(upbeat music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies: creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. You're watching theCUBE's Coverage day two of MWC 23. Check out SiliconANGLE.com for all the news, John Furrier in our Palo Alto studio, breaking that down. But we're here live Dave Vellante, Dave Nicholson and Lisa Martin. We're really excited. We're going to talk qubits. Vanessa Diaz is here. She's CEO of LuxQuanta And Antonio Acin is a professor of ICFO. Folks, welcome to theCUBE. We're going to talk quantum. Really excited about that. >> Vanessa: Thank you guys. >> What does quantum have to do with the network? Tell us. >> Right, so we are actually leaving the second quantum revolution. So the first one actually happened quite a few years ago. It enabled very much the communications that we have today. So in this second quantum revolution, if in the first one we learn about some very basic properties of quantum physics now our scientific community is able to actually work with the systems and ask them to do things. So quantum technologies mean right now, three main pillars, no areas of exploration. The first one is quantum computing. Everybody knows about that. Antonio knows a lot about that too so he can explain further. And it's about computers that now can do wonder. So the ability of of these computers to compute is amazing. So they'll be able to do amazing things. The other pillar is quantum communications but in fact it's slightly older than quantum computer, nobody knows that. And we are the ones that are coming to actually counteract the superpowers of quantum computers. And last but not least quantum sensing, that's the the application of again, quantum physics to measure things that were impossible to measure in with such level of quality, of precision than before. So that's very much where we are right now. >> Okay, so I think I missed the first wave of quantum computing Because, okay, but my, our understanding is ones and zeros, they can be both and the qubits aren't that stable, et cetera. But where are we today, Antonio in terms of actually being able to apply quantum computing? I'm inferring from what Vanessa said that we've actually already applied it but has it been more educational or is there actual work going on with quantum? >> Well, at the moment, I mean, typical question is like whether we have a quantum computer or not. I think we do have some quantum computers, some machines that are able to deal with these quantum bits. But of course, this first generation of quantum computers, they have noise, they're imperfect, they don't have many qubits. So we have to understand what we can do with these quantum computers today. Okay, this is science, but also technology working together to solve relevant problems. So at this moment is not clear what we can do with present quantum computers but we also know what we can do with a perfect quantum computer without noise with many quantum bits, with many qubits. And for instance, then we can solve problems that are out of reach for our classical computers. So the typical example is the problem of factorization that is very connected to what Vanessa does in her company. So we have identified problems that can be solved more efficiently with a quantum computer, with a very good quantum computer. People are working to have this very good quantum computer. At the moment, we have some imperfect quantum computers, we have to understand what we can do with these imperfect machines. >> Okay. So for the first wave was, okay, we have it working for a little while so we see the potential. Okay, and we have enough evidence almost like a little experiment. And now it's apply it to actually do some real work. >> Yeah, so now there is interest by companies so because they see a potential there. So they are investing and they're working together with scientists. We have to identify use cases, problems of relevance for all of us. And then once you identify a problem where a quantum computer can help you, try to solve it with existing machines and see if you can get an advantage. So now the community is really obsessed with getting a quantum advantage. So we really hope that we will get a quantum advantage. This, we know we will get it. We eventually have a very good quantum computer. But we want to have it now. And we're working on that. We have some results, there were I would say a bit academic situation in which a quantum advantage was proven. But to be honest with you on a really practical problem, this has not happened yet. But I believe the day that this happens and I mean it will be really a game changing. >> So you mentioned the word efficiency and you talked about the quantum advantage. Is the quantum advantage a qualitative advantage in that it is fundamentally different? Or is it simply a question of greater efficiency, so therefore a quantitative advantage? The example in the world we're used to, think about a card system where you're writing information on a card and putting it into a filing cabinet and then you want to retrieve it. Well, the information's all there, you can retrieve it. Computer system accelerates that process. It's not doing something that is fundamentally different unless you accept that the speed with which these things can be done gives it a separate quality. So how would you characterize that quantum versus non quantum? Is it just so much horse power changes the game or is it fundamentally different? >> Okay, so from a fundamental perspective, quantum physics is qualitatively different from classical physics. I mean, this year the Nobel Prize was given to three experimentalists who made experiments that proved that quantum physics is qualitatively different from classical physics. This is established, I mean, there have been experiments proving that. Now when we discuss about quantum computation, it's more a quantitative difference. So we have problems that you can solve, in principle you can solve with the classical computers but maybe the amount of time you need to solve them is we are talking about centuries and not with your laptop even with a classic super computer, these machines that are huge, where you have a building full of computers there are some problems for which computers take centuries to solve them. So you can say that it's quantitative, but in practice you may even say that it's impossible in practice and it will remain impossible. And now these problems become feasible with a quantum computer. So it's quantitative but almost qualitative I would say. >> Before we get into the problems, 'cause I want to understand some of those examples, but Vanessa, so your role at LuxQuanta is you're applying quantum in the communication sector for security purposes, correct? >> Vanessa: Correct. >> Because everybody talks about how quantum's going to ruin our lives in terms of taking all our passwords and figuring everything out. But can quantum help us defend against quantum and is that what you do? >> That's what we do. So one of the things that Antonio's explaining so our quantum computer will be able to solve in a reasonable amount of time something that today is impossible to solve unless you leave a laptop or super computer working for years. So one of those things is cryptography. So at the end, when use send a message and you want to preserve its confidentiality what you do is you destroy it but following certain rules which means they're using some kind of key and therefore you can send it through a public network which is the case for every communication that we have, we go through the internet and then the receiver is going to be able to reassemble it because they have that private key and nobody else has. So that private key is actually made of computational problems or mathematical problems that are very, very hard. We're talking about 40 years time for a super computer today to be able to hack it. However, we do not have the guarantee that there is already very smart mind that already have potentially the capacity also of a quantum computer even with enough, no millions, but maybe just a few qubits, it's enough to actually hack this cryptography. And there is also the fear that somebody could actually waiting for quantum computing to finally reach out this amazing capacity we harvesting now which means capturing all this confidential information storage in it. So when we are ready to have the power to unlock it and hack it and see what's behind. So we are talking about information as delicate as governmental, citizens information related to health for example, you name it. So what we do is we build a key to encrypt the information but it's not relying on a mathematical problem it's relying on the laws of quantum physics. So I'm going to have a channel that I'm going to pump photons there, light particles of light. And that quantum channel, because of the laws of physics is going to allow to detect somebody trying to sneak in and seeing the key that I'm establishing. If that happens, I will not create a key if it's clean and nobody was there, I'll give you a super key that nobody today or in the future, regardless of their computational power, will be able to hack. >> So it's like super zero trust. >> Super zero trust. >> Okay so but quantum can solve really challenging mathematical problems. If you had a quantum computer could you be a Bitcoin billionaire? >> Not that I know. I think people are, okay, now you move me a bit of my comfort zone. Because I know people have working on that. I don't think there is a lot of progress at least not that I am aware of. Okay, but I mean, in principle you have to understand that our