From High-Performance Computing to High-Performance Problem Solving / Davos 2025

Summary

This panel discussion focused on the current state and future potential of quantum computing. Experts from industry, academia, and policy discussed the technology’s progress, applications, and challenges. They emphasized that quantum computing is not meant to replace traditional computing or AI, but to complement them in solving complex problems that are currently intractable.

The panelists highlighted several real-world applications of quantum computing, including optimizing supply chains, drug discovery, and financial portfolio management. They noted that while quantum computers are not yet widely available, early implementations are showing promising results in specific use cases. The discussion also touched on the various physical architectures being developed for quantum computers, with panelists suggesting that diversity in approaches could be beneficial.

A key point of discussion was the need for ecosystem development around quantum computing, involving collaboration between universities, industry, and government. The panelists stressed the importance of developing talent and use cases alongside hardware improvements. They also addressed concerns about the potential risks of quantum computing, particularly in the area of encryption, and the need for preparedness.

The panel highlighted the global implications of quantum technology, with a particular focus on the risk of a “quantum divide” between developed and developing nations. They emphasized the need for international cooperation and investment in quantum capabilities across different regions to ensure equitable access to the technology’s benefits.

Overall, the discussion painted a picture of quantum computing as a rapidly advancing field with significant potential, while acknowledging the challenges and uncertainties that lie ahead. The panelists encouraged continued investment and exploration in the technology, predicting that its impact will become increasingly apparent in the coming years.

Keypoints

Major discussion points:

– Explaining quantum computing basics and how it differs from classical computing

– Current and potential use cases for quantum computing in areas like supply chain optimization, drug discovery, and financial modeling

– The complementary nature of quantum computing alongside AI and classical computing

– The need to develop quantum computing ecosystems with public-private partnerships

– Concerns about a “quantum divide” and ensuring equitable access to quantum technologies globally

Overall purpose:

The goal of this panel discussion was to demystify quantum computing for a general audience, highlight its current capabilities and future potential, and explore both the opportunities and challenges as the technology develops.

Tone:

The overall tone was optimistic and forward-looking. Panelists were enthusiastic about quantum computing’s potential while also being realistic about current limitations. There was a sense of excitement about recent progress, balanced with acknowledgment of remaining technical and societal challenges. The tone became slightly more cautious near the end when discussing potential risks and regulatory considerations.

Speakers

– Azeem Azhar: Founder of Exponential View, technology newsletter

– Amandeep Singh Gill: UN Secretary General’s envoy on technology

– Georges Olivier Reymond: Co-founder and CEO of Pascal, a quantum computing company

– Ana Paula Assis: Chair of IBM EMEA and senior vice president

– Paul Alivisatos: President of the University of Chicago

Additional speakers:

– Anna-Paula Assis: Chair of IBM EMEA and senior vice president (likely the same person as Ana Paula Assis)

РTorbj̦rn Atlan: Professor at ETH Zurich

– Vandita: From Australia (no further details provided)

– Bahia Jafar: From Kuwait, associated with Kuwaiti Danish Dairy Company

Quantum Computing: Complementary Technology with Transformative Potential

This panel discussion brought together experts from industry, academia, and policy to explore the current state and future potential of quantum computing. The conversation covered a wide range of topics, from the basic principles of quantum computing to its real-world applications and societal implications.

Understanding Quantum Computing

The panelists began by explaining the fundamental differences between quantum and classical computing. Paul Alivisatos highlighted that quantum computers use qubits instead of binary bits, allowing them to perform certain calculations exponentially faster than classical computers. Georges-Olivier Reymond emphasised that quantum computing is not meant to replace traditional computing or AI, but rather to complement them in solving complex problems that are currently intractable.

Ana Paula Assis elaborated on this point, explaining that quantum computers can process tasks that would require enormous capacity from traditional computers. She noted that some problems are impossible to solve with classical computers due to the sheer number of possibilities, making quantum computing a potentially revolutionary technology for certain applications.

Current and Potential Applications

The discussion then moved to the practical applications of quantum computing, both current and potential. Ana Paula Assis highlighted the technology’s potential in optimising supply chains and logistics, particularly in industries like oil and gas. She provided a specific example of ExxonMobil using quantum computing to optimize natural gas transportation. Georges-Olivier Reymond mentioned quantum computing being used in drug design and financial portfolio optimisation, noting that these applications are already being implemented with industrial partners.

Amandeep Singh Gill broadened the scope of potential applications, suggesting that quantum computing could revolutionise fertiliser production and address food security issues. This diversity of applications underscores the wide-ranging impact quantum computing could have across various sectors.

Developing the Quantum Ecosystem

A significant portion of the discussion focused on the importance of developing comprehensive ecosystems to advance quantum computing. Paul Alivisatos stressed the importance of public-private partnerships and university collaborations in driving innovation. Ana Paula Assis outlined IBM’s roadmap, which includes plans to have quantum computers with 100 million gates by 2029, potentially “scratching the surface of quantum utility.” She also mentioned IBM’s collaboration with Cleveland Clinic, which has its own quantum computer for studying molecules to accelerate treatment development.

Georges-Olivier Reymond discussed a European initiative where quantum computers are being deployed in data centers to build an ecosystem and mitigate risks associated with different technologies. This approach aims to foster development and practical use cases across the continent.

Amandeep Singh Gill raised concerns about an emerging “quantum divide” between nations with and without access to quantum technologies. He emphasised the need for international collaboration to ensure that less fortunate countries can develop their own quantum ecosystems, preventing a widening technological gap.

Challenges and Risks

The panelists addressed the challenges and potential risks associated with quantum computing. Ana Paula Assis noted the potential threat to current encryption methods, highlighting the need for new, quantum-resistant cryptography. Amandeep Singh Gill mentioned the “store now, encrypt later” concern, where adversaries could store encrypted data now and decrypt it later when quantum computers become more powerful.

Paul Alivisatos suggested that the ethical considerations surrounding quantum computing are similar to those of other powerful technologies, emphasising the need for responsible development and use. The importance of data quality in quantum computing applications was also highlighted during the audience Q&A.

