The World Economic Forum is an independent international organization committed to improving the state of the world by engaging business, political, academic and other leaders of society to shape global, regional and industry agendas. Incorporated as a not-for-profit foundation in 1971, and headquartered in Geneva, Switzerland, the Forum is tied to no political, partisan or national interests.
At Davos’ Open Forum on “Understanding Quantum Reality,” leaders from academia, national labs, industry, foundations, and government framed quantum as a general-purpose capability with near-term uses and hard constraints. Nobel laureate John Martinis explained the core idea: unlike classical bits, qubits can exist in “zero and one at the same time,” enabling new algorithms and, with many qubits, exponential state spaces. Lawrence Livermore’s Kimberly Budil emphasized the engineering reality—errors and fragility are “a bug in computing” but “a feature for sensing”—and warned that sufficiently powerful quantum machines could break today’s encryption, accelerating the push toward quantum-safe and quantum encryption methods where eavesdropping is detectable.
SandboxAQ’s Andrew McLaughlin argued value won’t wait for fault-tolerant quantum computers: physics-based, high-dimensional models running on GPUs can already drive drug discovery, materials optimization, and quantum sensing, while “predicting the next word isn’t necessarily getting all the science right.” Novo Nordisk Foundation’s Lene Oddershede highlighted Europe’s need for mission-driven collaboration so the “second quantum revolution” creates local economic anchoring, and pointed to quantum sensing already used in hospitals. Singapore’s Minister Josephine Teo made the policy case: quantum advantage should translate into public benefit, from port logistics to finance, while governance must address equity and dual-use risks. The consensus: quantum will complement, not replace, classical and AI systems—and success depends on collaboration, standards, and responsible deployment.
Good afternoon. Hello. It's such a happiness to be here with you to discuss the topic of quantum information science and technology. My name is Paul Alivisatos. I'm the president of the University of Chicago. And I'll moderate this panel. And we're very excited, all of us, to be here with you. We have an incredible group of people, and we're going to start to explore this, this marvelous topic. And so we're going to the first question I'll ask people, all the different people here, here on this panel, we have represented, universities, national labs, cutting edge companies, foundations and governments. So we've got all the pieces that come together to collaborate, to ultimately try to create, technology that can benefit, humanity in different ways. And so to start this off, of course, quantum is such a foundational deep technology. And we're very fortunate this year to have with us one of this year's Nobel Prize in physics winners, John Martinez. Congratulations, John. Pretty exciting. And so, John, we'd love it if you would take a moment and just explain, what is this area that we're talking about? So, what I'm trying to do in a big part of it, not everything that we do in quantum technologies is to build a quantum computer. And I'll explain a little bit about that. And what we can do is we can actually take advantage of quantum mechanics to do computation, that for certain problems can be way, way more powerful than what we can do with classical computers. So to understand that, let's just go back to basic computer ideas in a regular computer. You store your information as a bit that's either zero or it's one. And by making stringing together zeros and ones to make words that represent computer words that can represent data, you can manipulate that in a very powerful way. And what quantum mechanics does is allows you not just to have 0 or 1, but it can have zero and one at the same time in some quantum state. Kind of like the idea that an electron in a hydrogen atom is at multiple positions around the nucleus at the same time, you could have multiple zero and one states at the same time. And what this does is allows you to invent new algorithms taking advantage of the zero and one state. So one way to think about it is when you think about how to manipulate 0 or 1, you have basic operations. You can change the 0 to 1. You can interact two bits together. And in a quantum computer you have those operations, but you also have operations that deal with the zero and one state. So you can think of it as having an enhanced instruction set and using that enhanced instruction set. Because again, come about by nature, you can then develop algorithms that could solve certain problems much, much faster than if you have a classical computer. And one simple way to think about it is with the zero and one state, you can send that piece of data through your quantum computer and do a parallel computation between those two states. Now a factor of two. Okay, that's nice, but if you have many qubits together, then the parallel computation goes as two to the number of qubits, which is in fact, you know, a huge number. So by the time you get to something, you know, a few hundred qubits, that number to the few hundred is greater than the number of atoms in the universe. And clearly it could be very powerful. Yeah. Well. Thank you.
And, you know, to add to that, if I may, you know, you're used to the computers that work with zeros and ones and what kind of problems those do because they operate in your phones. And, and we have the neural networks that do the AI that look for relationships between words and they correlate things. And then these computers really, are designed around solving problems where there are many very similar solutions, you know, nearby, and they find the optimum for them. So it's a different kind of mathematics, and it's just profoundly powerful. Right? Yeah. If I could come to you because you represent national laboratories. And when we think about the creation of new technologies, very often we see, you know, ideas, might originate at universities like, like yours at Berkeley and Santa Barbara. And then, you know, very often they'll find a stage where they're working at national labs where they and you lead one of the, you know, most famous and greatest national labs in the world, Lawrence Livermore National Lab. So, Kim, what's up at Livermore? Just share a little bit with us about, your perspective on quantum.
Sure. So the National, I'm on one of the national labs in the United States. We're one of 17 run by the Department of Energy, and we're large multidisciplinary organizations. So what that means is we can do science at a scale and a level of complexity, that it would be hard for a smaller organization or an academic department to pursue. So quantum is a great example where you want to bring many disciplines together to learn how to exploit this new technology. So we have numerous activities in this area. In one case, we're trying to understand materials and the properties of the components that you would build into quantum systems. How do you build better qubits in another activity? We're trying to understand how to use qubits in quantum computing. So there we have two components to that. One is understanding how these systems work and how to make them operate better. So they're prone to things like errors. And can be difficult to operate. So we're working on making these systems better and understanding how to do error correction and things like that. But we're also trying to teach our researchers how to use them. So you program a quantum computer differently than you would program a classical computer. So our scientists, physicists, biologists, chemists need to learn how to speak in this new language and program those systems. And then the third area we're interested in is quantum sensing, where you can use quantum systems to be very sensitive detectors. So that could be for big science experiments. In our case, we're part of the axion Dark Matter search. Or for applications like trying to understand underground structures or doing detection under the ocean. So each of these is really important and requires that multidisciplinary character.
I guess, on the sensing front. If you have one of these systems that's between a zero and one, and somebody opens a door down the hall, it can it can lose whether it's a 0 or 1. So they're very sensitive, right? They're super sensitive. But you have to protect them somehow.
That's right. It's a bug in computing. It's a feature for sensing.