society is based on information and computation. Computers are a key element in our society. And if you have a machine that computes better but much better than our existing machines, this gives you an advantage for many things. I mean, progress is locked by many computational problems we cannot solve. We can want to have better materials better medicines, better drugs. I mean this, you have to solve hard computational problems. If you have machine that gives you machine learning, big data. I mean, if you have a machine that gives you an advantage there, this may be a really real change. I'm not saying that we know how to do these things with a quantum computer. But if we understand how this machine that has been proven more powerful in some context can be adapted to some other context. I mean having a much better computer machine is an advantage. >> When? When are we going to have, you said we don't really have it today, we want it today. Are we five years away, 10 years away? Who's working on this? >> There are already quantum computers are there. It's just that the capacity that they have of right now is the order of a few hundred qubits. So people are, there are already companies harvesting, they're actually the companies that make these computers they're already putting them. People can access to them through the cloud and they can actually run certain algorithms that have been tailor made or translated to the language of a quantum computer to see how that performs there. So some people are already working with them. There is billions of investment across the world being put on different flavors of technologies that can reach to that quantum supremacy that we are talking about. The question though that you're asking is Q day it sounds like doomsday, you know, Q day. So depending on who you talk to, they will give you a different estimation. So some people say, well, 2030 for example but perhaps we could even think that it could be a more aggressive date, maybe 2027. So it is yet to be the final, let's say not that hard deadline but I think that the risk, that it can actually bring is big enough for us to pay attention to this and start preparing for it. So the end times of cryptography that's what quantum is doing is we have a system here that can actually prevent all your communications from being hacked. So if you think also about Q day and you go all the way back. So whatever tools you need to protect yourself from it, you need to deploy them, you need to see how they fit in your organization, evaluate the benefits, learn about it. So that, how close in time does that bring us? Because I believe that the time to start thinking about this is now. >> And it's likely it'll be some type of hybrid that will get us there, hybrid between existing applications. 'Cause you have to rewrite or write new applications and that's going to take some time. But it sounds like you feel like this decade we will see Q day. What probability would you give that? Is it better than 50/50? By 2030 we'll see Q day. >> But I'm optimistic by nature. So yes, I think it's much higher than 50. >> Like how much higher? >> 80, I would say yes. I'm pretty confident. I mean, but what I want to say also usually when I think there is a message here so you have your laptop, okay, in the past I had a Spectrum This is very small computer, it was more or less the same size but this machine is much more powerful. Why? Because we put information on smaller scales. So we always put information in smaller and smaller scale. This is why here you have for the same size, you have much more information because you put on smaller scales. So if you go small and small and small, you'll find the quantum word. So this is unavoidable. So our information devices are going to meet the quantum world and they're going to exploit it. I'm fully convinced about this, maybe not for the quantum computer we're imagining now but they will find it and they will use quantum effects. And also for cryptography, for me, this is unavoidable. >> And you brought the point there are several companies working on that. I mean, I can get quantum computers on in the cloud and Amazon and other suppliers. IBM of course is. >> The underlying technology, there are competing versions of how you actually create these qubits. pins of electrons and all sorts of different things. Does it need to be super cooled or not? >> Vanessa: There we go. >> At a fundamental stage we'd be getting ground. But what is, what does ChatGPT look like when it can leverage the quantum realm? >> Well, okay. >> I Mean are we all out of jobs at that point? Should we all just be planning for? >> No. >> Not you. >> I think all of us real estate in Portugal, should we all be looking? >> No, actually, I mean in machine learning there are some hopes about quantum competition because usually you have to deal with lots of data. And we know that in quantum physics you have a concept that is called superposition. So we, there are some hopes not in concrete yet but we have some hopes that these superpositions may allow you to explore this big data in a more efficient way. One has to if this can be confirmed. But one of the hopes creating this lots of qubits in this superpositions that you will have better artificial intelligence machines but, okay, this is quite science fiction what I'm saying now. >> At this point and when you say superposition, that's in contrast to the ones and zeros that we're used to. So when someone says it could be a one or zero or a one and a zero, that's referencing the concept of superposition. And so if this is great for encryption, doesn't that necessarily mean that bad actors can leverage it in a way that is now unhackable? >> I mean our technologies, again it's impossible to hack because it is the laws of physics what are allowing me to detect an intruder. So that's the beauty of it. It's not something that you're going to have to replace in the future because there will be a triple quantum computer, it is not going to affect us in any way but definitely the more capacity, computational capacity that we see out there in quantum computers in particular but in any other technologies in general, I mean, when we were coming to talk to you guys, Antonio and I, he was the one saying we do not know whether somebody has reached some relevant computational power already with the technologies that we have. And they've been able to hack already current cryptography and then they're not telling us. So it's a bit of, the message is a little bit like a paranoid message, but if you think about security that the amount of millions that means for a private institution know when there is a data breach, we see it every day. And also the amount of information that is relevant for the wellbeing of a country. Can you really put a reasonable amount of paranoid to that? Because I believe that it's worth exploring whatever tool is going to prevent you from putting any of those piece of information at risk. >> Super interesting topic guys. I know you're got to run. Thanks for stopping by theCUBE, it was great to have you on. >> Thank you guys. >> All right, so this is the SiliconANGLE theCUBE's coverage of Mobile World Congress, MWC now 23. We're live at the Fira Check out silicon SiliconANGLE.com and theCUBE.net for all the videos. Be right back, right after this short break. (relaxing music)

>>Welcome back, everyone live here at Supercomputing 22 in Dallas, Texas. I'm John for host of the Queue here at Paul Gillin, editor of Silicon Angle, getting all the stories, bringing it to you live. Supercomputer TV is the queue right now. And bringing all the action Bresniker, chief architect of Hewlett Packard Labs with HP Cube alumnis here to talk about Supercomputing Road to Quantum. Kirk, great to see you. Thanks for coming on. >>Thanks for having me guys. Great to be >>Here. So Paul and I were talking and we've been covering, you know, computing as we get into the large scale cloud now on premises compute has been one of those things that just never stops. No one ever, I never heard someone say, I wanna run my application or workload on slower, slower hardware or processor or horsepower. Computing continues to go, but this, we're at a step function. It feels like we're at a level where we're gonna unleash new, new creativity, new use cases. You've been kind of working on this for many, many years at hp, Hewlett Packard Labs, I remember the machine and all the predecessor r and d. Where are we right now from your standpoint, HPE standpoint? Where are you in the computing? It's as a service, everything's changing. What's your view? >>So I think, you know, you capture so well. You think of the capabilities that you create. You create these systems and you engineer these amazing products and then you think, whew, it doesn't get any better than that. And then you remind yourself as an engineer. But wait, actually it has to, right? It has to because we need to continuously provide that next generation of scientists and engineer and artists and leader with the, with the tools that can do more and do more frankly with less. Because while we want want to run the program slower, we sure do wanna run them for less energy. And figuring out how we accomplish all of those things, I think is, is really where it's gonna be fascinating. And, and it's also, we think about that, we think about that now, scale data center billion, billion operations per second, the new science, arts and engineering that we'll create. And yet it's also what's beyond what's beyond that data center. How do we hook it up to those fantastic scientific instruments that are capable to generate so