusional fridge. So we are trying now to use delusional fritians that connect them, or we're trying to bring the GPUs inside the fridge. So we're trying to figure out how we can, you know, like make this engineering over, like, problem overcome and that depends on the modality and depends on how the scaling works so so if mark wants to become a better you know better snowboarder you know he can probably go in the gym do some squats

maybe work on you know his balance that sort of thing how do you build a better cubit i have to ask the hardware specialists on that uh i don't know just to call out for our episode with D-Wave to build more on what Jeremy was asking about the fidelity of the of the Q-Bits, because I think that D-Wave use an alternative weather where they want quantity over quality. So check that episode. It'll be just here if you're watching on YouTube. How's St. Catherine? This has been a super fun discussion. I know we have, it's time for the carryover question. Mark, this is another one of our fun segments. We like to thread these shows together. Mark, can you remind Catherine what the carryover question was from our last guest?

I sure can. The carryover question is continuing the Willow conversation because our last guest, they were working in AI, but they were interested in quantum. And I don't have the exact words, but essentially Willow solved a computation that would take a supercomputer, a billion years or something, to solve. And one of the theories he heard about how it did that

was quantum tunneling, between worlds, the multiverse theory. Do you think that is how it achieved it? And if not, what could be another explanation for it? So I am based on just understanding the test. So it's called random circuit sampling test. It's a very specific benchmark that has been used by Google and others. And pretty much what you do is you are taking a certain number of cubits

and you're applying gates, a random set of gates. to them over and over and over again. So you're mixing up their state. Like you randomize stuff, right? Talk to me about a gate. Is that a thing that open and closes, literally? You can think about it classically. Like in classical, you have like your voltage going high and low, right? And the gates have a certain way of like letting that voltage pass or not.

And that's how you like make your algorithm. Quantum computing, it's similar. Like in quantum computing, a gate is like you have, you kind of like a cubit that is represented in kind of like a sphere. And more or less like it's a vector. and the gates kind of like move that vector around, rotate the vector, and that's how you encode it. So in essence, you have like a qubit, you buy these gates and you do it randomly, but then you can also entangle two cubits so that if you move this, that qubit might, that vector might move like that so there's like a certain instructions. And then you kind of like add these instructions randomly with a random way of like these selecting these two cubit gates and single cubit gates.

And then you read out the result. And it turns out mathematically what the way I understand is, it's like that you do with the computation. So like when you have a qubit, you have two to the end kind of like states you can explore. If you have 60 cubits, you have then like, you know, two to the 60, right? Now if you do a gate, you actually have a tensor product. Now you have two to the 60 times two to the 60. So now you have like 200 to the 130 possibility. you have to encode just for one step in your calculation.

And that's why, classically, if you think about the matrix with having 10 to 30 entries, and you try to encode that, that just takes a really long time. Adding gates to qubits creates more potential for those qubits to do something. Is that accurate or no? It's kind of like changing their state vector. Like it's kind of like, you know, giving different instructions instructions. So it's, I don't know exactly to explain that, but it's just kind of, you develop like a probability distribution, then you sample that. And every time you sample it, you get some more data right out of.

And then you get some random, kind of like random outputs of some of the states being high and some of them being low. And it's just really hard for the classical compute to do that. I want to say, like, some of these algorithms that we develop, when you look at these matrices, some of them are spars, have certain characteristics. And that's why we can actually still solve some algorithms. even though we think we have quantum advantage even fast with a classical compute because there are some structures in these matrices that classical can actually solve very well. But when you do this random slash on like 60 cubits with like 40 layers of gates and it's just getting too much for classical to keep up.

Got it. But it's teamwork still with classical at the moment. Yes, yes. But I'm just saying this specific test shows the powerfulness of a quantum computer. It has no real application, random sampling. it's purely a benchmark, but it's just very powerful the way it does it, the classic hook can't keep up. We read Quantum Supremacy, Mitriu Kaku in Book Club, and he spoke about tunneling.

And just to go back, I don't know, he might have answered out beyond my pay grade for that one. But so this idea of tunneling between quantum realms or worlds is a non-starter for you. That's not what's happening. For me personally, I am more. I like what I see on paper. I'm thinking on paper. And I think if you are in multiverse, if you understand the multiverse a little bit more and you believe in it and kind of like on that kind of idea and it's something that resonates with you, I think people have taken that

approach. And, you know, there are people that talk about this as you pointed out. But for me personally, I feel very good at having the math in front of me. That makes sense. Yeah, I think we're where our heads were going during the last discussion in the last episode was like, okay, we're going to spin up this wormhole and throw a calculation in here. We're going to spin up this wormhole and throw a calculation in there. And that's how it's just, yeah, it's just how it kind of spins up in your head. But math, think on paper, 100%. I love it. What about a carryover question for our next guest, Catherine?

Yeah, so thanks for an invitation to have me like post one. And for me, it's really about like, say that we have, have quantum computing or AI or like artificial intelligence, you know, and having that self-thinking AI coming on. Like so how do we make sure that, you know, everyone can use that technology, right? And it has a good, it's, it's there for social good, right? That we make sure it's applied for social goodness and not for the applications we don't want it to apply to. So, so how do we, as a society, how you ensure that. It's a great question.

Technology has the power for good and bad within it, and it falls back on humans, at least at the moment, to make sure we do the right thing. But I love the idea of having a mechanism to grade for that. Deep, good, nice question. Is there an example of a technology that hasn't been used for nefarious uses

as well as noble? Well, it's just thinking about quantum computing, right? I'm just saying, like everyone is worried about quantum computing. computing, like, cracking RSA. Right? That seems to take the headline over other things, right? Anyways, like, we can protect for these things. But I'm just saying, I think there's technology we have used different ways.