Yeah. Andrew, you work at a really high energy company in California called sandbox. I guess it's a spin out from Google, and, you're practicing finding out what quantum really will do. And in different ways. And looking, I guess, probably closer to applications. And so maybe you could tell us a little bit about what you do in this area.
Yeah. So, so I think what, what, what I can contribute to the conversation is that, what was described earlier as is some mathematical techniques that come out of physics that allow you to build models that are different from language models. So we call them quantitative models that can, model the world in systems of high complexity. So we do atomic level simulation to do things like drug discovery, to discover new materials or optimize materials in areas like magnets and, battery chemistries, catalysts. And what's interesting is that, we don't have to wait for quantum computers to be able to make a lot of progress in this area. As Professor Martinez said, like, there are some things that you just can't do on classical compute. But the good news is, if we take the mathematics that's been developed over decades to solve physics problems, we put it to work on existing GPU platforms. We can actually make, maybe 97, 98% of the calculations that we would want to make to be able to try to understand. For example, if you have a misfolded protein like the kind that causes Alzheimer's disease, we believe, you can understand, how could I design a molecule or what molecule could I design that would bind with that protein and stop it from doing the harmful thing that that it's going to do in the brain? So, that's that's what we're working on right now. And I think, you know, maybe, maybe if there's one kind of exciting thing to say for those of you that are students, it's that the most valuable people in our company right now, are, computational physicists, computational chemists, computational biologists, and what those, are kind of integrated skill sets represent is an ability to go deep into the science and then understand how to use compute, how to use computing power and AI tools to be able to do the kind of modeling and to be able to solve the kinds of equations that let you take full advantage of these amazing computing platforms, in, in order to solve science problems. And maybe I'll just say, if I can just end on a note of negative campaigning, and make an incendiary remark, all of the language model tools that you're using are basically poorly designed for science. They can be a really interesting interface layer to talk to scientific tools. But if you want to use a language model to understand what's happening at the atomic level, it's going to give you a garbage answer, because that's not what it was designed to do. I use them every day. Nothing wrong with using them for what they're good for. But the existing universe of language models that's out there is not, the set of tools we need for science.
So many.
Predicting the next word isn't necessarily getting all the science right. Exactly. It's really a different kind of model.
Yeah. What what we want to do is we want to we want to we want to achieve trustworthy predictions in complex systems. And, words is a complex system, but it's not what's happening at the atomic scale. And, you know, I mean, if I could just. Well.
If I.
Can just if I can. So I know that there are a lot of students.
I'm glad we got the back and forth going here. That's good.
So, so, so there are a lot of there are a lot of students in the audience. And so this is directed primarily primarily to you. So okay, what's cool about a language model and the architecture behind it which you may have heard of. It's it's called a neural network is that we use computers to kind of simulate the neurons in the brain. And the way that we do that is by turning each neuron, or I shouldn't say turning each neuron into, but using a three dimensional vector as a neuron. So x, y, z coordinates align with a length and those coordinates. And so if you're doing science problems, actually it turns out that at at molecular level, they're dramatically more complicated than language problems. And so we need to use math which can handle a much greater number of variables, many more parameters. And so we use higher dimensional math. So we use mathematics that can be 200 dimensional or 2000 dimensional at the level of the neuron. So anyway very cool. We can do this on GPUs and it can solve tons of problems from drug discovery materials like I said. But even problems like finance or problems like understanding the weather you're going to have available to you if you lean into it. Amazing tools to be able to tackle very complex problems in a way that's manageable.
Yeah, it's going to open a lot of doors. So, Lina, you, were for quite a long time at the Niels Bohr Institute and of course, Niels Bohr, like he was, you know, helped us understand atoms, you know, where the electrons are moving around and famously had that big fight with Einstein, which ultimately, you know, he won. And and so you've had that part of your life as a, as a physicist and doing quantum things. And now you're at a foundation, and the foundation is, playing a role in this ecosystem of trying to help this area grow. So tell us a little bit about that.
Sure. So I now work as chief scientific officer in the Novo Nordisk Foundation, which is a charitable, non-profit foundation.
I have a little money I here because of, I guess, the development of some of the weight loss drugs, the GLP ones. So. so pretty popular product. Yeah.
Saving lives Enterprise Foundation, which means that we own companies, for instance, Novo Nordisk that produces insulin but also produces semaglutide derived products like Ozempic and Wegovy. Yes. And actually the foundation owns 180 companies, of which most are based in the US. So I also have a background as a professor of physics from the Niels Bohr Institute, where I did plasmonics like you did, actually. And so I have, I have like a scientific background if I would turn back to Niels Bohr. So Niels Bohr and his contemporaries back in 1930s actually laid the fundamental understanding of the principles governing quantum mechanics. So since then, the last 100 years, of course, engineering has been catching up. Compute has come about, nanoengineering, etc., which is all needed to realize quantum computing and quantum technologies as we are witnessing today. Now I'm going to take a Danish and a European perspective. If we now consider all these people who were in Denmark, were in Germany, were around Europe back in 1930s, they gave rise to what was called the first quantum revolution that gave rise to approximately one third of the value of all products today. You could ask the question how much of that value then returned to Europe? The answer is close to zero, actually. So, now in the second quantum revolution, we as a foundation, we try to think, how can we anchor at least part of those technologies in Europe, which are developed in Europe? And so one answer basically is we need to be more strategic. And this is something that the European Union and Europe, the European countries, also Switzerland, I believe, and the UK, we are frankly not very good at. We're losing out on many of the excellent companies that are based in research. They come from the university environment, they're excellent engineers, entrepreneurs. They go to different regions, basically because they cannot find, the venture capital that will support them if they stay in Europe. So one of the things we really try to do is to enable strategic thinking to enable mission driven research. So one example is within quantum compute where we have a program that is aimed at making fault tolerant quantum compute in an academic environment. And for that we need quantum materials. We need quantum chips that can then supply the region with high quality chips. For that. Actually, we need to be extraordinarily mission driven and that requires collaboration. And I'm looking at you guys here because this is actually not the lonely wolf approach. And so this is something which is quite different way of thinking than was the normal way in research. And I'm a researcher myself. I've been at the Niels Bohr Institute for 20 years. Look, most people, they are lonely wolves. They want their nature papers out. But if you want to say realize fault tolerant quantum compute, you need to collaborate. And I think that is true for all these critical technologies. If you really want to make a splash, if you want to do something that will have an impact to really benefit the world, you need to collaborate and you need to agree on where you're going.