much information? We need to understand how we couple all of those things together. So I agree, we are at, at an amazing opportunity to raise the aspirations of the next generation. At the same time we have to think about what's coming next in terms of the technology. Is the silicon the only answer for us to continue to advance? >>You know, one of the big conversations is like refactoring, replatforming, we have a booth behind us that's doing energy. You can build it in data centers for compute. There's all kinds of new things. Is there anything in the paradigm of computing and now on the road to quantum, which I know you're involved, I saw you have on LinkedIn, you have an open rec for that. What paradigm elements are changing that weren't in play a few years ago that you're looking at right now as you look at the 20 mile stair into quantum? >>So I think for us it's fascinating because we've had a tailwind at our backs my whole career, 33 years at hp. And what I could count on was transistors got at first they got cheaper, faster and they use less energy. And then, you know, that slowed down a little bit. Now they're still cheaper and faster. As we look in that and that Moore's law continues to flatten out of it, there has to be something better to do than, you know, yet another copy of the prior design opening up that diversity of approach. And whether that is the amazing wafer scale accelerators, we see these application specific silicon and then broadening out even farther next to the next to the silicon. Here's the analog computational accelerator here is now the, the emergence of a potential quantum accelerator. So seeing that diversity of approaches, but what we have to happen is we need to harness all of those efficiencies and yet we still have to realize that there are human beings that need to create the application. So how do we bridge, how do we accommodate the physical of, of new kinds of accelerator? How do we imagine the cyber physical connection to the, to the rest of the supercomputer? And then finally, how do we bridge that productivity gap? Especially not for people who like me who have been around for a long time, we wanna think about that next generation cuz they're the ones that need to solve the problems and write the code that will do it. >>You mentioned what exists beyond silicon. In fact, are you looking at different kinds of materials that computers in the future will be built upon? >>Oh absolutely. You think of when, when we, we look at the quantum, the quantum modalities then, you know, whether it is a trapped ion or a superconducting, a piece of silicon or it is a neutral ion. There's just no, there's about half a dozen of these novel systems because really what we're doing when we're using a a quantum mechanical computer, we're creating a tiny universe. We're putting a little bit of material in there and we're manipulating at, at the subatomic level, harnessing the power of of, of quantum physics. That's an incredible challenge. And it will take novel materials, novel capabilities that we aren't just used to seeing. Not many people have a helium supplier in their data center today, but some of them might tomorrow. And understanding again, how do we incorporate industrialize and then scale all of these technologies. >>I wanna talk Turkey about quantum because we've been talking for, for five years. We've heard a lot of hyperbole about quantum. We've seen some of your competitors announcing quantum computers in the cloud. I don't know who's using these, these computers, what kind of work they're being used, how much of the, how real is quantum today? How close are we to having workable true quantum computers and what can you point to any examples of how it's being, how that technology is being used in the >>Field? So it, it remains nascent. We'll put it that way. I think part of the challenge is we see this low level technology and of course it was, you know, professor Richard Fineman who first pointed us in this direction, you know, more than 30 years ago. And you know, I I I trust his judgment. Yes. You know that there's probably some there there especially for what he was doing, which is how do we understand and engineer systems at the quantum mechanical level. Well he said a quantum mechanical system's probably the way to go. So understanding that, but still part of the challenge we see is that people have been working on the low level technology and they're reaching up to wondering will I eventually have a problem that that I can solve? And the challenge is you can improve something every single day and if you don't know where the bar is, then you don't ever know if you'll be good enough. >>I think part of the approach that we like to understand, can we start with the problem, the thing that we actually want to solve and then figure out what is the bespoke combination of classical supercomputing, advanced AI accelerators, novel quantum quantum capabilities. Can we simulate and design that? And we think there's probably nothing better to do that than than an next to scale supercomputer. Yeah. Can we simulate and design that bespoke environment, create that digital twin of this environment and if we, we've simulated it, we've designed it, we can analyze it, see is it actually advantageous? Cuz if it's not, then we probably should go back to the drawing board. And then finally that then becomes the way in which we actually run the quantum mechanical system in this hybrid environment. >>So it's na and you guys are feeling your way through, you get some moonshot, you work backwards from use cases as a, as a more of a discovery navigational kind of mission piece. I get that. And Exoscale has been a great role for you guys. Congratulations. Has there been strides though in quantum this year? Can you point to what's been the, has the needle moved a little bit a lot or, I mean it's moving I guess to some, there's been some talk but we haven't really been able to put our finger on what's moving, like what need, where's the needle moved I >>Guess in quantum. And I think, I think that's part of the conversation that we need to have is how do we measure ourselves. I know at the World Economic Forum, quantum Development Network, we had one of our global future councils on the future of quantum computing. And I brought in a scene I EEE fellow Par Gini who, you know, created the international technology roadmap for semiconductors. And I said, Paulo, could you come in and and give us examples, how was the semiconductor community so effective not only at developing the technology but predicting the development of technology so that whether it's an individual deciding if they should change careers or it's a nation state deciding if they should spend a couple billion dollars, we have that tool to predict the rate of change and improvement. And so I think that's part of what we're hoping by participating will bring some of that road mapping skill and technology and understanding so we can make those better reasoned investments. >>Well it's also fun to see super computing this year. Look at the bigger picture, obviously software cloud natives running modern applications, infrastructure as code that's happening. You're starting to see the integration of, of environments almost like a global distributed operating system. That's the way I call it. Silicon and advancements have been a big part of what we see now. Merchant silicon, but also dpu are on the scene. So the role role of silicon is there. And also we have supply chain problems. So how, how do you look at that as a a, a chief architect of h Hewlett Packard Labs? Because not only you have to invent the future and dream it up, but you gotta deal with the realities and you get the realities are silicon's great, we need more of that quantums around the corner, but supply chain, how do you solve that? What's your thoughts and how do you, how, how is HPE looking at silicon innovation and, and supply chain? >>And so for us it, it is really understanding that partnership model and understanding and contributing. And so I will do things like I happen to be the, the systems and architectures chapter editor for the I eee International Roadmap for devices and systems, that community that wants to come together and provide that guidance. You know, so I'm all about telling the semiconductor and the post semiconductor community, okay, this is where we need to compute. I have a partner in the applications and benchmark that says, this is what we need to compute. And when you can predict in the future about where you need to compute, what you need to compute, you can have a much richer set of conversations because you described it so well. And I think our, our senior fellow Nick Dubey would, he's coined the term internet of workflows where, you know, you need to harness everything from the edge device all the way through the extra scale computer and beyond. And it's not just one sort of static thing. It is a very interesting fluid topology. I'll use this compute at the edge, I'll do this information in the cloud, I want to have this in my exoscale data center and I still need to provide the tool so that an individual who's making that decision can craft that work flow across all of those different resources. >>And those workflows, by the way, are complicated. Now you got services being turned on and off. Observability is a hot area. You got a lot more data in in cycle inflow. I mean a lot more action. >>And I think you just hit on another key point for us and part of our research at labs, I have, as part of my other assignments, I help draft our AI ethics global policies and principles and not