 Ladies and gentlemen, great to see you all in this wonderful panel on quantum leaps. And my name is Freike Heimann. I lead the quantum ecosystem in the Netherlands. And I'm also co-chairing the Global Future Council on the Quantum Economy. And guess what? Quantum technology is already here. And it has been for decades. Prime examples of quantum technologies are the laser and LEDs, light-emitting diodes, from the photonic space. So a little bit of history on the laser. So the first idea was already, came from Einstein in 1950. And it took decades. It took up in the 1950s before the first lasers were actually built in labs. And even it took more decades after the 1980s before lasers became widespread. It's a global market of 12 billion a year. And it's used in medicine. It's used in industry. It's used in fiber optic internet networks. We can't envisage life without it. And actually, this is a very common story of innovation. The time it takes from an idea to prototype, to actual applications, and to an industry, it's a process. It can take decades. And it goes with ups and downs. It goes with accelerations and pulses. It can feel slow and fast at the same time. So take AI, for example. It was already coined the idea, the concept, in 1950. And now 70 years later, we've seen enormous progress. We've been talking about it for the last couple of days with everything we see with generative AI, machine learning, robots, et cetera. And this year really felt like a game changer with chat GPT. But actually, I'm wondering how many industries already have been practically transformed. And me, personally, I'm still waiting to get obsolete that I can do other stuff than the work I do because of chat GPT. So for quantum, we're now in the second wave of the revolution. So the laser was part of the first wave. And it's also the same story about progress. And we see a lot of progress, but there's not yet a commercial industry of billions of dollars. But it's getting there. And today, we will hear from two amazing speakers. They're also part of the Global Future Council of the Quantum Economy. And it's Jack Hidry, he's CEO and founder of Sandbox AQ. And it's Dr. Charles Lin. And he's director at Morgan Chase and also associate professor at Singapore University. But first floor to Jack. Thank you, Federica. My name is Jack Hidry. I'm CEO of Sandbox AQ. It's great to be with you here today. The world of bits. We're all part of this world, the digital revolution. Every phone in our hands, every laptop, every server is in the world of bits. And this world of bits now leads us to artificial intelligence, large language models. What are these large language models? They come to us inspired by the brain. Our brains have 86 to 100 billion neurons. And they have 100 trillion to 1,000 trillion connections we call synapses in our brain. Scientists many years ago were inspired by this architecture of our own biological brain to make an artificial brain, an artificial set of neurons. And now with the chipsets from many GPU makers, we now have deep learning. And deep learning, of course, led to the large language models, the GPTs, that now we are using today. We started with RNNs, recurrent neural networks. And then the paper 2017, attention is all you need, gave us the breakthrough, the transformer, a new architecture of artificial neural networks, one that we do not see in the actual brain, but now we have an artificial brains. And that gives us this powerful, powerful tool. But I'm here to share with you the next level of computing breakthrough, the breakthrough that takes us from just the world of bits to the world of bits and atoms. And so in this world, we now turn again to that GPU. The G in GPU, of course, is for graphics. We started GPUs for graphics because we wanted to have better video games. So next time you see a teenager playing video games, thank that teenager because that teenager was responsible for the beautiful chips that we now have and give us the revolution of AI. But there's another revolution that also we get from these chips, from these GPUs. Not only do they give us deep learning and large language models, we can now run the equations of physics on them. These equations are the ones that govern how a molecule meets another molecule, how a potential drug can meet a receptor, how a new battery chemistry can be conceived, new solar panels, new kinds of clean energy, and diversification of energy sources. These are the GPUs that give us LLMs, but now take us beyond the bits into bits and atoms. And so now let's look, for example, at the drug industry. It takes, on average, 13, 14 years to get one molecule to become a medicine. It takes, on average, 3 billion euro to get from molecule to medicine. And even with all that time and money, most of those drugs will fail. In fact, 80% of drugs will fail when they get to clinical trial. So what can we do about this? How can we model these drugs well before they get to human trials? Now we have that ability. With AI and quantum, we have the ability to model, to simulate, to create a digital twin of that particular drug, and run simulations, not thousands, not millions, but sometimes billions of times, making small changes each time in that molecule. These small changes can give us an improved drug, a drug with more efficacy and less side effects. And so this is a powerful new tool. In fact, this word, simulation, we predict will be in the global lexicon in the next few years equal, standing