 Welcome back from our studio live, as you could see, in Halle. The next talk will be Natalie Kilber. She will talk about tails from the quantum industry. Nacho works since many, many years on quantum computers to make them real and useful. Hi, I'm Natalie, and I'll be talking about the progress, the prospects, and the poppycock, the nonsense of quantum technology, or you could also say, tails from the quantum industry. A little bit about me. I'm a prehistoric creature that has been there since the field emerged. I'm the master of stories for you today, and you might ask yourself, but why are you so gung-ho about quantum computing, buddy? Well, if you look at the Moore's Law's trends, then you know already that since 2000s, the clock speeds have been kind of stagnating, and we're going smaller and smaller. And IBM is going to fabricate a chip of about 2 nanometers in 2023. The problem here is, the smaller you go, if you go smaller than 1 nanometer, towards sub-nanometer scales, then you go into the quantum regime. And if you have a single electron transistor, you already have a quantum dot. That means that's a qubit. That's part of a quantum computer, but it's not reliable for classical computers, for any classical computation. So well, there we have it. We already are in a quantum regime if we want to go into the future. So why do I want quantum computers? I'm gung-ho about speed. I'm gung-ho about premium power. I want more juice. So first look at your PC. Now back to me. Now back at your PC. Sadly, it isn't an 8-core i9, or maybe it is. So yet, are you happy about your wiring? Well, I don't know why, but this fella is also happy as a muffin about his wiring. This is a quantum computer in Google's lab. And you can see the wiring is not trivial for this. And this is just one little chip. So a quantum computer is an accelerator, and you need a co-host CPU to do any sort of meaningful computation with it. And if you look at the wiring here, there's a different type of quantum computer. You see an optical table with optical components on it. I think this one is the Q-era startup. And yeah, this guy's not that happy about his wiring. I can see why. There's lots of other examples that look a bit difficult. And here specifically, you see a lot of controls that are sending signals into the quantum computer. And this one, again, is Q-era with a bit better wiring. This is specifically a trapped ion quantum computer. So they use trapped ion. Quantum computers don't come in one flavor. We have different flavors with different bases of fundamental technology that we use with different types of components. So in trapped ions, they use photonic components. There are photonic computers in itself. And for example, this one is a huge cryogenic fridge. So you go up to milli-sub-Kelvin stages right at the bottom. In the first picture, you've seen it without his clothes, without the enclosing, the enclave, and then again at the top, the massive wiring for just one chip. Then you have other examples like, for example, AQT, Alpine quantum technologies over there in Austria. And they know how to stuff their cables really well. And well, you might wonder, why am I not talking about quantum annealers? And well, if we define quantum computers so legs, then a salad is a quantum computer too. You know why a salad, any type of plant, uses quantum phenomena as well? So because of photosynthesis, the light tries to travel as fast as possible to the side, and they do it through quantum tunneling. And it solves it pretty fast. Yeah, so no quantum annealers. Then look back at your PC. Can it stem 5 gigahertz? You think you're unhappy about your clock speed? I think I win the complaining game. A quantum computer can do no more than 100 kilohertz. And that's twice the speed of the aniac back in the day. But then again, don't be so harsh on your setup or on the quantum computers. We still tinker with them with the capabilities we have. You've seen in the pictures before, there's a lot of wiring. They're components that are quite big, that haven't been invented yet. So the bottleneck component of a quantum computer of any setup, the slowest component is your bottleneck clock speed or your bottleneck in your clock cycle. And that's why they're so slow. In quantum computers, you have digital signaling processes. That means you have to convert digital signals to analog and analog signals to digital signals again. And we have that everywhere in our phones and our cameras. Imagine just sound that is analog that has to be converted into digital signals, or literally light photons if you take photo into digital signals. That's an analog to digital interface that we have. And here, because we're shooting microwave pulses and for example, superconducting computers and qubits, that's kind of difficult to do. So yeah, you might say, but they are parallel and they do everything a little bit different. Yeah, for algorithms, when you have such slow clock speed rates, if your time to solution outlives you, that's a problem. If you don't live to see your solution, that's too slow. And you might want to listen to your PC. You think that is loud, 30 decibels, 40 decibels? This is what a quantum