only tell getting advice about, about how we should live our lives, it also became the basis for our AI research lab at Shewl Packard Labs because they saw, here's a challenge and here's something where I can't actually believe, maintain my ethical compliance. I need to have engineer new ways of, of achieving artificial intelligence. And so much of that comes back to governance over that data and how can we actually create those governance systems and and do that out in the open >>That's a can of worms. We're gonna do a whole segment on that one, >>On that >>Technology, on that one >>Piece I wanna ask you, I mean, where rubber meets the road is where you're putting your dollars. So you've talked a lot, a lot of, a lot of areas of, of progress right now, where are you putting your dollars right now at Hewlett Packard Labs? >>Yeah, so I think when I draw, when I draw my 2030 vision slide, you know, I, for me the first column is about heterogeneous, right? How do we bring all of these novel computational approaches to be able to demonstrate their effectiveness, their sustainability, and also the productivity that we can drive from, from, from them. So that's my first column. My section column is that edge to exoscale workflow that I need to be able to harness all of those computational and data resources. I need to be aware of the energy consequence of moving data, of doing computation and find all of that while still maintaining and solving for security and privacy. But the last thing, and, and that's one was a, one was a how one was aware. The last thing is a who, right? And is is how do we take that subject matter expert? I think of a, a young engineer starting their career at hpe. It'll be very different than my 33 years. And part of it, you know, they will be undaunted by any, any scale. They will be cloud natives, maybe they metaverse natives, they will demand to design an open cooperative environment. So for me it's thinking about that individual and how do I take those capabilities, heterogeneous edge to exito scale workflows and then make them productive. And for me, that's, that's where we were putting our emphasis on those three. When, where and >>Who. Yeah. And making it compatible for the next generation. We see the student cluster competition going on over there. This is the only show that we cover that we've been to that is from the dorm room to the boardroom and this cuz Supercomputing now is elevating up into that workflow, into integration, multiple environments, cloud, premise, edge, metaverse. This is like a whole nother world. >>And, and, but I think it's, it's the way that regardless of which human pursuit you're in, you know, everyone is going to be demand simulation and modeling ai, ML and massive data m l and massive data analytics that's gonna be at heart of, of everything. And that's what you see. That's what I love about coming here. This isn't just the way we're gonna do science. This is the way we're gonna do everything. >>We're gonna come by your booth, check it out. We've talked to some of the folks, hpe obviously HPE Discover this year, GreenLake with center stage, it's now consumption is a service for technology. Whole nother ballgame. Congratulations on, on all this. I would say the massive, I won't say pivot, but you know, a change >>It >>Is and how you guys >>Operate. And you know, it's funny sometimes you think about the, the pivot to as a services benefiting the customer, but as someone who has supported designs over decades, you know, that ability to to to operate and at peak efficiency, to always keep in perfect operating order and to continuously change while still meeting the customer expectations that actually allows us to deliver innovation to our customers faster than when we are delivering warranted individual packaged products. >>Kirk, thanks for coming on Paul. Great conversation here. You know, the road to Quantum's gonna be paved through computing supercomputing software integrated workflows from the dorm room to the boardroom to Cube, bringing all the action here at Supercomputing 22. I'm Jacque Forer with Paul Gillin. Thanks for watching. We'll be right back.

>>How do y'all and welcome back to the cube where we're live from Dallas at a Supercomputing 2022. My name is Savannah Peterson. Joined with L AED today, as well as some very exciting guests talking about one of my favorite and most complex topics out there, talking about quantum a bit today. Please welcome Ken and Matthew. Thank you so much for reading here. Matthew. Everyone's gonna be able to see your shirt. What's going on with hybrid quantum? I have >>To ask. Wait, what is hybrid quantum? Yeah, let's not pretend that. >>Let's not >>Pretend that everybody knows, Everyone already knows what quantum computing is if we goes straight to highway. Yeah. Okay. So with the brief tour detour took qu regular quantum computing. Yeah, >>No, no. Yeah. Let's start with quantum start before. >>So you know, like regular computers made of transistors gives us ones and zeros, right? Binary, like you were talking about just like half of the Cheerios, right? The joke, it turns out there's some problems that even if we could build a computer as big as the whole universe, which would be pretty expensive, >>That might not be a bad thing, but >>Yeah. Yeah. Good for Dell Got mill. >>Yeah. >>Yeah. We wouldn't be able to solve them cuz they scale exponentially. And it turns out some of those problems have efficient solutions in quantum computing where we take any two state quantum system, which I'll explain in a sec and turn it into what we call a quantum bit or qubit. And those qubits can actually solve some problems that are just infeasible on even these world's largest computers by offering exponential advantage. And it turns out that today's quantum computers are a little too small and a little too noisy to do that alone. So by pairing a quantum computer with a classical computer, hence the partnership between IQ and Dell, you allow each kind of compute to do what it's best at and thereby get answers you can't get with either one alone. >>Okay. So the concept of introducing hybridity, I love that word bridge. I dunno if I made it up, but it's it for it. Let's about it. Abri, ding ding. So does this include simulating the quantum world within the, what was the opposite? The classical quantum world? Classical. Classical, classical computer. Yeah. So does it include the concept of simulating quantum in classical compute? >>Absolutely. >>Okay. How, how, how do, how do you do that? >>So there's simulators and emulators that effectively are programmed in exactly the same way that a physical quantum machine is through circuits translated into chasm or quantum assembly language. And those are the exact same ways that you would program either a physical q p or a simulated >>Q p. So, so access to quantum computing today is scarce, right? I mean it's, it's, it's, it's limited. So having the ability to have the world at large or a greater segment of society be able to access this through simulation is probably a good idea. >>Fair. It's absolutely a wonderful one. And so I often talk to customers and I tell them about the journey, which is hands on keyboard, learning, experimentation, building proof of concepts, and then finally productization. And you could do much of that first two steps anyway very robustly with simulation. >>It's much like classical computing where if you imagine back in the fifties, if, if the cube was at some conference in 1955, you know, we wouldn't have possibly been able to predict what we'd be doing with computing 70 years later, right? Yeah. That teenagers be making apps on their phones that changed the world, right? And so by democratizing access this way, suddenly we can open up all sorts of new use cases. We sort of like to joke, there's only a couple hundred people in the world who really know how to program quantum computers today. And so how are we gonna make thousands, tens of thousands, millions of quantum programmers? The answer is access and simulators are an amazingly accessible way for everyone to start playing around with the >>Fields. Very powerful tool. >>Wow. Yeah. I'm just thinking about how many, there's, are there really only hundreds of people who can program quantum computing? >>I kind of generally throw it out there and I say, you know, if you looked at a matrix of a thousand operations with hundreds of qubits, there's probably, I don't know, 2000 people worldwide that could program that type of a circuit. I mean it's a fairly complex circuit at that point and >>I, I mean it's pretty phenomenal When you think about how early we are in adoption and, and the rollout of this technology as a whole, can you see quite a bit as, as you look across your customer portfolio, what are some of the other trends you're seeing? >>Well, non quantum related trends or just any type you give us >>Both. >>Yeah. So >>We're a thought leader. This is >>Your moment. Yeah, so we do quite a bit. We see quite a bit actually. There's a lot of work happening at the edge as you're probably well aware of. And we see a lot of autonomous mobile robots. I actually lead the, the research office. So I get to see all the cool stuff that's really kind of emerging before it really regrets >>What's coming next. >>Let's see, Oh, I can't tell you what's coming next, but we see edge applications. Yes, we see a lot of, of AI applications and artificial intelligence is morphing dramatically through the number of frameworks and through the, the types and places you would place ai, even places I, I personally never thought we would go like manufacturing environments. Some