right alongside LLMs and generative AI. Generative AI, very, very powerful when you have a lot of data, when you could train on large data sets of words, images, videos. But in the case of novel drugs, in the case of novel technologies, there is no data to train on. We need to simulate and build new synthetic data sets, and this we can do from the physics itself. This is not a guess as to what the drug will do. This is actually what it will do, because it's governed by quantum mechanic interactions, the interactions of electrons to electrons in the world and in our bodies. And so we talked about the drug discovery area and the transformation that we actually need to bring down the years and the time and the cost to make it possible to hit diseases such as Alzheimer's, Parkinson's, brain cancer, pancreatic cancer, cancers that have eluded good treatments for decades. But now let's turn to clean energy. Here in the Emirates and around the world, we are seeking energy diversity. We're seeking to store energy when we can, and that storage of energy requires batteries. We've been stuck with essentially the same chemistry for battery power for about 40 years, lithium ion technology. There's been some improvements, and here there's a conference going on showing some of the most advanced EVs, electric vehicles. And again, wouldn't it be wonderful if we could have batteries that weighed less, that cost less, and took us longer range? We can then take us into a new era of mobility. The same for energy. No matter how we create the energy, we want to store that energy in stationary batteries, batteries in buildings like this. And so how do we go beyond the current chemistry of batteries? Well, there's a lot of possibilities out there. If we take the periodic table and take the 17, 18 chemicals in there, the elements, and try to combine them, that's a huge number of possibilities. In fact, it's the word trillion 14 times. That's how many possibilities there are. But now with AI and simulation, with bits and atoms, we can start to address this new area. We can create new kinds of battery chemistry that take us beyond just lithium ion. We can create new kinds of energy sources and store them in ways that we couldn't before. So while AI itself is quite powerful, AI plus simulation is even more powerful and takes us into a whole new realm. This is a realm that we can envision transformation of sector after sector of our economy. And we welcome you to join us in this, Bits and Atoms Revolution. Thank you. Let me hand over to Charles. Hello, everyone. I'm Charles Lim from JP Morgan Chase and Co. I oversee the research and development of quantum communications and cryptography in the company. So today I'm going to walk you guys through the overall narrative of what is the quantum threat. It is a developing issue today that we face and many organizations and government bodies that are working hand in hand together to mitigate this threat. So let me start by moving forward to the next. You see the quantum threat is an emerging issue that by now I guess most of you would have heard and seen about it and might have even done some studies about it, right? But unlike any typical cyber risk that you face today, the quantum threat is quite a fundamental one. And why do I say that? First of all, if you understand, how do we get security in the internet, right? The premise of internet security lies on the domain of cryptography, right? The science of securing information in the presence of adversary. So how do we achieve this in practice today, right? Basically, the foundation lies on the assumption that certain computational problems are hard to solve with state-of-the-art computing techniques. And what does this actually mean for people in practice? First of all, you have to realize that when you have a technology, right, or a cryptographic method that is dependent on the state-of-the-art computing technique, you need to evolve accordingly with computing advances. And for that reason, you would see that with your CISOs and your CIOs, you would need to work with regulators, with security agencies to upgrade your cypher suites, right? And to upgrade your security systems accordingly to the power that you face from advances in computing techniques. And that is where quantum computing brings about a game-changing event, right, in an inflection point. Because at that moment, the trend that you are using or you're developing your crypto suite to match the computing power that you have today will no longer be sufficient enough. And for that reason, you need to migrate to more sophisticated techniques that will be quantum-resistant and at the same time, secure in the long term. And for those of you here who are responsible for sensitive information that have long shelf life, right, or dealing with long retention schedule documents, you will need to plan for security, not only within the next three years, but also potentially for the next 10, 15, 35 years or even a permanent retention schedule is required. And there are two solutions to this. The first is commonly known as post-quantum cryptography, right, PQC in short. And what does it do, right? PQC is a method that is trying to, you know, reinstate the security principle of computational security, which means you're trying to find again a set of computational problems that quantum computer wouldn't be able to solve in reasonable amount of time, right? And for that reason, it's software-based and it can be, you