Thank you for that. You know, actually just, you know, as a scientist myself in so many ways, I just want to build on that for a moment, because I know there's a lot of young people here, and there's so much hope about what you might do in your lives. And I do think there's this image out there of, and, you know, maybe it is, Niels Bohr kind of locked away somewhere in this old room, and he's all by himself, and his hair is kind of going like this, and, and, you know, it's kind of like, you know, that it's, that maybe that's a very lonely, lonely thing or something like that. I do want to say, I think, the vast majority of science that people do is a very social activity. It involves many people working together, bringing their talents, and being, in a supportive environment, that, that, that achieves goals together. And, you know, I just I think it's important for you to hear that it's not a lonely path. Actually, it's a very, it's actually a very warm and fun path. But, you know, you have a goal we kind of want to get to. And that is, you know, that that is getting towards that goal just makes people very happy generally. So, if I could now and you kind of queued us up with what you said about, ideas having come from Europe, but value maybe not so much. Obviously, governments care enormously about the conditions that lead to their countries thriving, and part of thriving is to be a place where the new developments in technology are active in in that society and bring social benefit. And, you know, you're, at a in an amazing country. If I may, if I may say so myself. Josephine, you and Singapore, it's a small country, but it's got many diverse populations there, and it has had over many years a kind of like a laser like focus to bring advanced knowledge together. So what's it like? You're the minister for Digital Development and Information in Singapore. Tell us a little bit about what that's like.
Well thank you first of all for having me on this panel. I'm just wanting to make sure that you can hear me right at the back as it okay, I think is it better now you can hear me.
Okay.
Great.
Okay.
So, I'm really a very oddball on this panel because we have very distinguished, scientists, researchers who know a lot about quantum technology. So I'm going to give you a lens that is, different but complementary. I'm looking at it from the point of view of a policymaker. And as digital minister, I have to recommend to my prime minister, to his cabinet and persuade the citizens about the necessity and the usefulness of our investments in quantum technology. And I'm very happy that at the forum we're seeing a room full of young people, which is not something that we see a lot of at the forum.
Not enough.
And you are a wonderful reminder of why we do the work that we do. And ultimately, investing in technology is about creating opportunities for humankind. And who will stand to benefit most from this investments? It would have to be young people. Shall I turn it back on.
Your scarf?
This is a green light.
Your scarf.
Scarf? Maybe you should take off your scarf.
The scarf is causing it to be muffled.
It's a beautiful.
It dropped off.
It's a beautiful scarf. But let's take it off.
Let me just hold this.
That's good.
That's good.
What's better?
Great.
Very good.
So, from a public policy perspective, ultimately we want to see our citizens benefit from this technology. So you have heard of the Scouts model, which is to be prepared. And the question is that when it comes to quantum technology, what do we want to be prepared for as governments acting on behalf of our citizens, we want to be prepared so that the upsides can be enjoyed by our citizens. But equally, we also need to be prepared to understand the risks and to moderate against its most excessive or ill effects. So let me just ask the question for Singapore, what do we see potentially as the upside? It goes to the heart of what this technology can do, which is complex computations. So what is it in Singapore that will benefit from complex computations? Well, we are very small island. We are only 720km². It's tiny. We have 6 million people squeezed onto this little island. But we do contribute a little bit to the well-being of other people in the world. By having a port that operates very efficiently, this port enables many containers to be moved, you know, in a way that would otherwise make it more costly for us to get the goods that we would expect to have to make our daily lives more convenient. Now, the interesting thing about making the port effective and efficient is that optimization is actually its most critical function. There are many vessels calling at the port, and there are many containers that have to be moved in a gazillion number of permutations onto the vessel, off the vessel, and each time you don't do it efficiently, these containers have to be stacked ten high. And in order to get to box number two, in order to go to vessel number 364, if you have to do many, many different movements in order to achieve that, that makes it more costly. That means that the bottle of water that we drink, the scarf that we get to buy, will most likely end up having to cost more. So the way we're thinking about it is if we can use quantum computing to improve the efficiency of how a port operates, that serves as a very useful way for us to make a small contribution, and also then to be more competitive and as a result, sustained the relevance of our maritime center. And hopefully, hopefully in doing so, young people in Singapore continue to have a good livelihood. So there is one way of thinking about how this technology can be very useful. But of course we also have a financial center and there are also good jobs there. But as you can well imagine, in finance, there are also complex computations. How do you price, a derivative product that has got many different kinds of elements built into its risk as well as its returns profile. So these are just some of the reasons why we believe that understanding quantum technology being invested in building up an ecosystem. I really appreciate what, Lina was talking about and what you were talking about, that, you know, science is a very social activity. So even in terms of how we can make this technology come alive, in, in Singapore and anywhere else in the world, we believe that it's necessary to build an ecosystem. You can't just have 1 or 2 people. And we've been very blessed to have some outstanding researchers choose to work out of Singapore. But we believe that this ecosystem is essential to build up, and some of it will include startups that are trying to solve very specific problems in making quantum computing relevant and real in in any context. But I think also it's important to have that sense of community, a sense of community that sees this technology as being applied for the public good, and also a sense of responsibility to making sure that its risks can be properly managed.
Beautiful.
I have something to add. I just found out yesterday that my company will be collaborating with Singapore.
We are so excited about it.
Yeah. And, and and what's interesting is you're very good at semiconductor manufacturing and we hope to take advantage of that as help build components for the quantum computer.
That's right. And so we don't have everything you know. But we have some things. Yes. And one of the some things is semiconductor manufacturing. The other thing that we hope we have is that we have problems. We have problems that quantum computing can that can help to solve. And we want to demonstrate that the solving of these problems can be a plus for mankind.