places that were traditionally not very early adopters. We're seeing AI move very quickly in some of those areas. One of the areas that I'm really excited about is digital twins and the ability to eventually do, let's come up on acceleration with quantum technologies on, on things like computational fluid dynamics. And I think it's gonna be a wonderful, wonderful area for us moving forward. >>So, So I can hear the people screaming at the screen right now. Wait a minute, You said it was hybrid, you're only talking the front half. That's, that's cat. What about the back half? That's dog. What about the quantum part of it? So I, on Q and, and I apologize. Ion Q >>Ion >>Q, Yeah Ion Q cuz you never know. You never never know. Yeah. Where does the actual quantum come in? >>That's a great >>Question. So you guys have one of these things. >>Yeah, we've built, we currently have the world's best quantum computer by, by sub measures I drop there. Yeah, no big deal. Give me some snaps for that. Yeah, Ken knows how to pick em. Yeah, so right. Our, our approach, which is actually based on technology that's 50 years old, so it's quite, quite has a long history. The way we build atomic clocks is the basis for trapped eye quantum computing. And in fact the first quantum logic gate ever made in 1995 was at NIST where they modified their atomic clock experiment to do quantum gates. And that launched really the hardware experimentalist quantum Peter Revolution. And that was by Chris Monroe, our co-founder. So you know that history has flown directly into us. So to simplify, we start with an ion trap. Imagine a gold block with a bunch of electrodes that allow you to make precisely shaped electromagnetic fields, sort of like a rotating saddle. >>Then take a source of atoms. Now obviously we're all sources of atoms. We have a highly purified source of metal atium. We heat it up, we get a nice hot plume of atoms, we ionize those atoms with an ionizing later laser. Now they're hot and heavy and charged. So we can trap them in one of these fields. And now our electromagnetic field that's spitting rapidly holds the, the ions like balls in a bowl if you can imagine them. And they line up in a nice straight line and we hold them in place with these fields and with cooling laser beams. And up to now, that's how an atomic clock works. Trap an item and shine it with a laser beam. Count the oscillations, that's your clock. Now if you got 32 of those and you can manipulate their energy states, in our case we use the hyper fine energy states of the atom. >>But you can basically think of your high school chemistry where you have like an unexcited electron, an excited electron. Take your unexcited state as a zero, your excited state as a one. And it turns out with commercially available lasers, you can drive anywhere between a zero, a one or a super position of zero and one. And so that is our quantum bit, the hyper fine energy state of the atrium atom. And we just line up a bunch of them and through there access the magical powers of supervision entanglement, as we were talking about before, they don't really make sense to us here in the regular world, but >>They do exist. But what you just described is one cubit. That's right. And the way that you do it isn't exactly the same way that others who are doing quantum computing do it. That's right. Is that okay? >>And there's a lot of advantages to the trapped iron approach. So for example, you can also build a super conducting qubit where you, where you basically cool a chip to 47 mil kelvin and coerce millions of atoms to work together as a single system. The problem is that's not naturally quantum. So it's inherently noisy and it wants to deco here does not want to be a quantum bit. Whereas an atom is very happy to be by itself a qubit because we don't have to do anything to it. It's naturally quantum, if that makes sense. And so atomic qubits, like we use feature a few things. One the longest coherence times in the industry, meaning you can run very deep circuits, the most accurate operations, very low noise operations. And we don't have any wires. Our atoms are connected by laser light. That means you can connect any pair. So with some other technologies, the qubits are connected by wires. That means you can only run operations between physically connected qubits. It's like programming. If you could only use, for example, bits that are adjacent with an i untrapped approach, you can connect any pair so that all to all connectivity means your compilation is much more efficient and you can do much wider and deeper circuits. >>So what's the, what is the closest thing to a practical application that we've been able to achieve at this point? Question. And when I say practical, it doesn't have to be super practical. I mean, what is the, what is the sort of demonstration, the least esoteric demonstration of this at this point? >>To tie into what Ken was saying earlier, I think there's at least two areas that are very exciting. One is chemistry. Chemistry. So for example, you know, we have water in our cup and we understand water pretty well, but there's lots of molecules that in order to study them, we actually have to make them in a lab and do lots of experiments. And to give you a sense of the order of magnitude, if you wanted to understand the ground state of the caffeine molecule, which we all know and has 200 electrons, you would need to build a computer bigger than the moon. So, which is, you know, again, would be good profit for Dell, but probably not gonna happen time soon. That's >>Kind of fun to think about though. Yeah, that's a great analogy. That >>Was, yeah. And in fact it'd be like 10 moons of compute. Okay. So build 10 moons of >>Computer. I >>Love the sci-fi issue. Exactly. And now you can calculate caffeine, it's crazy or it just fits in a quantum computer the size of this table. And so we're using hybrid quantum computing now to start proving out these algorithms not for molecules as complex as caffeine or what we want in the future. Like biologics, you know, new cancer medications, new materials and so forth. But we are able to show, for example, the ground state of smaller molecules and prove a path to where, you know, decision maker could see in a few years from now, Oh, we'll be able to actually simulate not molecules we already understand, but molecules we've never been able to study a prayer, if that makes sense. And then, >>Yeah, I think there's a key point underneath that, and I think goes back to the question that you asked earlier about the why hybrid applications inherently run on the classical infrastructure and algorithms are accelerated through qs, the quantum processing units. >>And so are you sort of time sharing in the sense that this environment that you set up starts with classical, with simulation and then you get to a point where you say, okay, we're ready, you pick up the bat phone and you say I wanna, >>I would say it's more like a partnership, really. Yeah, >>Yeah. And I think, I think it's kind of the, the way I normally describe it is, you know, we've taken a look at it it from a really kind of a software development life cycle type of perspective where again, if you follow that learn experiment, pro proof of concept, and then finally productize, we, we can cover and allow for a developer to start prototyping and proofing on simulators and when they're ready all they do is flip a switch and a manifest and they can automatically engage a qu a real quantum physical quantum system. And so we've made it super simple and very accessible in a democratizing access for developers. >>Yeah. Makes such big difference. Go ahead. >>A good analogy is to like GPUs, right? Where it's not really like, you know, you send it away, but rather the GPU accelerates certain operations. The q p. Yeah, because quantum mechanics, it turns out the universe runs on linear algebra. So one way to think about the q p is the most efficient way of doing linear algebra that exists. So lots of problems that can be expressed in that form. Combinatorial optimization problems in general, certain kinds of machine learning, et cetera, get an exponential speed up by running a section of the algorithm on the quantum computer. But of course you wouldn't like port Microsoft Word. Yeah, exactly. You know, you're not gonna do that in your product. It would be a waste of your quantum computer. >>Not just that you wanna know exactly how much money is in your bank account, not probabilistically how much might be ballpark. Yeah. Realm 10, moon ballpark, right? >>10 moon ballpark. Be using that for the rest of the show. Yeah. Oh, I love that. Ken, tell me a little bit about how you identify companies and like I n Q and and end up working with Matthew. What, what's that like, >>What's it like or how do you >>Find it's the process? Like, so, you know, let's say I've got the the >>We're not going there though. Yeah. We're not >>Personal relationship. >>Well, >>You can answer these questions however you want, you know. No, but, but what does that look like for Dell? How do you, how do you curate and figure out who you're gonna bring into this partnership nest? >>Yeah, you know, I, I think it was a, it's, it was a, a very long drawn out learning opportunity. We started actually our working quantum back in 2016. So we've been at it for a long time. And only >>In quantum would we say six years is a long time. I love >>That. Exactly. >>By the way, that was like, we've been doing this for age for a >>Long time. Yeah. Very long time before >>You were born. Yes. >>Feels like it actually, believe it or not. But, so we've been at it for a long time and