computer sounds like. Specifically, you can hear the vacuum pump or superconducting on the IBM. Welcome to Sounds of IBM. Yeah, that's the grid that makes that sound. But you need it that way. So knowing what temperature can be taken for, well, only when you do get into the quiet zone is the dream of living, Kelvin. Stages, then you have a quantum phenomenon that you can control and improve. So yeah, this is nightmarishly loud. And yeah, you do need to quiet with it. But one thing is always the same. We always talk about size. And with size, I mean, we talk about qubits. You might read in Wired or Spiegel or wherever you want. Hype articles or just articles talking about the advancements in quantum computing, how many qubits they could instantiate on a chip. IBM released about 127 qubits and Q arrow, a bit about 200. Q arrow is the trapped iron one. And IBM is the superconducting one with these cylinders and the cryogenic fridges. But here you have to discern a lot of physical qubits is good. But a logical qubit is what you need for computation. You have a high arrow rate for just one physical qubit because of the noise, because of temperature, all types of noise, all types of environmental factors that you can't eliminate yet, because this is quite fundamental research, how you can control these things and how you can adjust the parameters so noise stays low. And then again, we have these types of signals in our normal classical devices where we need parity checks, where we need error correction. And so do quantum computers. Error correction is a huge field that needs to be advanced. And we use things that are called surface codes. And these are error correcting. And to get one logical qubit so we have reliable computation, we need a lot of physical qubits. So you could say there's a lot of overhead for those error correcting codes and parity checks. So if you hear about this many qubits have been accomplished by a company, it's usually physical qubits. But then at a factor of 20, that's just one logical qubit that you can use. Yeah, that's difficult. And there's a famous physicist that said, well, he's still alive, so it's actually on Twitter. And he said, well, qubits are like children. It's better to have a few high quality ones than a bunch of noisy ones. And yes, I agree. And John Presca has been at Microsoft before. Now he's at AWS. But at Microsoft, we witnessed a Majorana scandal. Well, we thought we can have a topological qubit that has no noise. That means if we have one qubit, we don't need these many physical qubits to have one logical one. Because it is a topological one with no, with entrenched error correction, one could say, by the physical nature. So yeah, also Reiner Blatt said, befittingly, it was called the elusive Majorana particle. Because yes, we've been waiting for 10 years for this Majorana qubit, but there was this scandal. The big one of Majorana qubits wasn't a big one. After all, they had to retract the paper that said they found one. So we're still looking for it. Yeah, but then again, it is better to have a few noisy physical qubits than none at all. So yes, quantum computing is full of challenges. You have seen the wiring. Getting so many wires into one of those cryogenic fridges is very difficult. So we have to find new ways to get CMOS those little controllers into that fridge so we have to reduce the wiring, for example. And that's not a trivial task, because you get a lot of resistance when you go colder for cables, for example. We're advancing microwave technologies with quantum computers. And one thing that kind of worries me the most is that we don't have quantum memory yet. So Q-ROM, random access memory. Because at the moment, a quantum computer is just an accelerator. So it's a read-only memory. So everything that is on the chip or on the qubits or on the setup, that is read out like that. You can't store them or you can't do any more meaningful computations. So that's a huge bottleneck. Another thing is the ethical dimension. We have to use in superconducting quantum computers a lot of helium. And helium has supply bottlenecks with just two companies, Qatar Gas and then one Northern Texas company that supplies helium. That is not really the problem, though, because we need even something else. We need three helium, which is an isotope, and that you get as a nuclear byproduct through tritium. That's not something I'm going to count on, especially because these are limited resources. And sometimes the components in quantum computers themselves, they're also rare earth metals. Those are also limited resources. And then people keep talking about democratizing quantum computers. Yet you have other problems there first. Not everyone needs access to something that doesn't solve a lot of things yet. And to be honest, with the security controls in place, it's kind of an open system already. But yeah, when we look at quantum, we have to think about the references. Which specs do you have to look for? And the magic here is common sense, as I've shown you. Compare it to what you know, the components that you know. And again, the magic is common sense. And the quantum computers are very specific. Yet quantum