I don't want to point anything out now. I'm sure I can think of something, but it might take a little longer. So the human, human is still an important cog in the wheel no matter the technology. And thinking on paper is still a recommended approach. I love it. Catherine, this has been a fantastic discussion. I would love to stay in touch with you as you figure out what this brilliant group of applicants and eventually prize winners are going to do to this.

We love an update at some point. It was so much fun. Yeah, thank you. The best show I joined thus far in XPriest, that's for sure. And if everyone, please check out XPrize.org. Thanks again for joining. Have a great rest of your day. All right.

Disruptors and Curious Minds. We are now backstage after a fantastic conversation. I leave conversations like this, Mark, absolutely inspired and renewed in kind of humanity's ability to do the right thing if situations are designed properly. I think it's great what they're doing to drive innovation, to get interdisciplinary teams working together. Like we talk about it, you know, the Nexus, Julio, Tino, there you go, Julio. And yet another shout out to you in your book, but the importance of interdisciplinary teams coming together and leaning on each other to solve the biggest challenges in the world. I'm, juiced, man. I'm psyched at what I heard. What about you? What did you learn? What did you take away?

Yeah, ditto. I always love a quantum computing conversation because I always leave it questioning how my intelligence and how little I know about it. Building on the previous episodes, every time we have one, I learn a little bit more. I love the power of competition. I think that there should be more competition. I think that it starts at home, and that's why I encourage my kids to compete, because it obviously drives innovation.

Quantum's particular, because all the other technologies we have on the show, we speak about AI a lot, robotics, blockchain. This is all happening. This is beyond theory. This is changing companies, changing lives, changing how we work and play. And Quantum kind of, we're just waiting for it.

It's all about potential. And I think with the XPRIZ, there's this concrete move to move it out of the lab and into the real world, however that might be. So I'm excited about that. I don't know about how you think about Quantum, but on the one hand, you've got, oh, it's going to be 20 years, 30 years before we have anything useful. On the other hand, you've got people like Kathleen in the next prize saying, we don't need to weigh that long. We can do things now. It's encouraging.

The term useful is bunk. It reminds me of like, I'm picturing, I'm picturing you and I sitting at a conference table. We're pitching to a VC and, you know, it's this brilliant idea and the VC's tapping his fingers going, well, when is this going to be built, Mark? When is it going to be useful, right? Everything that the quantum computing world, in my opinion, is doing is on a path to usefulness or, utility, right? Like every little step is, is getting us, you know, to a, to a point where it's going to change a lot of things that we don't even know it's going to change yet. But I'm going to reel it back in just to just a touch, but like pointing, pointing back to the quantum processes we don't understand. Catherine mentioned photosynthesis. We broke down photosynthesis from Micho Kaku's book. Like the power, nature does that stuff really well, right? And we try to repeat

natural systems and they end up being pretty clunky at first. But eventually, you know, we get a little bit better at him and we learn a little bit more and that sort of thing. So like the party's just getting started in my opinion. Well, yeah, like you said, nature is awesome at quantum mechanics. Nature is quantum mechanics and maybe all the information we need is just out there. We just need to find a way. Whenever we have conversations about humanity's greatest challenges. I always think about the doomsday clock and the ticking, you know? And can we wait 20 years? Can we wait 50 years? Should we as a species be waiting 50 years? Because if you spent all your life reading headlines in the mainstream media or in any kind of press, you'd think, no, we don't

have very much time, actually. We need to get this cooking now. We need to get it out of the lab now. We need to be doing this now because humanity's greatest challenges today might be a whole lot worse in 5, 10, 15, 20, 25 years. Well, this is worm. This is wormhole adventures by Jeremy and Mark. But like the, here's something, here's a theory. We talked about this a little bit today is the, um, the idea that the human equation, the human piece of the equation, no matter what technology we're using, there are things that we do human to human, you know, information networks. We're reading, you know, Nexus right now and talking about information systems and disinformation and all of that, I would argue we could buy ourselves more time by figuring

out that piece, which is easier to fix than figuring out how to use qubits to solve a problem that a VC is going to get excited about in fund. Oh, I like that. Yeah, using information networks to buy humanity some time. You can have to expand on that. How, what might that look like? Education. I think it boils down to a couple things.

I think it boils down to critical thinking. I think it boils down to empathy. Empathy is the biggest missing piece between one person and the other, right? If I'm not truly empathetic in your position, I'm not listening to you. All I'm doing is reloading what I think is correct and firing it at you, right? And as long as there's that disconnect. And I'm not saying nobody, the world isn't empathetic. They're empathetic people, right?

But I think if we can improve our empathy as a superpower moving forward, that position could buy us time to make better decisions on behalf of society to allow some of these great things to happen. I can't remember the guest, but I remember we had a conversation about this and how AI augments or dilutes our empathy. And I think the consensus that we drew was it doesn't, it decreases our empathy. I don't know if you remember that show. I've got a video somewhere of you saying that,

or our guests saying that it decreases empathy. So if we need more empathy by, and you had a big, good, nice rant about last week, by giving over our critical thinking, by giving over our choices, that actually decrease our empathy. We could have a whole study on that. Maybe we'll submit an application to an X prize

and they'll give us some money and we can make it happen. But thinking on paper.x, Y, Z, we want to hear from you. We started this show for us. we are now delivering it and producing it for you. We want this to be a valuable tool for help you to figure out these technologies and also what it means to our humanity and what the world is going to look like moving forward. Thinking onpaper.xyZ, let us know how you feel. Share it with a friend if you hear something cool.

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