You also have two world class universities, at least that I know of, and US and NTU, which have been very welcoming to scientists from around the world, which is phenomenal. I loved your boxcar statement. If I could, I just want to try to make a connection if I can. So Bor was trying to figure out it's called quantum mechanics because it was trying to describe he was trying to have an equation that would describe how electrons move in atoms. You know, if it's if it's moving this fast here, now where's it going to be later? And trying to describe that motion. And it turns out all the old equations of mechanics just manifestly did not work. And so the that group of 37, human beings that, you know, that that ultimately opened the door to everything we're talking about, they developed a set of equations that describe how electrons and it could be lots of them. And they don't like each other. They push against each other. But sometimes they kind of do like each other. Very complicated motion of these things. They create a set of equations that works to describe those things. I mean, it just really it works. And now you're saying, hey, that same set of equations could actually help us thinking about how we move these box cars around as we're trying to do transportation. And that's the bottom line. That's why quantum computing turns out to be so powerful is because anytime you got lots of objects or lots of things that are connected with each other, and there's some optimization that involves, really discriminating, very close but not identical solutions, it just shines. And so it's going to be very important because those problems are there everywhere. Now, I want to come back to you, John. And this is very important, working at, you worked at Berkeley, you worked at Santa Barbara and, and other places. I'm going to ask about that. But there have been at least a couple of moments in that career when boom, things really changed. And you were right there. And so, you know, maybe you could tell us, you know, what happened, in those moments, to the extent that you're willing to share. And I know that one of the key moments was also when you, university professor went it took a lot of oomph to go through that. And you said to Google, let's do something together. So can you just talk about those moments? Maybe.
Yeah. So, you know, clearly I my the Nobel Prize work was actually done for my thesis, which is a pretty amazing event in your life. So for my PhD.
So when, when when John Clark was John Clark, he signed it. Yeah. So how old were you?
I just well, when he, when he signed it I actually it's turning into thesis. That's the big moment I felt this big release because I didn't have to take a class anymore in my life. I did have to learn for my entire life. But it's not being graded.
Yeah, you don't stop learning at that point, but. But how old were you?
But. Yeah, well, it was just a very beautiful experience because there were some experiments already, but it was kind of murky what was going on. And with Michelle and John, we were able to think very carefully. And it's kind of like combining microwave electronics with quantum mechanics and fabrication, low temperature physics, a lot of things we had a.
Lot of stuff had to go right.
And I would say what happened is fairly soon we were trying something at high temperatures and it was a total disaster. It didn't work at all, didn't make any sense. We then designed it more carefully and then the data started making sense. And that was kind of the big moment. We still had to take the quantum data and do all that, but it was kind of a natural progression once we understood what was going on. And then I would say, you know, later, which.
Really turned out to be one way you could make these quantum states, right? And they then are a kind of qubit that's between a zero and one. And you made like the first one that was electrically addressable.
That's right. So normally you think about quantum mechanics as the physics of small atoms and molecules and the like. And what we showed is that quantum mechanics is much more general than that. You can have electrical circuit that's about that big. And then you, you do the mathematics and physics properly, and then you can see quantum mechanics with that. And of course what's interesting about that is we have our computers that are made out of electrical circuits. And if you can make qubits out of electrical circuits, then you can use all that technology to build up and, and do and build a quantum computer. So kind of along the way, there's lots of experiments we did and the whole field did that. I would say one experiment we did at UC Santa Barbara, it was just a qubit connected to a microwave resonance. But we were able to generate these really complex states, in the, in the resonance. And, and it was just strikingly beautiful data and it was basically taking some math and just programming in these pulses, trusting the math, and then out pop these really beautiful states. And what it occurred to me with that experiment that we could really control these quantum systems extremely well as well. You know, of course, there's always imperfections, but more or less mathematically, we could trust the mathematics.
And that was a key transition in the following sense that, you know, the previous quantum physicists wanted to describe an atom or a molecule, but now we're controlling them to use them for our own information manipulation. And so that was like an moment for everybody. Wow. These folks now can control the quantum state. And by doing that, they can now make, simulations of the world essentially. Right.
So then the the final thing I would mention is, when we moved my group from UC Santa Barbara to Google, and that was basically they had the funding to build up a proper hardware project. And then we did the experiment, published in late 2019, called the Quantum Supremacy Experiment, where it was 53 qubits. And we did an algorithm so that if you wanted to check, if you had the right answer, that would require a supercomputer to check it. Or in the, you know, biggest case, we couldn't really solve it at the point. And that showed that we could, you know, do a very powerful quantum computation. Now, that was just showing that quantum mechanics worked at this big scale, which, of course, is necessary to do to know that your technology is going to work. And I think a lot of people at the time, that was enough qubits and complex enough. They weren't quite sure that whether that would work, but it practically worked. And then, of course, now what people are doing is trying to do something useful. So that's better qubits, better control, more qubits finding the right algorithm. And there's just a lot of people, you know, out there in the world right now really pushing to do something useful, which is kind of the next big, big milestone.
And we'll come around to that in a bit. I want to ask you, because Livermore is, you know, it's just down the road from Berkeley, and it's, very special place. But, it's also a place that thinks about things like, you know, most of us probably want to have the, you know, the data when we have our, information that we're sending around. We'd like only the person we're sending it to to know what we're saying, and not just somebody else who happens to want to eavesdrop in and know what we happen to be saying, whatever that may be. We're not looking at you. Saying anything isn't, you know, you shouldn't be sure, but but, you know, we all want to have our privacy at some level. And quantum has enormous implications for the privacy of information. And so, you know, since Livermore deals with issues related to that in ways that governments care about, I wonder if you could just give a little bit of a sense of what that why that is.
Sure. So today we use algorithms to encrypt data that we care about to make sure that it stays private. So that could be you transmitting information to your bank. Or governments transmitting information between each other. And there are a whole host of very secure algorithms that are, in use today that work very well for that purpose.
And they're really hard for somebody. Somebody might capture the message, but they can't figure out what it says.
They can't figure out what it says. And no classical computer could break that encryption key. It's just too complex. There's no simple way to go about understanding how that data has been. Masked essentially from people who want to read it. But if you think about the power of these quantum systems, where they can begin to do these incredibly complex calculations at scales that are unimaginable with classical computing, you have the possibility that you can now break those encryption methods. So this would be able to unmask data, which would be a huge vulnerability for most of our secure systems. I mean, I don't know about the folks in the audience, but I do everything on my phone or on my computer at home. And so all of those systems would be vulnerable. And the good news front, there are approaches using quantum systems that will allow you to develop quantum encryption methods. And one of the features of these quantum systems is if you measure it, you, sort of destroy the quantum state, so you'll know if someone is trying.
If someone tried to eavesdrop, it actually is immediately detected. And that's like, that seems like a pretty good deal, because now we don't know if somebody tries to, you know, decrypt your message. Right? We don't know if they've done that or not. But with quantum we would know.