technologies, the components of a quantum computer, the sensors, single electron sensors that we use in MRI, that we use in spectroscopy for microscopes, and yada yada, even more things in quantum communication type. So semiconductors or semiconductor components or just our infrastructure and communication. They can be part of the quantum technology zoo as well. But you have to be also careful. Everything is quantum now. It's quite the hype. So finance is doing somehow quantum. I don't know what other companies think. Well, the buzzword cyber isn't enough and use two buzzwords, quantum and cyber. I'm very curious what they do. Then there's quantum transportation. I'm lost here. I don't know what they do. I don't want to know. And here, I mean, I'm sure that is pain-free. Yeah, you can also have TPN. Actually, I really wanted to find this in April 2020. Yeah, so quantum computing is claimed to solve a lot of today's problems. Some companies claim they're battling climate change, they're transforming the pharma industry, they're transforming the finance industry and they break all encryption in the future, as we know. So quantum computers will break the internet in the future. Yet again, looking at the research estimations, not including the clock speeds or the actual performance, that's difficult to claim. But then again, looking at the references and the facts and the specs, not all claim these weird things, but the reference facts like CLOPs from IBM, these are advancements that are meaningful. But then again, we have this flood of references of qubits, of we're advancing this and that, complexity theory claims. Well, but how are you going to test these complexity theory claims? Well, because we don't have the qubits we simulated on a fantasy machine. And if anyone like this old chap here had to deal with theoretical complexity resource estimates, AKA fantasy language, well, welcome to Imagination Learned. This town, this town is not a nice place for little fillies all alone. There are lots of twists and corners that could lead to the unknown. Let me guide you away and I'll be sure to help you through. You could really use a friend out here and luckily I've picked my free favorite corners for you. Well, quantum applications where applicability is optional. So come on, let's start with a very well known topic, optimization. In the beginning I've talked, I want premium power, I want maximum juice. So, VSLI design, what is a VSLI? It's very large scale integration and it means we need to partition these little chips that we have and the first chip that we had back in the day was an integrated circuit to help people hear better. So it was a hearing aid that Jack Kilby in 1958 designed. And this thoughtful design or the basis of it is the basis for our technology everywhere. And it's not a trivial task to design these chips so we don't have a lot of waste and we can pack more and more components on these little chips. So, integrated circuits, if you don't know it, anything IOT is that. Another problem, the mathematical basis for this problem is the same for network design or less waste and manufacturing like stenching or lasering. Even flight scheduling between cities has this mathematical problem. Some might know it as bin packing, max cut or multi-cut problems. You either seek to minimize or to maximize an objective. So, those combinatorial problems are really one of the hardest ones to solve and I like to call them combinatorial black magic. These levels of hard to solve are classes in themselves and this is actually a real graph. This is a Peterson graph and you can tell it's black magic. You might think this is not that hard but I'll show you a benchmark of max cut problems. This one's NP hard, NP complete. This is one of these fantasy language classes. It just means that no polynomial time algorithms for max cut in general graphs are known. That means, again, your time to solution outlives you and it's a problem if you need to wait until your solution comes and you die before. Or maybe it needs even a couple of hundred years. I don't know how long you live but some say it's almost as hard as beating calamity in dark souls. But yeah, you don't live to see it. That's the drawback of this. So yeah, you might think optimization. It's black magic, it sounds weird but you have heard these terms before. I will specifically be gung-ho and talk about nature-inspired ones, the physics-inspired algorithms. But you know neural networks, you probably know taboo search, linear programming, mixed integer programming. Within branch and cut, you can see the max cut problems that's in the brown part. And then, of course, other nature-based methods like bad search, genetic algorithms, or small methods. Where it becomes quantum is and that's what I like about the nature-inspired part. Nature-inspired optimization algorithms, for example, they minimize the Hamiltonian of an ICM model. So whatever mathematical basis you have, you minimize or maximize your objective. Hamiltonians are something you use in quantum computing and the ICM model I can explain later if we have a little bit more time. So what we need here too with classical and quantum computers is benchmarks