Quantum Computing, AI, and Classical Computing

The discussion explored the relationship between quantum computing, AI, and classical computing. Georges-Olivier Reymond explained that quantum computing can enhance AI by generating training data, while Amandeep Singh Gill suggested that quantum computing could potentially reduce energy consumption compared to current AI systems, offering significant benefits for large-scale AI applications.

Ana Paula Assis highlighted the development of hybrid approaches that combine quantum and classical computing, further emphasising the complementary nature of these technologies. This integration of quantum capabilities with existing systems emerged as a key theme throughout the discussion.

Global Implications and the “Quantum Divide”

Amandeep Singh Gill’s point about the potential for a “quantum divide” to emerge between nations with and without quantum capabilities was a crucial aspect of the discussion. This concern echoes existing disparities in digital and AI infrastructure, with Gill noting that no African country is in the top 200 list in terms of AI infrastructure, and less than 0.5% of GPUs worldwide are in Africa.

This observation led to a broader discussion about the need for international cooperation and investment in quantum capabilities across different regions. The panelists agreed that addressing this potential divide is crucial to ensuring equitable access to the benefits of quantum technology.

Expanding Beyond Quantum Computing

Paul Alivisatos broadened the scope of the discussion by highlighting that quantum systems have applications beyond just computing. He noted that quantum technology can also be applied to sensing and communications, opening up additional avenues for innovation and impact.

Technological Advancements and Open-Source Initiatives

Georges-Olivier Reymond emphasized the lack of fundamental limitations in their quantum technology, quoting physicist Alain Aspect’s statement on the matter. This suggests significant potential for future advancements in quantum computing capabilities.

An audience question highlighted the development of an open-source software stack called Pulsar, created by Pascal for programming quantum computers. This initiative demonstrates the growing ecosystem and collaborative nature of quantum computing development.

Conclusion

The panel discussion painted a picture of quantum computing as a rapidly advancing field with significant potential, while acknowledging the challenges and uncertainties that lie ahead. The panelists encouraged continued investment and exploration in quantum technologies, predicting that their impact will become increasingly apparent in the coming years.

Key takeaways include the complementary nature of quantum computing to classical computing and AI, the importance of ecosystem development and international collaboration, and the need to address potential disparities in access to quantum technologies. As the field continues to evolve, balancing technological advancement with ethical considerations and equitable access will be crucial to realizing the full potential of quantum computing across various sectors and regions globally.

Azeem Azhar: Good morning, and welcome to our panel discussion today on quantum computing, titled From High Performance Computing to High Performance Problem Solving. If there’s any area of technology today that is exciting but requires a lot of demystification, it’s quantum computing. And that’s what we will do in the next 40 minutes or so on this panel. I’m Azim Azhar. I’m the founder of Exponential View. It’s a technology newsletter which covers many topics, including quantum computing. But more importantly, we’ll be hearing from experts, starting with, on my left, Anna-Paula Assis, the chair of IBM EMEA and senior vice president at the firm. Then we have Georges-Olivier Raymond, who is the co-founder and CEO of Pascal, a quantum computing company. Next to him is Amandeep Singh Gill, who is the UN Secretary General’s envoy on technology. And finishing up the line-up is Professor Paul Alivastos, who is the president of the University of Chicago. Now, quantum computing promises to fundamentally transform a large class of very difficult problems. Quantum computers introduce new words into the English language. A few months ago, Google announced a quantum computer that undertook a particular calculation in a matter of hours, which would have taken a traditional computer septillions of years. Septillion not a word I think many of us use in our day-to-day language. But at the same time, quantum computing is a challenging technology. It’s a challenging science. It’s becoming challenging technology. And Jensen Huang, who is the boss of NVIDIA, the world’s largest chip manufacturer, said soon after that breakthrough, well, useful quantum computers are 20 years away. Now, quantum is of course about indeterminacy and uncertainty in some way, and nothing brings that home to us more than those two different pieces of news. So as we work through this, the forum has done a lot of work in building a quantum ecosystem, trying to understand the quantum economy, trying to bring a public-private partnership together to help countries and firms get ready for this. There is a new report, Embracing the Quantum Economy, which you’ll be able to find on the forum website. And being the age of social media, there’s also a hashtag, hash WEF25, that you can use. But let’s start with some basics. And George, if I could turn to you first. For an audience that is perhaps not expert in quantum computing, could you outline what it is? How should we understand quantum computing compared to traditional computing? Thank you for the question. And first of all, I would like to thank the WEF for the invitation. It’s a great honor for me to be here today. And as long as you don’t ask me to explain quantum computing in two minutes, I’m fine. Or less, indeed. I think I can answer the question. There is a lot of excitement around quantum computing. You mentioned them in your introduction. I think this is great for the community. It shows that the technology is delivering and is making progress steadily. And as a physicist myself, I mean, it’s been a long time ago, but I’m still a physicist. This science is very surprising. It’s been more than one century that this technology, this science, is delivering constantly. So I think all around the table, we are all confident that it’s happening. And now we have to be super cautious with the timelines, because there are a lot of expectations. Sometimes maybe it’s oversold. And back to your statement of Jensen, what he said exactly, it’s not a useful quantum computer. He said a universal quantum computer. That was his prediction. Maybe he’s right, 10 to 15 years away. But before that, there are useful quantum computers that will happen far before these timelines. The one that we are building at PASCAL, and we have use cases already running on them with industrial partners. And I know that IBM is also doing the same. Yeah. So, Anna, thank you for that, George. Anna, with respect to the distinction between universal and useful, just as a matter of definitions and clarity, what is that difference?

Ana Paula Assis: The difference is when you have quantum systems, really, that are available in a massive scale of usage and consumption. What we are really targeting right now, in the next, I would say, four to five years, is to have quantum computers that can actually surpass the capacity of classical computer to solve some problems that will take, as you said, years and years for a classical computer to solve. And that is really what we are targeting, because there are lots of requirements from a hardware development standpoint, and there are a lot of requirements around algorithms that can suppress the errors that compounding the amount of qubits in a quantum computer can generate.