know, kind of like installed and kind of like deployed in your system with reasonable resources. The second method is a little bit more radical. It's based on quantum technology in the transmission of photonic states. Quantum states, in fact, you know, single particles that will allow you to secure your information system with long-term security assurance. So there are two competing methods, but we shouldn't see them as competitors, right? Because if you talk about crypto agility, you'll want to have defense in-depth for your cyber systems. So moving forward, I'll say, to look into the future, right, what do we have here? This current situation with the quantum threat kind of like incentivizes us to look into a new paradigm which we can collectively call as the quantum internet, right? So what exactly is the quantum internet? You can view it as a collective of local quantum computers in different geographical locations connected, you know, with quantum communication channels, right? And once you have this distributed network, you'll be able to achieve new features that wouldn't have been possible with classical method that would allow users to protect their credentials without revealing it in practice. And this is extremely powerful because it kind of like decentralizes, you know, the trust that you now put into a system in the root of trust that you have today can be decentralized and given it back to the user. So this is the future for the quantum internet. Thank you. The audience as well. So, Jack, if you can also come back to the stage and we're gonna have a sit. So are there just first to infantize? How many questions, who of you has a question? Okay, well maybe we have to get things going a little bit because I can imagine. It's a very shy audience, I think. Yeah. Or it's too complex still. So maybe that's the first question. It is a complex topic for everyone. So we need to train people to grasp what we are doing. So what is your view on talent and workforce development? Well, I can start and then turn to Charles as well. And Fedeke, you yourself do a lot in training, of course, with Quantum Delta, an incredible program in the Netherlands, which is a exemplar of how people both get trained, but also how they immediately move from training to starting companies. So one thing I would like to share is that when we look at the world of AI and quantum, it's very, very critical that we do not have a divide. We know about the digital divide that happened 20 plus years ago. There were five billion people who did not have access to high-speed broadband, who did not have access to smart apps on their phone. Now, we're starting to close the digital divide. Every quarter, every three months, there's 300 million people in this world who are now getting, for the first time in their lives, access to smartphones in India, for example. Mukesh Ambani is now selling a $12 phone as an example that will take 600 million people in India from pre-web, pre-Internet access of smart apps into the world of smart apps. That means education, that means health, that means e-commerce. And so the digital divide is now closing. But as the digital divide is closing, Fedeke, we are now seeing another divide open. We're seeing the AI divide open and the Quantum divide open. And that's very concerning to all of us. There's only a handful of countries right now of the 200 countries in the world that are leaders in terms of AI. We are excited to see that in the Emirates, the emergence of Falcon, an open source LLM that was created right here. That's a great example of a country leaping forward and taking advantage of ideas and putting forward Falcon as an example. But in the Quantum world, we're seeing even a bigger divide than we see in AI. And so I think it's incumbent upon all of us, leaders here in this country, leaders around the world, tech leaders, like all the three of us here, to say how do we come together and not only train people in universities, PhDs, masters, so on and so forth, but also the workforce, the adult workforce? How do we go and upskill our current workforce? Not everyone must become a Quantum engineer or an AI engineer, but how do we train business people to use and leverage these technologies? So I think there's both an adult workforce issue as well as a training issue. And again, just to bring it back to Quantum Delta, the program that Fedeke leads, and it's probably too modest to talk about in the Netherlands, is translation of workforce training into startups, immediately starting companies and getting companies going that can then work with large companies such as JPMorgan and many others. Yeah, so I think Jack really touches us on a lot of important points, but I'll say that in terms of hiring, I wouldn't review JPMorgan hiring strategy in Quantum here, but I would share that certainly they're upstream and downstream highest that we're looking at. For downstream, you don't necessarily need a PhD in Quantum, but you need to be able to pivot to the right skills that's needed in this journey, right? So for that reason, for you to be able to attract the right talent and to upskill them for people in the mid-career, I would say you need to have a very solid platform. You have to have a runway that's long enough for people to pivot and to gain credibility and accomplishment in this journey, right? Only then you'll be able to attract people in their mid-career to join you in the downstream kind of engineering work. So going back to