so we can compare apples to apples because classical compute and quantum compute is more like apples and bananas. So we need a common ground. And if you want standardized benchmarks for such problems, you can Google Chuck, C-H-O-O-K. It's an open-source benchmark suit and you probably see it in the slide, good old professor Katzgraber, Kat Graber. He has written this benchmark suit and he's gung-ho about cats so please spam him with cat content. So yeah, I told you we'll get into the MaxCut benchmarks. This is from a paper of Cambridge, I think, Cambridge quantum computing. And these little circles, these little dots, these are nodes and you can see they've run it on a quantum computer for 10 nodes and it's very complicated. Yeah, and the problem here is when they went to 13 or 23 qubits, logical qubits, they had to simulate it. They had to put it on a fantasy machine on classical hardware. And yeah, that's also one algorithm they used, VQE, Variational Quantum Igon Solver and QuoA. These are approximate algorithms. You can think of very noisy, annoying quantum computers that don't spit out the correct results but if you run it a hundred times, the majority of it will be towards the correct regime. And yeah, that's how you go about it. This is a relatively new paper and I have to say these resource estimations, these are amazing results and I'm not worried about the algorithmic advances in quantum computing because we have smart people and I want more smart people so if you want to, you should get into it. So yeah, that's not what I'm worried about. Yet, I don't wanna solve something for 10 qubits or sorry, 10 nodes on a quantum computer yet we can solve something bigger. So this is from another paper from a nature-inspired, physics-inspired algorithm. Some already call it quantum-inspired. These are a hundred nodes but at the lowest, you can see the physics-inspired GNN, the pi GNN, they managed to do it with a 10,000 nodes. So on classical hardware, the quantum algorithm put on classical hardware to overcome the QPU hardware limitations by treating these physics algorithms as optimizes. So from a business perspective, if I want to have maximum power and maximum juice, I would use classical computers and use the heuristics from quantum and classical until the quantum computers are ready. So yeah, nature-inspired optimization with quantum algorithms, that's like putting neural networks in steroids. Quite like that. This is the paper for it. But yes, we've been far deep into one corner. So I'll drag you back and I'll show you another one. Some companies claim we're solving climate change with it. We're transforming pharma. And yeah, this comes from ideas of physicists where they said, well, the nature is quantum mechanical. We might as well need a quantum phenomena to simulate them. Well, he's right. But yes, it's not that easy. These physicists played bongos and strip probes. He's a real hero. And yeah, as a physicist, he's known for that. We're talking about chemistry. Here's ammonia. You don't think this is difficult, but ammonia is used for a lot of things in the world. You use it as a base if there's something acidic. You use it as a fertilizer. You use it in a lot of things in chemistry. Even raw latex is being transported with it or anything that has an acidic nature. You get it by a very difficult process. Well, it's not a difficult, but an energy expenditure, high one. So you need high temperatures and high energies to put it into the Haber-Bosch process. And it accounts for 2% of the global energy expenditure. It's a very famous problem that quantum physicists wanted to solve because it's really useful stuff, ammonia. And if we can cut 2% of the global energy expenditure, that's a good thing. It's not trivial though, Richard said it. It's not an easy thing to do. Here you can see just the active side of an enzyme where you can produce ammonia without high temperature and high energy. Bacteria can do it by room temperature, ambient temperatures. There's algae that's all types of bacteria that can do it. And the active side is called Fumoco. Here you can see the resource estimates for half of the sites for the energy to simulate to see how this works because bacteria can do it. We don't know how they do it. That's why we use so much energy and temperature. The enzyme and the bacteria looks like this. And then again, look back at the computer. For both parts we need over 2,000 logical qubits. Now think back, physical qubits are by a factor of 20 or 100 more, so we're not here yet. And then again, classical computers can simulate it either and we will probably simulate it better on quantum but we're not there yet. And to put it into perspective, to the far right, the orange little molecule is the Fumoco bit in the whole enzyme. And you might wonder, what is the THC cost while that's tensor hypercontraction? So yeah, algorithmic advancements, I'm not so worried about. We're pushing the frontiers there. So yeah, but back to imagination lines. The most powerful magic is common sense and you should wield it. So what do you think? Do you wanna use a quantum computer or intermediate steps to find out what we need? Well, what people do these days