Azeem Azhar: We had some technical terms there. Yes. And I think maybe it’s worth just defining some of those. And, Paul, I’ll ask you to do that. You know, the qubit…

Paul Alivisatos: Let me back up a little. Exactly. Just give us the basics. So I think you all know a conventional computer works by having zeros and ones and has a certain set of algorithms for how to take numbers that are expressed in those binary formats, add them, subtract them, multiply them, divide them, do all kinds of ands, ors, logics, and so on. In a quantum computer, the number of… There is no zero and one per se. A qubit can be any value in between those, almost arbitrarily. And then, ultimately, there’s a rendering of which probabilities of those it has. So that inherent piece of it lends quantum computing to problems where there are an extremely large number of possible outcomes. As an example, you know, people will say the calculation of molecules, but often that feels a little bit harder for people to grasp. So, for example, let’s say a supply chain problem might turn out to be an example of one where there are just an absolutely enormous number of ways in which that supply chain could be rendered, but some are better than others. And that’s an example of the kind of problem that people, you know, the IBM team, the Pascal team, they might be able to kind of create something which would be a specific computer useful for a supply chain problem. I mean, that’s really, you know, the kind of thing that we’re talking about in this moment.

Azeem Azhar: Right. So let’s summarise that observation, which is that there’s a class of problems that are real-world problems. How do I optimise my factory production schedules? How do I optimise my financial portfolio? How do I model the interactions of two drugs computationally rather than having to go through the expensive process of building them? And it’s far, far, far too expensive. That doesn’t even do justice to the word. It may take trillions of years

Amandeep Singh Gill: or longer for the most powerful classical computers to do this well. That’s why we want quantum computing. Amandeep, from the perspective of civil society, how do you connect that far-off promise with what matters today? Let me bring one other example which kind of reflects this perspective of the global majority, in a sense, and that’s fertiliser production. Critical for food security and the way it’s produced today. Also critical in terms of the green transition, because it consumes a lot of energy. And fundamentally, we don’t understand completely how the compost heap works. So the reaction that go on when nature produces fertiliser. And quantum computing can help us understand that and can help us bring forth new catalyses for fertiliser production that would improve both those prospects, food security and the green transition. These are the kind of issues that civil society, the United Nations, and all those who are interested in the sustainable development goals are interested in. So the sooner we get to those, the more grateful the world would be to the scientists and the private companies that are driving innovation in this space.

Azeem Azhar: It can’t have escaped us that we sit two years after the CHAT-GPT meeting. That’s exactly right. And many of the applications that you’ve talked about improve catalysis, improve scheduling. You see AI companies out there promising that they can do that and showing certain results. At the same time, there is a wall of noise around artificial intelligence. George, why shouldn’t we put our eggs in the AI basket? Indeed, that’s a very good question. I was attending the forum last year and, of course, everyone was talking about AI. It was definitely on every lip.

Georges Olivier Reymond: It’s the same this year, but this year everyone adds quantum after AI. And I think it’s quite important to understand that. I mean, quantum computing will not replace AI, will not replace CPUs. It’s a complementary approach. And now you have three pillars for your high-performance computing solutions. You have CPUs with classical algorithms. You have GPUs for gen-AI. And now you have quantum processing units, QPUs, for quantum algorithms. And each of these pillars is addressing a very specific mathematical problem. But the others cannot do. So that’s the reason why it’s complementary. Of course. We can give maybe some examples. Well, you can think of using, let’s say, a quantum computer to better train the AI by generating data, as an example. Or the thing that we noticed at Pascal on the specific use cases is that the quantum computer, the quantum algorithm, thanks to its quantumness, was able to find correlation between the data that the GPUs were not able to see. Wonderful. That’s a kind of complementary approach that we can have.

Azeem Azhar: I’m going to keep a list of all the new words I’m learning, quantumness being another new word. Paul, can you come in? I think you’re right to point out this question of, okay, AI just hit. Why are we talking about something else?

Paul Alivisatos: So let me just try to help to explain the kind of differences between them at the root. Maybe some of you would have seen just a few weeks ago there was this incredible DeepMind announcement of understanding the weather in Europe, 15 days prediction. And it was conventionally we would have a model of the weather that would say, this is the temperature here, and this is the temperature there, and that’s going to create a pressure and gases. It would make a model of the weather, very complicated model of the weather. And AI says, no, I’m just going to take all the data and find all the correlations between them, and so there’s no model anymore. It’s gone away. It’s something that comes out of the historical data that’s very detailed. Quantum, and it outperformed the models, right? The AI outperformed the models of that period. But in my view, the quantum will allow certain aspects of physical models of what’s really happening on certain kinds of problems to return and to be far more efficient so that when this hybrid of the AI and quantum will be a very powerful way of getting that kind of balance in the different kinds of computing right. So it is another kind of revolution, and you referred to the Chad GPT moment, and I think we’ll have a quantum moment like that where people will see a problem and suddenly it will be, oh my gosh, and AI took decades for that. And I don’t think people six months before the Chad GPT announcement, if you had asked people what do you think of AI, you would have gotten plenty of really smart people saying, oh, they’ve been working on that for decades. It could be another 30 years. And yet there was this moment when it came, and that’s how you should think about this. We could get to that moment soon. We don’t know when. Nobody can say when. Jensen Huang has an opinion. George has an opinion. We have our roadmaps. We don’t know, but it’s going to happen.

Amandeep Singh Gill: But there’s a big reason why we should be thinking of this, and that’s energy consumption. Today’s AI systems, the infrastructure around that is consuming a lot of energy, and it’s increasing exponentially, and people are thinking of co-locating data centers and SMRs, small modular reactors, nuclear reactors. So hopefully we will transition to quantum infrastructure, which would reduce the energy footprint of AI, and hopefully we would be able to work with not only the power but also different kinds of data sets. Like a lot of the data that’s coming out of outer space has quantumness associated with it. It’s not data, binary data that we can apply Boolean logic to. So I think we would unleash all kinds of possibilities of understanding quantum biology, what’s happening in outer space, if we were to also start thinking about this quantum computing infrastructure bridging to that.

Azeem Azhar: I mean, it’s clear that there are some great possibilities for new classes of science. We, of course, have to also ground possibilities into practical plans and roadmaps. So Ana, what’s in IBM’s quantum roadmap? So I think this year was a year of tremendous progress for us, and I think it’s good to do a little bit of a recap.

Ana Paula Assis: We opened our first quantum data center outside of the United States in Germany recently

Azeem Azhar: in partnership with the government. Could you say what’s in a quantum data center? I have a sense of what’s in an AI data center.