you know, we went down some very specific learning paths. We took a lot of different time to, to learn about different types of qubits available, different companies, what their approaches were, et cetera. Yeah. And, and we ended up meeting up with, with I N Q and, and we also have other partners as well, like ibm, but I N q you know, we, there is a nice symbiotic relationship. We're actually doing some really cool technologies that are even much, much further ahead than the, you know, strict classical does this, quantum does that where there's significant amount of interplay between the simulation systems and between the real physical QS. And so it's, it's turning out to be a great relationship. They're, they're very easy to work with and, and a lot of fun too, as you could probably tell. Yeah. >>Clearly. So before we wrap, I've got it. Okay. Okay. So get it. Let's get, let's get, yeah, let's get deep. Let's get deep for a second or a little deeper than we've been. So our current, our current understanding of all this, of the universe, it's pretty limited. It's down to the point where we effectively have it assigned to witchcraft. It's all dark energy and dark matter. Right. What does that mean exactly? Nobody knows. But if you're in the quantum computing space and you're living this every day, do you believe that it represents the key to us understanding things that currently we just can't understand classical models, including classical computing, our brains as they're constructed aren't capable of understanding the real real that's out there. Yeah. If you're in the quantum computing space, do you possess that level of hubris? Do you think that you are gonna deliver the answers? >>I'm just like, I think the more you're in the space, the more mysterious and amazing it all seems. There's a, but there is a great quote by Richard Feinman that sort of kicked off the quantum exploration. So he gave a lecture in 1981, so, you know, long before any of this began, truly ages ago, right? Yeah. And in this lecture he said, you know, kind of wild at that time, right? We had to build these giant supercomputers to simulate just a couple atoms interacting, right? And it's kind of crazy that you need all this compute to simulate what nature does with just a handful >>Particles. Yeah. >>Really small. So, and, and famously he said, you know, nature just isn't classical. Damn it. And so you need to build a computer that works with nature to understand nature. I think, you know, the, the quantum revolution has only just begun. There's so many new things to learn, and I'm sure the quantum computers of 40 years from now are not gonna look like the, you know, the computers of today, just as the classical computers of 40 years ago look quite different to us now, >>And we're a bunch of apes. But you think we'll get there? >>I, yeah, I, I mean, I, I have, I think we have, I feel incredibly optimistic that this tool, quantum computing as a tool represents a sea change in what's possible for humans to compute. >>Yeah. I think it's that possibility. You know, I, when I tell people right now in the quantum era, we're in the inac stage of the quantum era, and so we have a long way to go, but the potential is absolutely enormous. In fact, incomprehensibly enormous, I >>Was just gonna say, I don't even think we could grasp >>In the, from the inac is they had no idea of computers inside of your hand, right? Yeah. >>They're calculating, you know, trajectories, right? Yeah. If you told them, like, we'd all be video chatting, you >>Know, >>Like, and kids would be doing synchronized dances, you know, you'd be like, What? >>I love that. Well, well, on that note, Ken Matthew, really great to have you both, everyone now will be pondering the scale and scope of the universe with their 10 moon computer, 10 moons. That's right. And, and you've given me my, my new favorite bumper sticker since we've been on a, on a roll here, David and I, which is just naturally quantum. Yeah, that's, that's, that's, that's one of my new favorite phrases from the show. Thank you both for being here. David, thank you for hanging out and thank all of you for tuning in to our cube footage live here in Dallas. We are at Supercomputing. This is our last show for the day, but we look forward to seeing you tomorrow morning. My name's Savannah Peterson. Y'all have a lovely night.

>>mhm Yes. >>Welcome back to the Cube coverage of Red Hat summit 21 2021. I'm john for host of the Cubans virtual this year as we start preparing to come out of Covid a lot of great conversations here happening around technology. This is the emerging technology with Red hat segment. We've got three great guests steve watt manager, distinguished engineer at Red Hat hurl saying senior software engineer Red Hat and luke Hines, who's the senior software engineer as well. We got the engineering team steve, you're the the team leader, emerging tech within red hat. Always something to talk about. You guys have great tech chops that's well known in the industry and I'll see now part of IBM you've got a deep bench um what's your, how do you view emerging tech um how do you apply it? How do you prioritize, give us a quick overview of the emerging tech scene at Redhead? >>Yeah, sure. It's quite a conflated term. The way we define emerging technologies is that it's a technology that's typically 18 months plus out from commercialization and this can sometimes go six months either way. Another thing about it is it's typically not something on any of our product roadmaps within the portfolio. So in some sense, it's often a bit of a surprise that we have to react to. >>So no real agenda. And I mean you have some business unit kind of probably uh but you have to have first principles within red hat, but for this you're looking at kind of the moon shot, so to speak, the big game changing shifts. Quantum, you know, you got now supply chain from everything from new economics, new technology because that kind of getting it right. >>Yeah, I think we we definitely use a couple of different techniques to prioritize and filter what we're doing. And the first is something will pop up and it will be like, is it in our addressable market? So our addressable market is that we're a platform software company that builds enterprise software and so, you know, it's got to be sort of fit into that is a great example if somebody came up came to us with an idea for like a drone command center, which is a military application, it is an emerging technology, but it's something that we would pass on. >>Yeah, I mean I didn't make sense, but he also, what's interesting is that you guys have an open source D N A. So it's you have also a huge commercial impact and again, open sources of one of the 4th, 5th generation of awesomeness. So, you know, the good news is open source is well proven. But as you start getting into this more disruption, you've got the confluence of, you know, core cloud, cloud Native, industrial and IOT edge and data. All this is interesting, right. This is where the action is. How do you guys bring that open source community participation? You got more stakeholders emerging there before the break down, how that you guys manage all that complexity? >>Yeah, sure. So I think that the way I would start is that, you know, we like to act on good ideas, but I don't think good ideas come from any one place. And so we typically organize our teams around sort of horizontal technology sectors. So you've got, you know, luke who's heading up security, but I have an edge team, cloud networking team, a cloud storage team. Cloud application platforms team. So we've got these sort of different areas that we sort of attack work and opportunities, but you know, the good ideas can come from a variety of different places. So we try and leverage co creation with our customers and our partners. So as a good example of something we had to react to a few years ago, it was K Native right? So the sort of a new way of doing service um and eventing on top of kubernetes that was originated from google. Whereas if you look at Quantum right, ibms, the actual driver on quantum science and uh that originated from IBM were parole. We'll talk about exactly how we chose to respond to that. Some things are originated organically within the team. So uh luke talking about six law is a great example of that, but we do have a we sort of use the addressable market as a way to sort of focus what we're doing and then we try and land it within our different emerging technologies teams to go tackle it. Now. You asked about open source communities, which are quite interesting. Um so typically when you look at an open source project, it's it's there to tackle a particular problem or opportunity. Sometimes what you actually need commercial vendors to do is when there's a problem or opportunity that's not tackled by anyone open source project, we have to put them together to create a solution to go tackle that thing. That's also what we do. And so we sort of create this bridge between red hat and our customers and multiple different open source projects. And this is something we have to do because sometimes just that one open source project doesn't really care that much about that particular problem. They're motivated elsewhere. And so we sort of create that bridge. >>We got two great uh cohorts here and colleagues parole on the on the Quantum side and you got luke on the security side. Pro I'll start with you. Quantum is also a huge mentioned IBM great leadership there. Um Quantum on open shift. I mean come on. Just that's not coming together for me in my mind, it's not the first thing I think of. But it