is they're a bit smarter and they do simulate it. They do use some digital parts, but it's mostly haptic. Haptic means they simulate a little bit and they test it in a lab. And what they test it in the lab, they can funnel down what they need to simulate. The paper I've been talking about for the Fumoco bit with the logical qubits is a very recent one. So it's just a couple of days it's been published. I think this is a preprint even. And if you wanna know anything about resource estimates in quantum computing, for chemistry specifically, Nathan Wiebe and Ryan Babush are a good place to look for. Then we are still at quantum applications where applicability is optional and it has been true so far, hasn't it? Let's move to a corner that hits closer to home, cybersecurity. We have to be specific here. I know a lot of companies claim there won't be any type of encryption as we know of in the future because quantum computers will break it. Well, for once AES-265, 56, my English is bad, AES-256 bit mode can't be broken by quantum computers and symmetric key size, symmetric encryption methods are known to be quantum secure at a specific key size. So not really. What people usually think of is asymmetric encryption. So yeah, these are some resource estimates to look out for. This is a Microsoft paper not too long ago and they said with a punchline it is easier to break elliptic curve encryption than RSA. Then Google not too long ago came up with two million noisy qubits, so physical qubits, to break RSA 2048 bit in eight hours. And then also the newest paper saying that factoring at 2048 bit RSA integer can be done in 177 days with about a little bit more than 13,000 qubits but with a multimode memory that does not exist yet. These are incredible results over the years in resource estimation numbers. Yet again, let's put it into perspective. So 2012, we said it's a billion in this year. 2021 isn't over yet. This year, Google came up with 20 million noisy qubits and then Guzien came up with a little bit of 13,000 or more but let alone any workable implementation of cura-ram is a purely theoretical nature as of now. So we're still in imagination land when it comes to breaking the internet as we know it. It's time to leave fantasy land so you might say, hey, but we did factor relatively high numbers back then in 2013. You've heard this in the news. Well, yes, we did, but if you know the base beforehand, so if you know that with 35, the number 35, you can divide by five or seven. If you know one base, that's a really easy thing to do and you can do that classically as well. So IBM had to counter publish that they were oversimplifying quantum factoring and the algorithm you use for it is Schwartz algorithm. It's one of the purebred quantum algorithms out there. And then again, another one pretending to factor large numbers on quantum computers. So no, we haven't been able to break it so far. Another one in 2019, and this is a very interesting one because IBM goes to these problems and says, yeah, well, I wanna test it. I wanna simulate it, sorry, not simulate it. I wanna test it literally in quantum hardware. And they did so, but they failed to factor just the number 35. So I think we're safe for some time. You have to think of quantum computers not as a quantum threat, but more as a quantum advantage. If someone knows how to steal encrypted data and store it about 20 years to decrypted, you know, get it now and decrypted 20 years. Later and store it somewhere. They probably know where to get it unencrypted as well. They're more low-hanging fruits for them. And I don't think they will wait until the quantum computer comes into fruition to do these sort of things. So let's put the quantum threat into perspective. Quantum computers are logical extensions of Moore's Law's trend. And quantum computers are tailor-made for simulating the behavior of quantum systems like molecules or materials. And whether they lead to breakthroughs in cryptography or optimization problems, that is less clear yet. But we're pushing the boundaries. If anything, components of quantum computers are pushing the boundaries for us literally now. If we have better seeds like quantum random number generators for short QRNGs, that is very useful. We need seeds that are truly random. For example, in places where we can't use true random number generators that use entropy to generate the random numbers because in a data center, you don't want a lot of entropy. So you don't want temperature diversity. You want it to be cold and stay cold. Or sometimes you don't have the possibility of having this anywhere where it's just not there. So we do make things smaller with it as well. You've seen the wiring. So we have to design microwave technology or any type of cabling, any types of chips, preprocessors that can go into smaller and smaller spaces. So, yes, we do need quantum computers and the research around it. We don't need it in business settings just yet because they're not ready. This is still very much fundamental research and we should note that. So, mathematical concepts are more useful to find also new ciphers when we're talking about cybersecurity. And I'm not talking specifically