Ana Paula Assis: Yeah, it’s really a quantum computer that has all the capabilities to process quantum. It’s a very interesting machine because basically it’s a cylinder where you can perform the different tasks there. And the reason why we have chosen Anaheim in Germany, and we are now announcing collaboration with the governor of Illinois as well and also with the University of Chicago, is because you have to think about quantum in the sense of an ecosystem. You have to have all the research and the development around the hardware and the technical capabilities, but if you don’t work in parallel creating the use cases, to the point that Guillaume made, and the fact that you really have to have clarity on what this is going to generate in terms of business impact, I think we are going to see a moment where people will go into the disbelief of the technology like it happened before. So we have to be working in parallel. We have here a good partner, Algorithmic, that has been working with us exactly on that stack of creating functions and capabilities that will make it easier to program a quantum computer. We have today 60,000 users on our platform. We operate in a network with multiple collaborators, about 250 universities and organizations. And our roadmap now is pointing to, by 2029, we are going to have quantum computers that will have 100 million gates, which, from a technical standpoint, gives you the size and the capacity of what a quantum computer can perform. And that is the point where we are going to scratch the surface of quantum utility. 100 million gates sounds like a lot to me.

Azeem Azhar: Yes. But is it? What could you actually do with 100 million gate quantum computers? At that stage, we believe that we can have quantums that can process tasks that today would require enormous capacity from traditional computers. Fantastic. Paul, this ecosystem idea, public-private innovation, you are at the heart of one of these. Yeah. What are the specific innovations that have come out of the partnership?

Paul Alivisatos: Well, first of all, let me just sort of help people understand the scale of what an ecosystem looks like, as Anna was just saying. So, in our case, the university started building faculty in this area about 16 years ago. And they’re in physics, chemistry, molecular engineering, computer science, and statistics. So, there’s a range of faculty, and they’ve been working. And 10 years ago, we participated with other organizations around us to create the Chicago Quantum Exchange, which includes other universities like Northwestern and Urbana. It includes the two national labs, Argonne and Fermi, and the first startup funds for some smaller companies that we could nucleate in our area. And then, over time, now we’ve been building for a few years now these partnerships with IBM, some with Google, some with a few other large companies. So, now we’re getting… getting to where you see this ecosystem, all these different parts. And where we are right now is thinking that it takes about every time Argonne builds its largest supercomputer, that takes about 10 years to fully first say we want to build it to where it’s completely installed. And Aurora, the latest one there, just finished installation. So we could say today, let’s cooperate all of us together around this ecosystem and prepare the first mixed quantum conventional supercomputer. That’ll probably take about 10 years to fully build that from a pilot phase into the full machine. And that’s where I think our ecosystem, that’s one of the things our ecosystem could do. The other big one that was just mentioned is to develop specific, industry-specific use cases. So you could imagine those things coming together into an important moment. And that’s where the field kind of stands.

Azeem Azhar: Well, of course, University of Chicago is a deep-storied university. The Fermilab, the first nuclear fission, was produced there, a very wealthy university in a wealthy state in a wealthy country with a well-developed technology sector. It sounds like a perfect place to put something like this together. For governments in the global majority nations, Amandeep, should they be actually thinking about the quantum economy today? And if so, how should they approach it? I mean, they have so many other priorities.

Amandeep Singh Gill: Azim, you’ve outlined a major issue, which is the emerging quantum divide. We have the existing digital divide, connectivity divide. There is the AI divide today. No African country is in the top 200 list in terms of AI infrastructure, for example. Only less than 0.5% of the GPUs worldwide are in Africa. So we have a massive problem there. And now this is coming up. And my challenge is to convince the policymakers who have limited resources, say, look, you have to invest on the digital divide. You have to also invest in data and AI. Think also about quantum in terms of at least talent development and applications in certain areas, which is where international solidarity, international collaboration is important. The ecosystems that Paul mentioned, they need to kind of link up and also help those who are less fortunate to develop ecosystems, even if they are small ecosystems, of their own. But could we not also get the use cases? We won’t necessarily need the talent, but perhaps you could feed the use cases into Paul’s ecosystem, Anna’s consultants, and George’s computers. Is that not sufficient? You could. But there is now a realization that unless you understand the technology and do some tinkering with it, do some development with it, the applications are not going to be contextually sensitive, powerful enough, impactful enough. I think there is a backlash against a kind of neocolonial situation where the tech is developed in just a few geographies, and the rest of the world is takers of that tech. You can call it the sovereignty backlash, but I think there is a sentiment, and we have to respect that sentiment and make sure. Also, in terms of the interest we have in the long-term talent flows, long-term development of high-quality data sets, the materials that go into some of this, that there is a more collaborative and respectful and solidarity-driven approach.

Azeem Azhar: Right. OK, so we’re going to come back to this, I think, later in the discussion. I’d love to get to some very, very tangible use cases, things that are actually being done today. And maybe, perhaps, I’ll start with you, Anna. Give us an example of something that you have built for your clients using a quantum or a quantum hybrid approach, where you pair quantum capabilities with traditional computing, delivered results. And why was this only possible using this quantum hybrid approach?

Ana Paula Assis: One example is what we are doing with ExxonMobil, for example, with their strategy and research division. They wanted to see what’s the best, what is the optimal supply chain and logistics structure to transport natural gas. It’s a very complex commodity to move around. You have to have some kind of real-time demand and supply planning. And this is one of the types of applications that we have been working with them on. Cleveland Clinics is a great partner of ours. And they become really so intense in the usage of quantum computer that they decided to have their own in the era of studying molecules that is going to accelerate the development of treatments. So the reason why only quantum can do that is exactly for the reasons that the professor explained. The way that the technology works allows you to have multiple simulations in parallel that will take forever for a traditional computer to do, because a traditional computer would do multiple iterations until it finds the answer. One at a time. One at a time. So I think that those are the typical use cases that you’re seeing are in supply chain optimization, investment portfolio.

Azeem Azhar: Can you give us a number on one of those? Was there an IRR or a productivity benefit that was achieved?