Right. And you could imagine that for people who are interested in understanding what others are thinking or doing or transmitting, they could be gathering data up and waiting for this moment when these encryption methods are able to be broken, to look at all that old data. So there are a whole host of implications to privacy that come with, the breaking of encryption algorithms. But a lot of work is going on now to develop this quantum encryption approach, which will allow you to know when someone's trying to snoop on your data, just as you said.
Okay, I'm going to come right back to you. And I want to go here because this thing, what we just talked about that you would know depends on something called entanglement. Right. And I want to go to because there's a famous story, of Bohr's and Bohr and Einstein having a conflict. Einstein didn't much believe that, the equations of, for atoms moving around was right. And so he just struggled and struggled, and he tried to create a test case that would obviously be so silly that it wouldn't, that it would show that those equations could not be right. And ultimately, you know, could you just say a little bit I mean, you know, you know, that stuff, you know, quite well. And so maybe you could just say a little bit about, about that.
Well, I'll just basically say that of course, Einstein was proven wrong. In that case.
Einstein was proven wrong.
And he.
Did it. But his case, his weird case that he created, turns out to be foundational to quantum computing. Right?
So and he called it spooky action at a distance. And he wasn't believing that God was playing dice with the universe, of course. But maybe just one one thing, I mean, the concept of entanglement also is behind. Now, we talked a lot about quantum compute. I just want to to mention also quantum sensing, which also relies on on entanglement. And that's actually a much more tangible technology. You can say in that it already these extremely sensitive sensors, they exist and they are in use. For instance, they are being used at at hospitals. So one of the things that we do as a foundation is that we enable the translation of quantum technologies into the biomedical space, and quantum quantum sensing is exactly such a technology where you could use actually, it's being used to pick up extremely weak electromagnetic signals from the brain or from the heart. So you can, in a non-invasive manner, follow the action or the potential disability of the heart in a non-invasive manner, which is extremely useful. And such technology is actually already being used in using prototypes in Danish hospitals. I mean, I've seen them with my own eyes because part of our discussion.
Is real health. It's going quantum will have a big impact on, sensing and diagnosis for human health.
Yes. And so here we are trying to also give you maybe an impression of what is realistic and what is still some years out in the future. And so quantum sensing is here. So it can be used for diagnosis also. Actually it can be used if you had a have a blood sample from that blood sample. It can detect malnutrition metabolic diseases. So that is extremely useful because it's very sensitive and non-invasive. And also it can actually be used in a low middle income country setting. So it has a lot of potential.
Yeah.
Can I describe a thing that we're building that's like using one of these.
So a sensor oops a quantum sensor.
It's earth shattering.
Oh how excited I am.
You're going to do a quantum sensor. Is that what you're. No. Yeah yeah. Go ahead.
Just to describe a use case. That's that's cool. So, so we're actually working on a cardiac diagnostic device as well. But maybe the one that I'll describe. And by the way, in, in collaboration with Professor Bettina Heidegger at Charité Hospital in Berlin, a key European institution for quantum sensing, but, so, the device, another device that we're working on is a navigation device for airplanes. And so, you know, to, to kind of, like, oversimplify, if you'll forgive me. And you can, you know, grade my paper later. The, the way that this quantum sensor works is that you take a nitrogen atom and you create a vacancy next to it, and then you put that into some very stable environment.
The nitrogen atom is in some kind of crystal periodic arrangement of atoms. And then the vacancy is there's a missing atom nearby missing atom. So you've got an atom that doesn't belong and an atom that's missing. And they're right next to each other. Exactly. Right. Okay.
And so for reasons that I'll let you all explain, if you choose to, you can shoot a light laser into that nitrogen atom. The electrons get all excited and as they go down, the energy states.
Crustal magnetic field. So the magnetic field that is the residuum of the kind of array of rocks and minerals and things from early in the Earth's history. It's so specific. It's almost like a fingerprint that if you can detect that magnetic field as you're flying at 30,000ft over the planet, and you compare it to a base map that shows what the Earth's magnetic field is known to be, you can geolocate your airplane.
Without any satellite.
Without any satellite, a completely passive. You don't have any signal going out of the aircraft, any signal going in. You're just reading the magnetic field. So anyway, we're flying on planes right now. We've been flying on, with Airbus. And we've published a few papers about that. We've been flying with, multiple countries, air forces to try to get flight time and hours and prove this thing out. And the one thing that I want to say, just to, like, put a little period on the sentence, is what makes this possible, is that we were able to use, quantum algorithms. So the kind of math that we were describing earlier to denoise this extremely sensitive sensor. So if you think about a cardiac device operating in an emergency room, we've built one that operates at room temperature, plugs into the wall, doesn't need any shielding, but that means that the AI has to do the job of distinguishing between what is a heart, what's an elevator, what's a phone, what's an electrical outlet, and to do so dynamically as people are moving around and machines are moving around. Same thing with an airplane. You've got to know what's the cargo, what's the wing, what's the engine, and what's the Earth's magnetic field. So we've now reached an era where, thanks to massive scale GPU compute, our ability to take these quantum the kind of math that was designed for physics problems run that on those GPU platforms. We can now denoise the signal in a way that can make these devices viable.
But you've hit on something very important. It's not unrelated to the quantum cryptography question, where you say, now we can break all these encryption algorithms. What happens if we lose access to GPS? Right. So you're flying airplanes or you have your military is tremendously reliant on these space assets. And so.
You're just walking around town.
Or you're just walking around and you're trying to find the Congress. Center for that. It's harder than it looks. Okay. You know, this is a way to to get around that problem and have dead reckoning for position.