really that sounds compelling. Take us through, you know, um how this changes the computing landscape because heterogeneous systems is what we want and that's the world we live in. But now with distributed systems and all kinds of new computing modules out there, how does this makes sense? Take us through this? >>Um yeah john's but before I think I want to explain something which is called Quantum supremacy because it plays very important role in the road map that's been working on. So uh content computers, they are evolving and they have been around. But right now you see that they are going to be the next thing. And we define quantum supremacy as let's say you have any program that you run or any problems that you solve on a classical computer. Quantum computer would be giving you the results faster. So that is uh, that is how we define content supremacy when the same workload are doing better on content computer than they do in a classical computer. So the whole the whole drive is all the applications are all the companies, they're trying to find avenues where Quantum supremacy are going to change how they solve problems or how they run their applications. And even though quantum computers they are there. But uh, it is not as easily accessible for everyone to consume because it's it's a very new area that's being formed. So what, what we were thinking, how we can provide a mechanism that you can you don't connect this deal was you have a classical world, you have a country world and that's where a lot of thought process been. And we said okay, so with open shift we have the best of the classical components. You can take open shift, you can develop, deploy around your application in a country raised platform. What about you provide a mechanism that the world clothes that are running on open shift. They are also consuming quantum resources or they are able to run the competition and content computers take the results and integrate them in their normal classical work clothes. So that is the whole uh that was the whole inception that we have and that's what brought us here. So we took an operator based approach and what we are trying to do is establish the best practices that you can have these heterogeneous applications that can have classical components. Talking to our interacting the results are exchanging data with the quantum components. >>So I gotta ask with the rise of containers now, kubernetes at the center of the cloud native value proposition, what work clothes do you see benefiting from the quantum systems the most? Is there uh you guys have any visibility on some of those workloads? >>Uh So again, it's it's a very new, it's very it's really very early in the time and uh we talk with our customers and every customers, they are trying to identify themselves first where uh these contacts supremacy will be playing the role. What we are trying to do is when they reach their we should have a solution that they that they could uh use the existing in front that they have on open shift and use it to consume the content computers that may or may not be uh, inside their own uh, cloud. >>Well I want to come back and ask you some of the impact on the landscape. I want to get the look real quick because you know, I think security quantum break security, potentially some people have been saying, but you guys are also looking at a bunch of projects around supply chain, which is a huge issue when it comes to the landscape, whether its components on a machine in space to actually handling, you know, data on a corporate database. You guys have sig store. What's this about? >>Sure. Yes. So sick store a good way to frame six store is to think of let's encrypt and what let's encrypt did for website encryption is what we plan to do for software signing and transparency. So six Door itself is an umbrella organization that contains various different open source projects that are developed by the Six door community. Now, six door will be brought forth as a public good nonprofit service. So again, we're very much basing this on the successful model of let's Encrypt Six door will will enable developers to sign software artifacts, building materials, containers, binaries, all of these different artifacts that are part of the software supply chain. These can be signed with six door and then these signing events are recorded into a technology that we call a transparency log, which means that anybody can monitor signing events and a transparency log has this nature of being read only and immutable. It's very similar to a Blockchain allows you to have cryptographic proof auditing of our software supply chain and we've made six stores so that it's easy to adopt because traditional cryptographic signing tools are a challenge for a lot of developers to implement in their open source projects. They have to think about how to store the private keys. Do they need specialist hardware? If they were to lose a key then cleaning up afterwards the blast radius. So the key compromise can be incredibly difficult. So six doors role and purpose essentially is to make signing easy easy to adopt my projects. And then they have the protections around there being a public transparency law that could be monitored. >>See this is all about open. Being more open. Makes it more secure. Is the >>thief? Very much yes. Yes. It's that security principle of the more eyes on the code the better. >>So let me just back up, is this an open, you said it's gonna be a nonprofit? >>That's correct. Yes. Yes. So >>all of the code is developed by the community. It's all open source. anybody can look at this code. And then we plan alongside the Linux Foundation to launch a public good service. So this will make it available for anybody to use if your nonprofit free to use service. >>So luke maybe steve if you can way into on this. I mean, this goes back. If you look back at some of the early cloud days, people were really trashing cloud as there's no security. And cloud turns out it's a more security now with cloud uh, given the complexity and scale of it, does that apply the same here? Because I feel this is a similar kind of concept where it's open, but yet the more open it is, the more secure it is. And then and then might have to be a better fit for saying I. T. Security solution because right now everyone is scrambling on the I. T. Side. Um whether it's zero Trust or Endpoint Protection, everyone's kind of trying everything in sight. This is kind of changing the paradigm a little bit on software security. Could you comment on how you see this playing out in traditional enterprises? Because if this plays out like the cloud, open winds, >>so luke, why don't you take that? And then I'll follow up with another lens on it which is the operate first piece. >>Sure. Yes. So I think in a lot of ways this has to be open this technology because this way we have we have transparency. The code can be audited openly. Okay. Our operational procedures can be audit openly and the community can help to develop not only are code but our operational mechanisms so we look to use technology such as cuba netease, open ship operators and so forth. Uh Six store itself runs completely in a cloud. It is it is cloud native. Okay, so it's very much in the paradigm of cloud and yeah, essentially security, always it operates better when it's open, you know, I found that from looking at all aspects of security over the years that I've worked in this realm. >>Okay, so just just to add to that some some other context around Six Law, that's interesting, which is, you know, software secure supply chain, Sixth floor is a solution to help build more secure software secure supply chains, more secure software supply chain. And um so um there's there's a growing community around that and there's an ecosystem of sort of cloud native kubernetes centric approaches for building more secure software. I think we all caught the solar winds attack. It's sort of enterprise software industry is responding sort of as a whole to go and close out as many of those gaps as possible, reduce the attack surface. So that's one aspect about why 6th was so interesting. Another thing is how we're going about it. So we talked about um you mentioned some of the things that people like about open source, which is one is transparency, so sunlight is the best disinfectant, right? Everybody can see the code, we can kind of make it more secure. Um and then the other is agency where basically if you're waiting on a vendor to go do something, um if it's proprietary software, you you really don't have much agency to get that vendor to go do that thing. Where is the open source? If you don't, if you're tired of waiting around, you can just submit the patch. So, um what we've seen with package software is with open source, we've had all this transparency and agency, but we've lost it with software as a service, right? Where vendors or cloud service providers are taking package software and then they're making it available as a service but that operationalize ng that software that is proprietary and it doesn't get contributed back. And so what Lukes building here as long along with our partners down, Lawrence from google, very active contributor in it. Um, the, is the operational piece to actually run sixth or as a public service is part of the open source project so people can then go and take sixth or maybe run it as a smaller internal service. Maybe they discover a bug, they can fix that bug contributed back to the operational izing piece as well as the traditional package software to basically make it a much more robust and open service. So you bring that transparency and the