about PQC. There are other mathematical concepts for asymmetric and symmetric encryption that can be used. But for now, let's leave imagination land and let's think about how quantum computers interface with the world. Well, I've shown you before that quantum computers sometimes have a quaggling fridge. So if you look at the cylinder, you see the enclosure of it. So this specific example, I use a superconducting computer for now. I've told you before we need a host CPU and then a control system, lots of peripherals and wiring to get into the cryogenic stage and the enclosure. And there we usually have an analog digital interface and at the bottom where it's the coldest, the QPU. So you can think of it as, yeah, a huge system. So this is an example of Google setup. And I think the key concept that needs to be highlighted here is that quantum computers are merely co-processes and as such, they depend on traditional compute environments to host them. A quantum processing unit at QPU requires an unlocked digital interface to convert those signals back and forth and in turn the application logic in the host CPU that may connect to a network may. Some people think if I have it in the lab and it's not connected to anything, this must be air-gapped. But then again, you know how loud these devices are. So you kind of want RDP so people don't become deaf. And with Corona, you kind of want people to work from home as well. So they won't be air-gapped for the foreseeable future I guess or for the next year at least. So the issue of cyber security and nascent quantum computing resources that is rarely discussed. These systems are and they will be hybrid systems for the foreseeable future with those CPU hosts, with cloud-based or managed APIs and we need reliable services and secure services and architectures as this arises. So subsequently, the critical applications and data these systems will handle and store if it's the knowledge and the algorithms how to simulate FAMOCO so we can produce the ammonia with less energy expenditure. If we design new batteries, these are probably patents so we want to secure the data behind it and those algorithms. So this means that all classical security best practices hold for quantum computers. So yeah, this example of a QC lab at Google. These enterprise system constitute of a mix of Windows, Mac OS, Linux, I don't know, maybe Azure AD, SaaS, network containers, whatever, platforms. And they're part of these industrial control systems and programmable logic controllers, PLCs or discrete process control systems. Anything in ICS, SCADA, this is rarely air-gapped. It means physically separated from any network. So we need API hardening. ICS security is not a big topic in quantum computing yet because it's still just a system on the internet and it's not quite ripe yet. Yet people sell it and companies put sensible data on there. So if the ISS back in the day got infected with the gummy malworm, that was considered air-gapped. No ICS system is truly really air-gapped anymore. So before we offer quantum computing as breakthrough accelerators, we need to make them safe to use. So if you want to join me, let's protect quantum computers from getting pawned. Thank you for listening to me. Let's talk. Thank you so much. We have some time for questions. So audience, dear audience, please ask some questions. The hashtags are on Mastodon and Twitter. Hashtag RC3 chaos zone and the IRC room is the channel RC3-chaos zone. All right, and I will watch the questions. We have some questions already. What do you think about rolling out so-called post-quantum cryptography now? Yeah, post-quantum crypto. I know it's been a useful concept from NIST and they have a specific problem in mind. NIST is for the national security and probably government infrastructure in the US specifically, but they're thinking of a long-lived systems with PQC. You have the problem, it's highly computationally intensive so a lot of infrastructure can't cope with it so you need to deploy other infrastructure. And if you're worried about your data, your intelligence behind your data being stolen and then saved for 20 years, not many companies have secrets that you can store or that intelligence, that specific data that they then steal and store, that is useful. So if you have data that has an intelligence life for over 20 years, yeah, that's useful. If it's a government side, if it's a nuclear bomb place side or something, very critical, yes, you have to think about it now and we do need time to implement the infrastructure. And I mean, it hits close to home. We've heard about crypto agility. It's a thing that we would like to have but it's not the reality. We just have legacy systems. We have to keep them running and especially if it's critical infrastructure, you can just turn it off, build something new and turn it on. It has to work throughout. So PQC is useful for some problems but not for all. It's not a one fits all glove. All right, thank you. The next question is, you talked about the current number of qubits and how no practical problem of the difficult problems that the people are hopeful for quantum computers to solve. That the technology isn't there yet due