Sorry, I'm a physicist, and I want to go back to the Einstein question. Okay, but wait, I want to save you for something else. So I'm going to do the Einstein question real quick. And then I'm going to I'm going to come back to you. You want to. Okay look this is the bottom line. You've got a zero and one. And the in the quantum system it's going back and forth between those. It has some, ability to move between those. And now that's happening in another quantum system. It's going back and forth between a zero and one. And the question is, are those related to each other? I mean, is this one up, one that one's down. And how are those related? And if you entangle them, their motions are connected. In other words, they'll do something like this or they'll do something like this as opposed to just randomly. And what Einstein said is if you really connect those things, it won't matter how far apart they are. If you if you interrogate this one, this one will will answer. Or do you want to jump in? I'm sorry. Maybe I'm butchering it. No, no. That's okay. To give you the background, the really interesting thing here for the students, it's not whether Einstein was right or wrong or. No no no no no. He asked a really good question. The and then people have been thinking about this for many years, and the violation of Bell's inequality. One Nobel Prize a couple of years ago, kind of showing experimentally what was going on. But Einstein really understood this was a good question. It was a great question. He forced he what he tried to do. And this was the dialogue of these two scientists. One had proposed an idea and the other one said, I'm going to work with your idea and try to find a situation where it just it makes sense. I mean, it just doesn't make sense anymore, but it turns out to be that it's an incredibly fascinating question, because if you get these two quantum systems connected to each other and they're phased, they could be the universe apart. And if you interrogate one and say, I've now found out it's a zero, it will affect the outcome. It'll either have to be a zero or a one at the other one. And so they're correlated over the any distance instantaneously. And that's the spooky action at a distance. And quantum computing would not work without that incredible effect. So we.
Should we should just say maybe today Paul, today there are entangled quantum systems on the Earth and in a satellite. That's right.
And it's being used for communication and encryption. Okay. So now I want to do two. I still have two more questions to get through. And we have to open up to the audience very very shortly. So you have to have in Singapore a small country, but it's a very punches above its weight, if you like or hate to use pugilistic and analogs, but it's a country that contributes to the world outsized compared to its size of its population in a way that's very beautiful. And so, you know, you've got to be getting people educated. And so what do you see happening? I mean, are you approaching, you know, a lot of young people here, you know, we just want to kind of give them some, some, some thought about how they can go through the system and find a way, in an area. What would you say to the if this was a young, an audience of young people in Singapore? And.
Well, what we have learned over the years is that young people need role models. They need to have people to look up to. They want to know what good this technology, the science can do for humankind. Did you have a higher purpose in life? And we have to find ways of making it feasible for them to pursue their ambitions in this way. So what we have done is to create scholarships for people, as you also have them available in your ecosystems to go as far as they can. But more importantly, I think whether they get to do the work in Singapore or they collaborate with their international partners, everybody would like to be plugged into a network where people are thoughtful about the kind of problems that deserve to be properly interrogated, and hopefully in time to come. Solutions found to so, the international collaboration. So what your company is seeking to do in Singapore, what we are trying to do to support the Novo Nordisk Foundation's ambitions in our part of the world and with a whole host of other partners, whether they are in Japan, whether they are in Europe. And, and this is the kind of environment that we hope to be able to create. And I would just add one more thing. I think it is important for us to be able to define the audacious questions that we are prepared to put, resources into addressing. If we simply talk about how exciting this field is. But there are no resources for them, no resources for talented people to pursue these important endeavors, I think that will always come across as being empty talk. So we are very fortunate that from about 20 years ago, we set up the centre for Quantum Technologies. You may well be familiar with my colleagues who work out of that centre, and we are very proud of the work that they have produced. They consistently rank in the top ten of research institutes worldwide. And I think in terms of the age index, which is something that you're familiar with, they also do quite well. I think these are, acknowledgements of their contributions, but I think what keeps them going is the sense that, we take this as a serious opportunity. We take this as an opportunity to do something better for our society. And we also take, the the question of how to deal with the risks seriously. So, for example, they can see for themselves and they can apply their knowledge in helping us to already put in place a quantum safe communication network. And we don't want to stop there. We are bringing on board, the first, a continuum, quantum computer in into Singapore. And this is the first one that is going to be outside of the United States. And we also have a vibrant startup ecosystem, because what we have learned is that there are many different aspects of helping to make this technology, workable. And it's not just one question that needs to be resolved. There are tons of questions that need to be answered, and the more vibrant we can create as an ecosystem, the the more likely we're able to make advances.
Okay, I'm going to take my moderator's prerogative and explain one last thing, and then we're going to open it to questions. So sadly, you cannot go to the store and buy a quantum computer today. And despite these brilliant people and, 100 years of people working on it. And the reason is because if you make a system go back and forth between 0 and 1, and you've got two of them and say they're going like this or they're going like that, the world around them, they're so sensitive things happen and they get confused. And so, you know, they start out going like this and then suddenly and that's right in the middle of your compute and the whole thing goes to pot. That's called dephasing. And, the goal has to be at this point to fix those problems. We're getting better and better at it. Existing quantum computers, of the kind that John co-invented, reached a scaling threshold not too long ago. Which means that the bigger you make it, the more accurate it gets, which was a big deal. So we're getting close. But I think we did enough today, because if we don't ask for questions from you, I don't think we would be doing the right thing. And maybe we can squeeze a couple of things about, you know, scaling and stuff like that in the question part. We'll see. So I'm going to open it up. We'd love to hear your questions. All questions are open. There's no there's no bad question. And so we'll start here.
Thank you so much for the presentation. I wanted to ask you about the quantum readiness and the quantum advantage. Are there any metrics already in relation to how do we actually measure if we are ready, and what kind of advantage does exist? And if not, who should be able to certify this this kind of John. Thank you.
I think the basic idea is you run a problem on your quantum computer and you run it on a classical computer, and you have quantum advantage, quantum supremacy. If you can get it much faster on the quantum computer. That's a very simple, good operational definition. There's a little bit of a problem there, because you have to choose the best classical algorithm you can do. And, you know, part of the problem, it's kind of a sociological problem, is you're building a quantum computer and you run it. Are you really motivated to find the absolute best classical algorithm? And if you talk to the most brilliant algorithm developers in the in the world to do that, but it's a pretty good operational definition. And then if you claim that, you know, all these computational physicists jump in and try to, you know, improve your algorithm, which is actually very good science. So that's kind of the way I think about it. I think in the end, what is really going to be the deciding vote, if you like, is if you can have so much advantage and do a useful problem that you can start making real money off of it. Exactly. And then the, you know, people have started to do a little bit.
, even with current generations.
Then then you really know.
Okay, we had a question on the at the, at the edge there or did you not did you stop having a question. You stopped having a question okay. Raise your hand.
We wanted students.
I know I'm trying. So yeah. No, no, no. On the edge there. Right there. She's got her hand raised right there. I want to go to her. What you got?
Thank you very much for the presentation. My question goes to, Lynn Loehlin, I think. McLaughlin yeah, yeah. Thank you. So my question is, now that we're talking about quantum, faces in technology, how do we avoid white, the whitening and the existing racial and global inequalities? I mean, we need to consider this in terms of public health. How do we consider its availability, accessibility across the world? I mean, it's not it's not just supposed to be a technology that should be restricted to the rage or to, a particular race. So are you considering how this will really impact the global health?