Ana Paula Assis: So far, you’ve been able to get answers much faster than what it would take for you from a traditional computer. So the analysis that we did for this ExxonMobil use case is I would take the whole amount of atoms in the universe in the amount of possibilities to compute how many routes you could do to transport natural gas, which is impossible. So there are problems that are really impossible to solve. So once you can have that type of capability, you’re really optimizing the way that you are consuming fuel. You’re really optimizing how you’re going to deliver solutions to your clients. So it’s hard to give an exact figure

Azeem Azhar: because the implications are very broad. Understood. Georges, you must have an excellent use case. Yeah, absolutely.

Georges Olivier Reymond: And I will have to choose, because just this year, we published more than 40 scientific papers around applications using our technology. I will pick up maybe two or three. The first one is in drug design area. It’s a use case that we developed alongside with a Cupid pharmaceutical. And with them, we were able to introduce not some quantum nest, but some quantum algorithm inside their workflow of operation where there was a bottleneck for classical solutions. And the result is that we can better predict the reactivity of the docking sign of a protein. That’s the first example. We are working a lot with electrical utilities to optimize the grid, to reduce the carbon footprint. Because if we optimize the grid, there are, of course, less losses. We are also working with utilities to better understanding the aging and corrosion of materials inside nuclear plants. And for me, materials could be the Chad GPT moment of quantum. It’s a quantum problem. We have quantum computer. It’s a good match. And the last one is about portfolio optimization, since you mentioned it. It’s not very fancy, but it works. We did that with a bank, with real-world data. I mean, it’s very tangible, very concrete. And what we achieved with that, what we saw with that is that, thanks to the quantum algorithm, we could see that the bank needed less pre-processing time to prepare the data for their classical solution. So this is already, I think, a positive impact. And what we saw as well is that by having a slightly more powerful quantum computers, it’s more qubit for us. And again, it is in the timelines. I’m talking about these two to three years. We could improve also the accuracy of the prediction. And so we have 40 use cases implemented. We also did, and very carefully and very honestly, a benchmark against classical solutions. And strong on that, we are pretty confident that within two, three years, like IBM, we’ll have its first break-even, let’s say, on a very specific use case.

Azeem Azhar: Right. I just want to get a clarification question with you on that. So when you did these use cases, and you you were using classical computing with quantum computing. This was a real physical Pascal quantum computer with the neutral atoms. It wasn’t simulated in deep QBits.

Georges Olivier Reymond: Yes, thank you. It was not simulation. It was a real world implementation on real world QBits with real experimental results. Wonderful.

Azeem Azhar: Paul, I’d love to hear. Well, I just want to, OK. So I think what you’re saying, correct me.

Paul Alivisatos: I may be wrong about this. But my reading of it would be that these are companies who are understanding that this could completely change their business. And they have teams. And they’re early there defining, I don’t want to call, I don’t mean to say this in the wrong way, but kind of, I’d say, almost toy problem versions of these things. In other words, we’re looking at one of them. And we break it down into the simplest case. And then we try to see if it has the value there. And then as the computers become better developed, they’ll be able to do more and more sophisticated versions of them. So the reason that you don’t see this computer everywhere today is because the problems are more simplified compared to the completely complicated. Because the computers are still, the physical hardware is still being improved and extended. And so there’s kind of this ratcheting. Yes. But compared to two years ago, it’s much more close to where you could go to the Exxon and say, let’s talk about this. The change is rapid. And the problems are getting close. But they’re a little simplified. And that’s why you don’t see it like it’s everywhere today. And that’s why Jensen Hwang can take his shot at quantum.

Azeem Azhar: Sure, sure. You do raise a really important point, which is the state of the hardware. And when we think about building computer chips today, traditional ones, they’re built on silicon. And you get a laser machine from ASML. And you send them to Taiwan. Where we are with quantum computing, the elemental part is the qubit. And different companies have completely different ways of building them. They’ve got wonderful names, superconducting iron traps, diamond vacancy, Majorana, Fermions, not doing so well, neutral atoms, photonic qubits. And it feels like there is a certain amount of risk there. When we look at other traditional industries, there tends to be a standardisation. Engines for cars became four-stroke engines. For lawnmowers were two-stroke engines. Storage, computer storage, largely gone down to the sort of NAND, solid state approach. What are the implications of the uncertainty that is in which physical architecture might actually work? Actually, I want to come to Amandeep on this one Obviously, you speak for a very, very large community that has a lower tolerance for technical risk. The global majority are not venture capitalists.

Georges Olivier Reymond: Do you mind if I start first? You want to start? OK. I would like to highlight a great initiative for the European community regarding that. It’s all about building ecosystem. And indeed, it’s all about also mitigating the risk associated to different technologies. And what Europe did is that Europe decided to equip European data centers with quantum computers to build this ecosystem first. And they decided to do that with different technologies in order to mitigate the risk. And they started with our technology. So now, to that, Pascal, we have two quantum computers deployed in Germany at Jewish Supercomputing Center and also in France at Gen-C. So I would like to highlight this great, let’s say, initiative. I think it’s a great support for the entire ecosystem. And we’ve benefited for everyone because it’s a way also to grant access, to remove access to this technology in a completely open way and also to train the people to the usage of these technologies. Amandeep, I did want to come to you from the perspective of the risk here. Yeah. I see an opportunity more than a risk in these diverse approaches.

Amandeep Singh Gill: Anything that displays quantum properties can be used for quantum computing. The photon, neutral atom, what have you. So I think we are at an early stage in terms of the science of it and the technology of it. So different approaches should be pursued. And who knows which may win, and maybe several will win. And there’s an optimization around the capability, the power, the reliability. And we all know about the error correction side of it, but also the costs around it. So in different contexts, different technologies may work. Look at the nuclear energy scene, for example. Many technologies coexist from PHWRs to LWRs, and now other technologies are coming up. So I think a degree of uncertainty and diversity in terms of the tech development is important. I think where we will have an issue is if you start to lock down certain ecosystems as per geopolitical, geoeconomic preferences, and then you have massive efforts around standardization, that kind of a gain. I mean, we’ve had the internal combustion engine. You looked at the history of these technologies, and it’s the easiest thing to do at that time. And then you pay the costs in the long run. So I think diversity may be a decent thing.