your point for upstream, training students in Quantum engineering is a multi-disciplinary problem, right? You cannot just do this alone with the physics department or computer science department or even the engineering department. You need integrated sciences to come together. Who's faculty members will have expertise, right? In implementing some of the networks or quantum computers around the world to come together to train these students in terms of the implementation, but also the challenges you would have to face, that you need to overcome, be it in research or in terms of engineering know-how. So I think it's a very big challenge for all of us and it has to be an ecosystem push to get this to be done. Yeah, and to add on this, Quantum is fun. Everyone who is active in our community loves their work. We work 24-7 because this technology is... We enjoy it. We enjoy it, it's fun. And there's also already ample opportunities to play around with quantum systems and to their demonstrators. So please feel invited to join our journey. So maybe now looking at the audience. Yes, this is Mr. and the second one. If you can say who you are. So there's all this new technology like quantum computing, like AI. And it's very promising. It looks fantastic. But when we look at the macro data, economic data like productivity, it's not increasing. So it's increasing slowly in many countries. In countries like the UK, it's even backwards. So we don't see really that it changes the way we produce, the way we work, that it contributes to productivity increase. So what is the reason? Perhaps you already gave some elements of the answer. You also said that it takes 30 to 40 years until new technology triggers down and becomes perhaps adopted at larger scale. Perhaps that's the reason, but perhaps you have more information on that. Yes, well, the 30 or 40 years is in general, it was exemplary, but I think it's important to state that quantum is still in its early days. So there's a lot of R&D going on and startups, so there is an economy in the R&D space, but not yet commercial applications on a big scale. And I think that's important. We talked about hype in several of the Global Future Councils and we want to have a realistic picture. So it's coming, there's no doubt about that. And there's a lot of developments, but when do we actually see the economy rising into the billions of dollars? I'll just make one quick comment. I think it's a very good question. When we look at the impact of AI in quantum and we look at it on sector by sector basis, you can certainly find AI already in GitHub co-pilot as one example, which is increasing the productivity of coders. So now we see that programmers of code are increasing their productivity. There's some good stats that just in the last 18 months we're seeing a pickup in the ability of people to produce code. That doesn't mean we're gonna lose coders. Actually, we need all the coders and they're gonna produce even more code. The average car, the average automobile that people here, everyone here drives has 100 million lines of code. So we need a lot of code in this world. When we look though at the bigger picture of vertical by vertical, we're used to in the tech world of Moore's Law. Things double in speed every 18 months and have in cost. But that's only when it comes to chips. Most verticals in the world, most sectors in the world do not work in Moore's Law. In fact, what we say is they work on eRooms Law. eRooms Law is more spelled backwards because actually it's getting slower and more expensive over time. And that's the case in drug discovery and development. Drug discovery and development is slower today than it was 20 years ago as a vertical, as a segment. We believe that AI and quantum together are gonna have a massive impact, for example, on that $1 trillion industry. And I think the good news there is from the global divide perspective that those countries that were not part of the biotech revolution and have not been part of the drug revolution can now leapfrog their incumbents and actually take a leadership role because the new tools allow you to jump ahead. That to me is a very exciting prospect. Yeah, maybe I'll just chip in. So to answer your question directly, I would say that for any emerging technology when you want to implement it in an organization, you know, potentially a large organization, it's gonna be a multifaceted problem. It's not only purely a technology problem, you have to consider the stakeholders surrounding this line of business, how they would kind of like receive this new technology, how they would drive it for their own business results, and also the controls in place, the regulatory pressure around it. You know, many things needs to be changed in order for a technology to be really efficient and kind of like get the number that the academia will kind of predict. But at the end of the day, what I'm trying to say is that is no emerging technology would be just, you know, plug and play. You need to spend effort not only in the technology, but also in terms of, you know, the surrounding factors around it that would really get it to work at an ecosystem level. And maybe one last thing on this. It's important to distinguish different quantum domains. So you have quantum sensing, which is already there, and already there's a market for it. There's quantum communication. Charles, you mentioned about the quantum internet that's also starting to be there. And then there's quantum