Yeah, this is a fantastic question. Okay. So, so I should I should disclose, part of my life was being a policymaker. I worked for President Obama in on his white House staff. Can I just stress President Obama? And, and so, so this, this question about equity in the development of a new technology is really fundamental. And if I can just say one thing, there is, there is a profound need for governance and the kind of governance, that we need is, multi-stakeholder. It's inclusive, it's transnational. It is cross-sectoral. And that is something that we do not see right now around the world the way that we need to. So governance doesn't just mean regulation by governance. It means broadly, what are the rules and the conditions and the constraints that govern a technology. And to your point, what are the mechanisms by which we make it available on an equitable basis? To all the all the populations that are affected by it. So, I don't have a brilliant thing to say about this other than maybe to raise a dire warning, which is that, you know, the kinds of technologies that we're talking about are classic, dual use technologies, meaning you can use them for good and you can use them for ill. You can design drugs to combat diseases. You can design poisons and pathogens to kill people. And so the imperative of governance is just very profound. And maybe I would just end by just making a plea, which is that, no technologist should be working on a technology. No company should be working on technology, no researcher should be working on technology without thinking about what their contribution to governance, and, access, could be.
You wanted to contribute. Yeah.
So in the Novo Nordisk Foundation, we collaborate with the Gates Foundation and the Wellcome Trust, which are the three largest private, non-profit foundations in the world. And so we work actually in explicitly towards making new technologies available for low middle income countries. And it's an explicit goal to to really consider equal access to such technologies to make them benefit all countries throughout the world. Also, to enable every single country in the world to have ownership of their own data. And this is like exceptionally important and something you don't see from all actors in this space. And it's something that we really strive to have the data of the country benefiting the people who live in the country and have the people living in those whatever country they may be from, benefit from these new technologies. It is sometimes hard because, first of all, some of these technologies are not yet mature. Also, they're really expensive. So it could be for a beginning. It would be like a few times a few years time like before they would be available. But we really strive to make such technologies available. So for instance, we do something called the Real World Evidence Platform together with Gates and Wellcome, which will soon be announced. That will allow, the Global South to have access to well-tested algorithms that will accelerate primary health care. So just to say it's something we have a very laser focused target on exactly what you are saying there.
You say, yes. That young man back there with the blue.
I would like to ask a question raising a problem that I think all of you are familiar with. The. In the year 2000, the Clay Mathematics Institute posed the millennial problems among of them being the n equals a p equals NP problem with kind of problems that have solutions and problems that the solutions is easy to verify. Once all the problems of quantum computing are resolved and they are able to be commercialized, meaning that we don't have binary computers anymore at home, but quantum computers, do you guys believe that there will.
Be both? Okay.
You know, of course. But once the once kind of many problems are solved and these are more developed, do you believe that the likelihood of solving this millennial problem will increase?
John. , I'm not an algorithm person, so I'm sorry, I can't answer that. I do want to answer, though in a practical sense, one has to be careful about using these algorithmic and deep computer science, ideas. You know, AI, there is not a lot of theoretical understanding from it, but people generated it and got results. And, you know, you see where it is now. And that just came from people seeing what works. And of course, eventually you understand more what's going on and the same kind of thing, kind of thing might happen in quantum technology. It may not obey some mathematical theorem you thought about, but practically it still could be useful. So there's there's a real advantage not just looking at, you know, the deep mathematical aspects, but just empirically trying things and see if it works.
We got next. To go right here. Sorry.
Students.
I'm trying.
I'm also a student.
You see, one point to me, okay?
I'm also a student from the University of Konstanz. But I wanted to ask you already mentioned encryption, and, I was wondering how, the well, like, in the finance, this whole blockchain market and, well, how encryption will kind of develop the blockchain also, and how finance will develop through quantum computing.
To my knowledge that the cryptocurrencies, have to take extreme, early measures to not be vulnerable, because obviously, if somebody has a decryption method, they can take all the Bitcoin in the world and become very wealthy. Yes.
I don't want to speak too much, but, but this is something that we've been working on, which is, you know, we have, you know, commercial tools to upgrade your encryption. Basically, the standards that Kim was referring to are available. The national standards bodies of US and Europe and others have accepted these new encryption algorithms. And so now.
That are already quantum ready.
Already quantum ready. Yeah, we have put them through the wringer and we've got at least three, maybe more that are that can do the job.
Which you can do by saying, I'm going to pretend that I have a perfect quantum computer and see what that sort of what that does, even though we don't have.
That's right. Can we make our, our data hard for quantum computer to read? Yeah. And so, so, just one note about the blockchain thing. So for any of you that are kind of like enthusiasts about blockchain, you should be pushing for your blockchains and anything you're working on to be to be embracing these new standards like today.
Because you don't know when somebody's going to suddenly pop up and they've got the deal here, right?
They're not going to. issue a.
Press release. People were talking about neural networks for, you know, 30 years, and then boom, one day, you know, now boom, one day, let's say, people, it was developing. But there was a moment when it just really hit and some people were ready for that, and some people were just kind of floored and are still trying to recover.
And one, you know, one just very interesting irony is like the dominant cryptocurrency, Bitcoin is dominant in part because nobody's in charge. Right. There is no overlord like there are in things like Ethereum. But that presents a huge collective action problem that it's going to have to overcome. Because for Bitcoin to survive a.
Quantum attack, it has to change.
Not only the protocol has to change, but every wallet has to upgrade. The minute that somebody can go in and duplicate a transaction, the whole thing goes to zero. You know, it blows up unless they can solve this collective action problem.
Students, students students students students. Okay. Right there. White shirts. Yeah. Some students in the back there. Let's let's go there.
We're sorry.
No offense to non-students.
Her students.
In your review, what is the most important skill students should develop now to contribute into, quantum technologies in the future?
Channel. What's the most important skill a student could develop now to be part of this, big movement that's going on here?
Well, of course, depends what you want to do. And I will speak as an experimentalist. And I think the most thing is you need to have actually have a good knowledge of a variety of different scientific and engineering disciplines.
Yeah. I mean, I think what part of what this discussion has shown is, you know, you might come from a physics background, computer science, finance, you might.
Be operations.