Paul Alivisatos: Let me just add one piece onto that. So we’re talking about quantum computing. In other words, you’re trying to get an answer to a certain kind of problem with this method. But the quantum systems that we’re describing can do two other things. One is sensing, and the other is communications. And for example, for sensing, at Fermilab today, they’re building some detectors that will be useful in understanding the aspects of dark energy and things of that kind that people really care about. That maybe they won’t turn out to be what you would use in a computer, but they turn out to be really important for understanding that new physics. Or as is the case, people are working on new kinds of medical imaging that is going to be absolutely astonishing, I’m quite sure. And maybe those won’t look like the computing ones. And similarly for communications, the ability to have secure communication that cannot be easily eavesdropped on. So I think there may be a diversity of qubits, but there’s also a diversity of applications. And communications are one of them.

Azeem Azhar: Just like you have sensors all around your house today with cameras and all, there are sensors. A complete ecosystem of technologies interact with each other. So it’s going to be OK. It’s going to be OK. We’ve been so optimistic so far, and I’m so glad you brought in other applications. I would just say to attendees, we’re very happy to take questions from now. If you signal to me, I’ll come to you. But I mean, just continue on this thread. You talked about secure communications, which of course, the flip side of that is encryption. We hear a lot about the risks to traditional encryption. Maybe I’ll come to you, Anna. Could you first just very briefly explain what the risk is for those who are perhaps not familiar with it? And tell us where we are and what the appropriate response is today. Yeah.

Ana Paula Assis: Yeah, I mean, by the nature of the technology, it can really break all the cryptography mechanisms that exist today to protect data. So that is really the impact that we can see with that technology. What we have been doing, and first of all, developing algorithms that protect data from that scenario, helping clients understand, looking at their data stack, what are the types of encryption mechanisms that they use that would be more at risk of being impacted by that, and supporting them with mitigation and preparation for that scenario. I mean, encryption is critical lifeblood to any free society,

Azeem Azhar: and it’s critical lifeblood for any modern economy. Amandeep, I would just love to know how you are supporting governments to think about this. Because in some sense, encryption, we may look back historically as almost a fundamental human right.

Amandeep Singh Gill: Absolutely, because it’s linked to privacy and freedom of expression, freedom to hold different opinions. So it’s fundamental. I think there is now a worry about store now, encrypt later when the tech is mature enough. So I think attention must be paid to this, and new standards are being put out in terms of quantum cryptography as well. How do we protect data better against what is coming? To Paul’s point, I think when you look at the global majority, not everyone would be able to do quantum computing or find the resources for that, but sensing is something that I’ve heard often spoken about, less capital intensive, communications as well. I think the cryptographic effort has to be a global effort because you can’t just isolate that. We’ve solved the problem in one place, but the communications are global today. We’ve done so well in this discussion. There are so many hands up for questions. So we’ll start with the gentleman over there. If you could just briefly say who you are, where you’re from, and a brief question would be fantastic.

Azeem Azhar: Just one.

Audience: My name is Torbjörn Atlan. I’m a professor at ETH Zurich. I’m interested in the use cases, business cases of quantum. I thank the panel for being optimistic about it. But I think it will also maybe hit some of the same issues that AI has. I mean, the models are just as good as the data, and particularly in the supply chain space. I think the gas example is brilliant, but most supply chains are global, and they just simply don’t have data. So I wonder what the panel thinks about that, and how we can solve those issues, or if we need to just pursue a lot of developments at the same time.

Azeem Azhar: Wonderful, okay. So we’ll go for one panelist who wants to answer that, who would like to do that. Ana, would you be happy to answer that question

Ana Paula Assis: about the data? Yeah, I can do that. I mean, I think data is really the major constraint today across the board, and even for existing technologies. So one of the things that we have been encouraging our clients a lot is really to work on the data, but quantum will also help on that, because one of the things that we can see, and Guillaume mentioned that a little bit, is the fact that quantum is going to also help us with generation of synthetic data. That will help with training a lot of the models that will be out there. So I think there is also potential coming from the technology itself to solve the data issue.

Azeem Azhar: Wonderful, thank you. Question from the lady here, yeah.

Audience: Sure, I’m Vandita from Australia. Just wanted to know, as a layperson, last three days, thinking about AI, it’s been interesting to get perspectives on different risks people think of when you talk about AI. So as a layperson, very interested in top two things that keep you awake from a quantum perspective, which is your particular worry or risk or a challenge.

Azeem Azhar: Thank you. Wonderful. Georges, you can give one, and then I will ask Paul to give another one. One thing that keeps you awake.

Georges Olivier Reymond: I have too many to give only one. But the thing I would like to say, I would like to quote one of my co-founder, Alain Spey, who was awarded the Nobel Prize in Physics in 2022 for his early work on quantum entanglement. And his quote is the following. When there is no fundamental limitation, engineers find a path. With our technology, there is no fundamental limitation. We have all the equations, it works. At Pascal, we are engineers, they will make it work. In five years, they managed to move from the academic lab to on-prem installation. We are still facing tons of challenges, having good lasers, having good optomechanical designs. I mean, I will not go into all the details.

Azeem Azhar: That keeps me awake at night, but I know that at the end, it will work. Paul.

Paul Alivisatos: I mean, I think you’re asking whether there are ethical risks and societal impacts that are undesirable. I think, Amandeep, you spoke to the inequities. I mean, I have no doubt that like any technology that’s powerful, it can be used for good and for ill. And we will see those things. And I think, you know, the power of the computing is so enhanced compared to what we normally see that those can be amplified. And as well, you know, you can use, you’re designing a molecule, well, molecules can be used for good and ill. So, you know, in that sense, I’m not sure that there’s a qualitatively new, you know, set of ethical challenges there. But like with many of these things, as they become more powerful, they become, these issues become more acute. All I would say is that I think right now, it’s hard to know how you would think about regulating something when it hasn’t yet done anything. And it’s better to wait for it to actually start to do some things because then you really find out, I mean, you know, we’ve got a pretty good sense that it’s gonna be used for a number of use cases. But till it really gets in there, you know, it’s a little bit too early. So I think coming to a panel like this is the right thing because, you know, you can, it’s in your mind. But I’m very cautious about wanting to start regulating things before they’ve actually ever even really demonstrated that they work. I mean, I don’t mean to say they don’t work, but that’s a little hard. But you know, really work, okay. We’re still a few years away. We’re still a few years away from that moment. Yeah, everyone having a quantum computer in their pocket.