computing, the big thing, you can buy quantum computers, but it's not yet the quantum advantage age. So more questions. Yes, the lady. Hi, my name is Reem. I have a question for Jack. First of all, thank you both of you for your insights. Excuse my ignorance, this is not my field, but when it comes to testing, the whole premise of testing is discovering something that never existed or side effects that never existed. And so I can't seem to understand how we use quantum and AI, which is based on previous data, previous information, to simulate things that never existed when it comes to, you know, trying a new medicine or solving these kind of issues. So that's my question. Great question, thank you for asking it. So absolutely correct. One of the limitations of AI on the AI side of the house is that you need previous data to train on. And that's why there's many, many attempts in the last 10 to 15 years to use AI to galvanize faster drug discovery and 99% of those efforts failed for the exact reason that you just cited. This is why we in this industry are very excited about another tool that sits side by side with AI, this tool of simulation, because in simulation, to your point that you make, we can actually start to work from first principles, from the physics itself of how a molecule will hit a receptor. We can simulate a digital twin of that molecule and a digital twin of that receptor. We can model it in a very large computer system that says if we had such a molecule, then it would bind or not bind with this certain specificity to the receptor and then we can change the molecule and this is what's absolutely critical to your point. We can change it millions and billions of times, adding a carbon, adding a ring, taking away something and then do it again and again, not based on previous data, but based on actually the equations given to us by Einstein, by Niels Bohr, by Schrodinger and others 100 years ago. It was their dream in the 1910s and 20s that this one day would happen. They wrote about it. Ernest Schrodinger wrote a book called What is Life? But they didn't have the computing at that time. In fact, this computing only became available to us, to the human race, only 24 months ago. And so this ability to compute forward, to look forward in simulation, not using past data, this is the key and your question is absolutely on point. We need both of these tools. If we have healthcare data, for example, Cleveland Clinic is here in the Emirates and they're building up data sets, of course, we can make some predictions based on those electronic medical records. But to your point, a novel drug, there is no data. We need this new tool, simulation. First on GPUs and then as quantum computers get more and more powerful, we'll combine GPUs with GPUs, quantum processing units in a hybridized computational setup. So this is why we're excited. So we have two more minutes, so I'm sort of curious to see in the audience, how many of you feel you have to do something with this information or with the technology? Are you ready to engage or, so just show hands if you think this is something I need to get into. Some people, okay. Okay, that's... Can we go back to Charles' point earlier than his talk about cybersecurity? Yes. Is that okay? Because I think that Charles brought up a number of key points and I think it's worth bringing that out a little more to your point about what can people do today? Yes, exactly. That has impact today. And Charles, I think you very well, if I could say, summarize and capture the fact that AI and quantum, they give a lot. They have a lot of opportunity for massive positive change in the world. But we must also recognize that AI and quantum are two of the biggest threats to our cybersecurity infrastructure in this world. The data that we all have and are in hospitals, are in companies, are in governments, is we try to secure it, but AI can look for patterns and try to get internet works and quantum, of course, as it scales, not the quantum computers of today, but as it scales, breaks the cryptography. Charles showed that and was talking about that, that all the public key cryptography, when you have WhatsApp in your phone and it says encrypted end to end, this encryption will be broken. When you have e-commerce and you're trading with credit cards and swift transactions over the internet, this will be broken. And so one of the takeaways I hope we can all share with you is that from a cybersecurity perspective, AI and quantum are threats. And one of the takeaways for action, to Fideke's point, is to have conversations at the board level about what companies, what governments are doing about this. There's a set of best practices that many, many banks are doing. JP Morgan is one of the leaders in securing the JP Morgan customer data as an example. Many others are doing this and Fideke in her quantum Delta incubator has several companies already addressing these issues. So this is a sea change in how we must think about cybersecurity. And I hope that's one of the takeaways from today. Yes, so one takeaway and the other one is the opportunities that quantum will bring for the years to come. And with that, I would like to thank you too and of course you to be here. And in 15 minutes, I guess there's a next session here also in the beta zone format so you can hang around to wait for that. And we will be around in the coffee corner if you wanna chat some more. Thank you. Thank you. Thank you. Thank you. Thank you.