Port operations, you know. So I think there will I will say, if you did want to say, I want to be a part of this, and I want to have like a maximum impact, high probability of maximum impact, it might be if you were bilingual in two things, you know, you know, enough to understand the underlying kind of ideas of quantum that we were just talking about and enough to understand a business or a use case and be because in any, area, people who can speak two languages across groups of people who can't talk to each other very well, often have a real premium in that sense. So, you know, you might be able to split your skills and find that there's two things I also just have to say, I mean, this is going to be an exciting development there going to be lots of opportunities, but it's also kind of important to follow something like, you know, it is also important to find something that you just enjoy doing. You know, there's so much pressure, there's so much pressure in the world to line up with something that's going to happen and so on. But you know, when you find something you love, you'll probably be really good at it and you'll be quite successful. You were going to go on that.
We have good examples, even in AI, because for a period of time everybody was thinking this do I have to learn data science? Do I have to become a machine learning engineer? And what we are finding is that when it comes to AI implementation, the data scientists and machine learning engineers can't do very much unless someone brings their domain knowledge to the table.
Yeah.
So the only person who knows about the difficulties in manufacturing is the manufacturing technologies. The only person who knows about how to optimize the production line is the process engineer, who knows about where the bottlenecks are and where you need to apply the sensors. So there you go.
Can I say one more thing to them? I'm sorry. Quick thing here. I do want to emphasize we're now in a world where, AI tools can produce answers to questions of certain value. And I think for you, as you're developing your skills of how to think, it will mean that you'll learn how to think with machines. But don't allow yourself to to don't allow that to inhibit the growth of your mind as a thinking mind. Because in the end, the human centered approach to being a great thinker is what will drive your success. Even in an era when machines seem to be able to do some parts of thinking.
So I would say it's extremely important what the core expertise and I've seen that, say quantum mechanics is something it's just difficult to learn what you are above, say 40 years old. So you better do it when you are younger and then you can always build on top with other things. I mean, even as an investor, it's just really important to have a core expertise, be that molecular biology or any other field, but you just so much more certain whatever you do, if you have a core expertise on top of everything else which I've been that has been said, I would say the ability to collaborate, that does become more and more important. And that's something that in AI, for instance, cannot do. This is the human factor that you add on top of everything else.
I will go with that because I will say, it turned out if you do study quantum, it's a very beautiful subject. Okay, who else have we got here? In the back there. We've got somebody is that, you know, not having as many hands now.
Second row here.
Two. Oh. Second row. Okay. Sorry. We'll come to you next.
Hello. I just wanted to say thank you for taking the time to share your knowledge with such an interesting topic. My question to you is, could you also share perhaps the limitations on quantum computing, both from its intended use perspective as well as any limitations that come in its process of research and development?
Good question. I've tried to emphasize I don't think it's it's going to substitute for the other forms of computing. It's going to add to them. So it will expand things. But yeah.
I think that's a really important point, Paul, that quantum computing will be good at certain types of problems. We've talked about complex problems with big molecules. So chemistry and material science, those are places where we can see there'll be real benefits to using quantum machines. There are other problems that will always be just as amenable to be done on classical computing. So there's going to be a period where we learn how to use these two types of systems together. So that's one of the things I think is important. Not every problem needs to be turned into a quantum computing problem. And so we'll need to learn how to work between these two worlds more facilely.
Maybe it's even three worlds, right? Because there's the world of zero and one computing, and there's the quantum world, and then there's this neural network AI world. And I think all of them are going to coexist. And frankly, if some new form of mathematics emerges that we haven't thought about yet, for those of you who might go off and become amazing mathematicians that might get used to represent information in some other kind of computer we've never heard of.
Let me just try to be a little bit specific. The quantum computers we have are not large, and even the ones that we want to build in the future might not certainly not as large as our classical computers. So if you're thinking about big data, the kind of applications that people are thinking of, that's a kind of a classical compute. But if you can take that data and distill it down to smaller number of bits or smaller number of qubits, then you could run on a quantum computer some specialized algorithm to find the special information you want. So it's a matter of kind of the data size that you also have to think about. But again, all this is not 100% understood. And people need to try the different algorithms. Understand it.
Yeah. Yeah. There is a tendency sometimes for people in the science community to say this will never happen. And often those are, you know, mistakes to is just thrown out a challenge to somebody to go find a way to do it. So, we got time for one more question. I think we have one here right in the front, second row.
Yes. Thank you for this presentation. My question is for Geo. I think you so you talk about best place for younger people, and, well, my question is your generation with this, world now and is not very successful. So we have we have big problems around off the road. So my question is why we still don't have young people in charge here in this stage. For example.
Did you get that?
Why are there no young people on the panel.
Yeah. Okay. Who wants to take that one?
The moderator will fix that the next time. I think it's a great question. I think getting young people involved.
Poking us.
Yeah. Getting young people involved in understanding what matters to you and also applying our minds in a way that is cognizant of the impact to your generation. Policymakers have a role in this process. We have to be thinking, deep and hard about, the value of the decisions that we're making on your behalf. We have to think about the consequences. But I also want to suggest to you that, you do have agency, just as you have posed this question to us, there are other contexts that you can pose the questions to, and also in terms of how you apply your talents, your gifts, to the problems of mankind that are important. You can also make a contribution if there is, if there are occasions that you feel that, the future is slipping away and that, you know, there are so many things that are not within your control. I can fully empathize with that, and I can understand that. But I want to suggest to you that that cannot be further from the truth within your own spheres of influence, together with your friends, with your peers, in the classes that you attend and the jobs that you will engage in, you have something within you that can be the seeds of change. So I want to encourage you in that. I'm not sure that that answers your question, but I hope that, it gives you a way to think about the future.
Very quickly. If you look at AI research, there's a lot of young people on panels and the like. And I think what you're not seeing is the many decades of research leading up to the breakthroughs, where now you can abstract away and young people can be very creative. And we're still kind of wandering in the desert phase where, you know, maybe experience matters in terms of putting it together. But our our objective is to build something that can be understood well enough that anyone can look at the phenomenon and start inventing on it. So that's our our goal in the future is to do exactly what you're saying.
Yeah. And big institutions like mine, one of the things we do is go out into schools and try to introduce very young students to these technologies, to demystify it, to make it seem accessible so that you don't have to have, well, I think we would all think of ourselves as relatively young, but that there can be a really diverse population of people pursuing these technologies. So we take your point. We take it very seriously.
Thank you all.
Thank you.