Azeem Azhar: Last question then over here.

Audience: So my name is Bahia Jafar. I am from Kuwait, the Kuwaiti Danish Dairy Company. And my question is maybe for Mr. Raymond. How would you then work, how would we work with you? So if we have a manufacturing entities that has 300, 500 different products and to plan the production is extremely difficult. So we are using, trying to use AI. How would we be able to work with you if you don’t work with Oracle or staff or any of those systems that we actually work with?

Azeem Azhar: Okay, I’m gonna give you about 40 seconds to answer this one.

Georges Olivier Reymond: Okay, so two answers, two fold. First of all, we have developed all the tools that are abstracting the quantum complexity. So today, traditional data scientists can program our technology. We have all the tools, we call that a software stack. It is called Pulsar, it’s open source. You can download it on our GitHub and start working on programming a computer. There is an emulator simulating the behavior of a quantum computer associated. So it’s a way of onboarding you and we have support, example, tutorials and so forth. Last but not least, you have to be sure that you are facing a technical challenge with classical solutions before moving to quantum.

Azeem Azhar: You need to have a bottleneck. Okay. There’s a conversation to be had after this panel ends and we’ve had a fantastic discussion. We started recognizing the indeterminacy that exists in the quantum system. Are things going very well or are things facing lots of hurdles? And I think the wonderful panel has collapsed those possibilities into something much, much more specific, which is there are useful things that you can do today in a range of different use cases, of complex use cases with real quantum computers mapped together with your traditional classical approaches. In order to do that, in order to develop these, the technology, an approach that builds on ecosystems, bringing together the advanced researchers, the people building the systems, algorithm specialists, but of course, also people from the commercial world and critically, the governments that will support this becomes increasingly important. Amandeep really raised the dangers of a quantum divide, which seems to me to be a knotty problem that we need to return to in the near future, but perhaps in future panels, we’ll have even more specific use cases we can talk about and also examples coming from the global majority. So thank you very much to the panelists and to the audience for joining us today. And just make sure that your lunch is not as indeterminate as the quantum states that we deal with over here. Thank you. Thank you. Thank you. Thank you. Thank you.

Agreement Points

Quantum computing is complementary to classical computing and AI

speakers

– Georges Olivier Reymond
– Ana Paula Assis
– Paul Alivisatos

arguments

Quantum computing is complementary to classical computing and AI

Quantum computers can solve problems impossible for classical computers

Quantum computers use qubits instead of binary bits

summary

The speakers agree that quantum computing is not meant to replace classical computing or AI, but rather to complement them by solving problems that are impossible or extremely difficult for classical computers.

Importance of developing quantum computing ecosystems

speakers

– Ana Paula Assis
– Paul Alivisatos
– Amandeep Singh Gill

arguments

IBM’s roadmap includes opening quantum data centers globally

Importance of public-private partnerships and university collaborations

Need for international collaboration to address the “quantum divide”

summary

The speakers emphasize the importance of building comprehensive ecosystems involving universities, national labs, startups, and large companies to advance quantum computing technology and ensure its global accessibility.

Similar Viewpoints

Both speakers highlight specific industry applications of quantum computing, particularly in areas requiring complex optimization and simulation.

speakers

– Georges Olivier Reymond
– Ana Paula Assis

arguments

Drug design and portfolio optimization in finance

Optimizing supply chains and logistics for industries like oil and gas

Unexpected Consensus

Diversity in qubit technologies

speakers

– Amandeep Singh Gill
– Paul Alivisatos

arguments

Diversity in qubit technologies may be beneficial for development

Importance of public-private partnerships and university collaborations

explanation

While the diversity in qubit technologies might be seen as a challenge, both speakers view it as an opportunity for innovation and development, encouraging multiple approaches and collaborations.

Overall Assessment

Summary

The speakers generally agree on the complementary nature of quantum computing to classical computing and AI, the importance of developing comprehensive ecosystems, and the potential of quantum computing in various industries. There is also a shared recognition of the challenges and opportunities presented by the diversity in qubit technologies.

Consensus level

The level of consensus among the speakers is relatively high, particularly on the fundamental aspects of quantum computing and its potential applications. This consensus suggests a unified vision for the development and integration of quantum computing technologies, which could accelerate progress in the field and encourage more widespread adoption and investment.

Different Viewpoints

Timeline for useful quantum computers

speakers

– Georges Olivier Reymond
– Ana Paula Assis

arguments

Georges Olivier Reymond discusses specific use cases for quantum computing in drug design and financial portfolio optimization. He mentions that these applications are already being implemented with industrial partners.

Ana Paula Assis explains that quantum computers can process tasks that would require enormous capacity from traditional computers. She emphasizes that some problems are impossible to solve with classical computers due to the sheer number of possibilities.

summary

While Georges Olivier Reymond suggests that useful quantum computers are already being implemented, Ana Paula Assis implies that the full potential of quantum computers to solve impossible problems is still in development.

Unexpected Differences

Diversity in qubit technologies

speakers

– Amandeep Singh Gill
– Ana Paula Assis

arguments

Amandeep Singh Gill suggests that the diversity in qubit technologies is an opportunity rather than a risk. He argues that different approaches should be pursued as we are still in the early stages of quantum computing development.

Ana Paula Assis outlines IBM’s quantum roadmap, which includes opening quantum data centers globally. She emphasizes the importance of creating an ecosystem that includes research, development, and practical use cases.

explanation

While Amandeep Singh Gill sees diversity in qubit technologies as beneficial, Ana Paula Assis’s focus on IBM’s specific roadmap and ecosystem development suggests a more standardized approach. This difference in perspective on technological diversity versus standardization is unexpected and could have significant implications for the development of quantum computing.

Overall Assessment

summary

The main areas of disagreement revolve around the timeline for useful quantum computers, the approach to building quantum ecosystems, and the perspective on technological diversity in qubit technologies.

difference_level

The level of disagreement among the speakers is moderate. While there is general consensus on the potential and importance of quantum computing, there are notable differences in perspectives on implementation timelines, development strategies, and technological approaches. These differences could impact the global development and adoption of quantum computing technologies, potentially leading to varied approaches in different regions or by different companies.