to the low number of qubits. Would it make sense to serialize the problems and run them on low qubit count quantum computers? Does that work? I think I might not understand the question fully but I assume you mean you package these little problems and I've shown you the algorithm that THC, the tensor hyper contraction algorithm that the chemical guys have used where we do these sort of things but then again, one qubit you can think of roughly as one transistor and you just need a couple more than five or 10 to do meaningful computations. As you've seen, that is a very good question that we do package these problems into smaller bits and if you go back into the slides or look into the paper of Nathan Wiebe and Ryan Babush, you see that you need still about more than 2,000 logical qubits. So you're spot on, this is the direction that they wanted to go and we have to go and they had to try it. The unfortunate, we still need more than a couple of hundred. So are there any current quantum computers that are programmable to do something useful? I mean, it depends on what you mean useful. It's very educational to use them. If you want to have a workforce in 10 years that knows how to use them, you need to have postdocs or master students who know how to program these things, who need to know how to write better compilers, what are the bottlenecks, how we can swap gates, quantum gates, these are operations on a quantum computer so how we can swap these things and that's a useful thing for them to do. In any stage, a workable quantum computer with just a few qubits is still needed to advance the field and to advance the workforce. So for me, this is still useful. All right, yeah, that makes sense. What do you see as candidates for earliest productive uses of quantum computers? Ooh, so you mean the question of the killer application for quantum computers? That's a difficult one, so for cryptography or for optimization, I've said it before, it's less clear, but for chemistry, once we hit those 20,000 or more logical qubits, we'll see advancements in catalysts. You've seen the Fomoco molecule, this is the active site for the nitrogenase to get ammonia at room temperature and that's where I see the advancements for small catalysts for get alloys and metals to find better storage for batteries. There's still a field out there that we couldn't simulate on classical because it's quite intractable, but we're pushing the field and I think chemistry could be one of the first ones, it's just not there yet. All right, do you also think that'll be the earliest ones, chemistry applications? Small molecules for catalysts, yes, they could be. I mean, the smarter people than me out there that might have better ideas, maybe design a completely new battery storage where ammonia is being used in fuel cells as well for storage, maybe they'll simulate how to get ammonia cheaper energy expenditure-wise and then use it to store, have better storage in fuel cells. Yet, I mean, there are some quantum computing services out there that are kind of interesting, depends what you're looking for. I think Cambridge Quantum Computing offers a three-qubit encryption suite if you want to do QKD. I mean, it's a fun toy game, I'm not sure if it's very business-relevant, but if you want to look if your current infrastructure could hold it, that's an interesting one. Quantum communication with the components especially in it, that part of the quantum tech world is more advanced and more ripe, so a lot of devices in quantum communication you can use now already. So it's just about your risk appetite. Do you want to, well, spend a lot of money on it? Do you want to invest into it and try it out? There are some testbeds in Berlin and Paris where they're trying out QKD networks. Yet, you know, this is telecom, this is not quantum computing, but it would be the backbone if we want to have a quantum internet where then again quantum computers are useful. So everything is useful because it's an intermediate step towards something you would like to have, but most of the things in quantum computers, they don't beat classical solutions yet. You talked about the tech sectors on quantum computers that are dramatizing this a little bit. What is the worst case of a quantum computer getting pawned? I mean, worst case is some company has their sensible business data on it and they harvest that. I mean, because they're not, you know, they're not critical components as of yet and there are a lot of down times because they have to recalibrate them, you know, get them off the grid, see if the fridge works or do some sort of maintenance. You don't have these usual SLAs with them yet, but think about all these companies that don't know what they're doing and they might have the critical data up there in the cloud pushing it there and if the API isn't hardened, if it's open access for everything, they may just have low hanging fruit to pick out there. Thank you so much, Nacho. This was Tales from the Quantum Industry by Nacho. Thank you. Thank you. All right, our next talk will be at 1730. What is Algorave? It's about a community that live codes music and celebrates the artifacts and the algorithms that they use.