Partial Agreements

Both speakers agree on the importance of building quantum computing ecosystems, but they differ in their focus. Amandeep Singh Gill emphasizes international collaboration to address the ‘quantum divide’, while Paul Alivisatos focuses more on local and national collaborations between academia and industry.

speakers

– Amandeep Singh Gill
– Paul Alivisatos

arguments

Amandeep Singh Gill raises concerns about an emerging ‘quantum divide’ between nations. He emphasizes the importance of international collaboration to ensure that less fortunate countries can develop their own quantum ecosystems.

Paul Alivisatos emphasizes the importance of building ecosystems that involve universities, national labs, startups, and large companies. He describes how these collaborations can lead to significant developments in quantum computing.

Similar Viewpoints

Both speakers highlight specific industry applications of quantum computing, particularly in areas requiring complex optimization and simulation.

speakers

– Georges Olivier Reymond
– Ana Paula Assis

arguments

Drug design and portfolio optimization in finance

Optimizing supply chains and logistics for industries like oil and gas

Key Takeaways

Quantum computing is complementary to classical computing and AI, not a replacement

Current quantum computers can already solve some problems impossible for classical computers

Major potential applications include supply chain optimization, drug design, and financial portfolio optimization

Development of quantum computing requires ecosystem collaboration between academia, industry, and government

There is a risk of a ‘quantum divide’ emerging between nations with and without quantum capabilities

Diversity in qubit technologies may be beneficial for development at this early stage

Quantum computing poses a potential threat to current encryption methods

Resolutions and Action Items

Continue developing quantum-classical hybrid approaches to solve real-world problems

Expand quantum computing ecosystems through public-private partnerships and university collaborations

Work on addressing the potential ‘quantum divide’ through international collaboration and knowledge sharing

Unresolved Issues

Exact timeline for when quantum computers will achieve broad practical utility

How to effectively regulate quantum computing given its early stage of development

Best approach to ensure equitable access to quantum computing capabilities globally

How to fully address the threat to current encryption methods

Suggested Compromises

Pursue multiple qubit technologies in parallel rather than standardizing on one approach too early

Balance development of quantum hardware with creation of practical use cases and applications

Combine quantum capabilities with classical computing and AI in hybrid systems

Quantum computing will not replace AI, will not replace CPUs. It’s a complementary approach. And now you have three pillars for your high-performance computing solutions. You have CPUs with classical algorithms. You have GPUs for gen-AI. And now you have quantum processing units, QPUs, for quantum algorithms. And each of these pillars is addressing a very specific mathematical problem. But the others cannot do. So that’s the reason why it’s complementary.

speaker

Georges Olivier Reymond

reason

This comment provides a clear framework for understanding how quantum computing fits into the broader computing landscape, addressing a common misconception that it will replace existing technologies.

impact

It shifted the discussion from viewing quantum computing as a standalone technology to seeing it as part of an integrated computing ecosystem. This led to further exploration of specific use cases where quantum computing complements AI and classical computing.

Today’s AI systems, the infrastructure around that is consuming a lot of energy, and it’s increasing exponentially, and people are thinking of co-locating data centers and SMRs, small modular reactors, nuclear reactors. So hopefully we will transition to quantum infrastructure, which would reduce the energy footprint of AI, and hopefully we would be able to work with not only the power but also different kinds of data sets.

speaker

Amandeep Singh Gill

reason

This comment introduces an important perspective on the potential environmental benefits of quantum computing, connecting it to broader concerns about energy consumption in the tech industry.

impact

It broadened the discussion beyond just computational capabilities to include environmental considerations, adding a new dimension to the potential impact of quantum computing.

Azim, you’ve outlined a major issue, which is the emerging quantum divide. We have the existing digital divide, connectivity divide. There is the AI divide today. No African country is in the top 200 list in terms of AI infrastructure, for example. Only less than 0.5% of the GPUs worldwide are in Africa. So we have a massive problem there. And now this is coming up.

speaker

Amandeep Singh Gill

reason

This comment highlights a critical global issue that hadn’t been addressed earlier in the discussion – the potential for quantum computing to exacerbate existing technological inequalities.

impact

It shifted the conversation to consider the global implications of quantum computing development, prompting discussion about international collaboration and the need for inclusive development strategies.

So we’re talking about quantum computing. In other words, you’re trying to get an answer to a certain kind of problem with this method. But the quantum systems that we’re describing can do two other things. One is sensing, and the other is communications.

speaker

Paul Alivisatos

reason

This comment expands the scope of the discussion beyond just computing to include other applications of quantum technology, providing a more comprehensive view of the field’s potential.

impact

It broadened the conversation to include quantum sensing and communications, leading to discussion of additional use cases and potential impacts beyond just computing applications.

Overall Assessment

These key comments shaped the discussion by expanding its scope from a narrow focus on quantum computing to a broader consideration of quantum technologies within the larger tech ecosystem. They introduced important perspectives on environmental impact, global inequality, and diverse applications of quantum technology. This led to a more nuanced and comprehensive exploration of the potential impacts and challenges of quantum technology development.

How can we address the emerging quantum divide between developed and developing nations?

speaker

Amandeep Singh Gill

explanation

This is important to ensure equitable access to quantum technology and prevent widening technological gaps between countries.

What are the specific innovations that have come out of public-private partnerships in quantum computing?

speaker

Azeem Azhar

explanation

Understanding successful collaborations can help guide future ecosystem development and investment.

How can we quantify the tangible benefits and ROI of quantum computing applications?

speaker

Azeem Azhar

explanation

Concrete metrics are needed to justify investment and demonstrate the practical value of quantum computing.

How can we ensure data quality and availability for quantum computing applications, particularly in global supply chains?

speaker

Torbjörn Atlan

explanation

Data quality and availability are crucial for realizing the potential of quantum computing in real-world applications.

What are the potential ethical risks and societal impacts of quantum computing?

speaker

Audience member (Vandita from Australia)

explanation

Understanding potential risks is crucial for responsible development and regulation of quantum technology.

How can traditional businesses integrate quantum computing solutions with their existing systems?

speaker

Audience member (Bahia Jafar)

explanation

Practical integration strategies are needed for businesses to adopt and benefit from quantum computing.