agency back to the SAS model as well. >>Look if you don't mind before, before uh and this segment proportion of it. The importance of immune ability is huge in the world of data. Can you share more on that? Because you're seeing that as a key part of the Blockchain for instance, having this ability to have immune ability. Because you know, people worry about, you know, how things progress in this distributed world. You know, whether from a hacking standpoint or tracking changes, Mutability becomes super important and how it's going to be preserved in this uh new six doorway. >>Oh yeah, so um mutability essentially means cannot be changed. So the structure of something is set. If it is anyway tampered or changed, then it breaks the cryptographic structure that we have of our public transparency service. So this way anybody can effectively recreate the cryptographic structure that we have of this public transparency service. So this mutability provides trust that there is non repudiation of the data that you're getting. This data is data that you can trust because it's built upon a cryptographic foundation. So it has very much similar parallels to Blockchain. You can trust Blockchain because of the immutable nature of it. And there is some consensus as well. Anybody can effectively download the Blockchain and run it themselves and compute that the integrity of that system can be trusted because of this immutable nature. So that's why we made this an inherent part of Six door is so that anybody can publicly audit these events and data sets to establish that there tamper free. >>That is a huge point. I think one of the things beyond just the security aspect of being hacked and protecting assets um trust is a huge part of our society now, not just on data but everything, anything that's reputable, whether it's videos like this being deep faked or you know, or news or any information, all this ties to security again, fundamentally and amazing concepts. Um I really want to keep an eye on this great work. Um Pearl, I gotta get back to you on Quantum because again, you can't, I mean people love Quantum. It's just it feels like so sci fi and it's like almost right here, right, so close and it's happening. Um And then people get always, what does that mean for security? We go back to look and ask them well quantum, you know, crypto But before we get started I wanted, I'm curious about how that's gonna play out from the project because is it going to be more part of like a C. N. C. F. How do you bring the open source vibe to Quantum? >>Uh so that's a very good question because that was a plan, the whole work that we are going to do related to operators to enable Quantum is managed by the open source community and that project lies in the casket. So casket has their own open source community and all the modification by the way, I should first tell you what excuse did so cute skin is the dedicate that you use to develop circuits that are run on IBM or Honeywell back in. So there are certain Quantum computers back and that support uh, circuits that are created using uh Houston S ticket, which is an open source as well. So there is already a community around this which is the casket. Open source community and we have pushed the code and all the maintenance is taken care of by that community. Do answer your question about if we are going to integrate it with C and C. F. That is not in the picture right now. We are, it has a place in its own community and it is also very niche to people who are working on the Quantum. So right now you have like uh the contributors who who are from IBM as well as other uh communities that are specific specifically working on content. So right now I don't think so, we have the map to integrated the C. N. C. F. But open source is the way to go and we are on that tragic Torri >>you know, we joke here the cube that a cubit is coming around the corner can can help but we've that in you know different with a C. But um look, I want to ask you one of the things that while you're here your security guru. I wanted to ask you about Quantum because a lot of people are scared that Quantum is gonna crack all the keys on on encryption with his power and more hacking. You're just comment on that. What's your what's your reaction to >>that? Yes that's an incredibly good question. This will occur. Okay. And I think it's really about preparation more than anything now. One of the things that we there's a principle that we have within the security world when it comes to coding and designing of software and this aspect of future Cryptography being broken. As we've seen with the likes of MD five and Sha one and so forth. So we call this algorithm agility. So this means that when you write your code and you design your systems you make them conducive to being able to easily swap and pivot the algorithms that use. So the encryption algorithms that you have within your code, you do not become too fixed to those. So that if as computing gets more powerful and the current sets of algorithms are shown to have inherent security weaknesses, you can easily migrate and pivot to a stronger algorithms. So that's imperative. Lee is that when you build code, you practice this principle of algorithm agility so that when shot 256 or shot 5 12 becomes the shar one. You can swap out your systems. You can change the code in a very least disruptive way to allow you to address that floor within your within your code in your software projects. >>You know, luke. This is mind bender right there. Because you start thinking about what this means is when you think about algorithmic agility, you start thinking okay software countermeasures automation. You start thinking about these kinds of new trends where you need to have that kind of signature capability. You mentioned with this this project you're mentioning. So the ability to actually who signs off on these, this comes back down to the paradigm that you guys are talking about here. >>Yes, very much so. There's another analogy from the security world, they call it turtles all the way down, which is effectively you always have to get to the point that a human or a computer establishes that first point of trust to sign something off. And so so it is it's a it's a world that is ever increasing in complexity. So the best that you can do is to be prepared to be as open as you can to make that pivot as and when you need to. >>Pretty impressive, great insight steve. We can talk for hours on this panel, emerging tech with red hat. Just give us a quick summary of what's going on. Obviously you've got a serious brain trust going on over there. Real world impact. You talk about the future of trust, future of software, future of computing, all kind of going on real time right now. This is not so much R and D as it is the front range of tech. Give us a quick overview of >>Yeah, sure, yeah, sure. The first thing I would tell everyone is go check out next that red hat dot com, that's got all of our different projects, who to contact if you're interested in learning more about different areas that we're working on. And it also lists out the different areas that we're working on, but just as an overview. So we're working on software defined storage, cloud storage. Sage. Well, the creator of Cf is the person that leads that group. We've got a team focused on edge computing. They're doing some really cool projects around um very lightweight operating systems that and kubernetes, you know, open shift based deployments that can run on, you know, devices that you screw into the sheet rock, you know, for that's that's really interesting. Um We have a cloud networking team that's looking at over yin and just intersection of E B P F and networking and kubernetes. Um and then uh you know, we've got an application platforms team that's looking at Quantum, but also sort of how to advance kubernetes itself. So that's that's the team where you got the persistent volume framework from in kubernetes and that added block storage and object storage to kubernetes. So there's a lot of really exciting things going on. Our charter is to inform red hats long term technology strategy. We work the way my personal philosophy about how we do that is that Red hat has product engineering focuses on their product roadmap, which is by nature, you know, the 6 to 9 months. And then the longer term strategy is set by both of us. And it's just that they're not focused on it. We're focused on it and we spend a lot of time doing disambiguate nation of the future and that's kind of what we do. We love doing it. I get to work with all these really super smart people. It's a fun job. >>Well, great insights is super exciting, emerging tack within red hat. I'll see the industry. You guys are agile, your open source and now more than ever open sources, uh, product Ization of open source is happening at such an accelerated rate steve. Thanks for coming on parole. Thanks for coming on luke. Great insight all around. Thanks for sharing. Uh, the content here. Thank you. >>Our pleasure. >>Thank you. >>Okay. We were more, more redhead coverage after this. This video. Obviously, emerging tech is huge. Watch some of the game changing action here at Redhead Summit. I'm john ferrier. Thanks for watching. Yeah.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the queue. It's virtual coverage of Avis reinvent 2020 I'm John furry, your host. Um, this is a cute virtual we're here. Not in, in remote. We're not in person this year, so we're doing the remote interviews. And then this segment is going to build on the quantum conversation we had last year, Richard moles, general manager of Amazon bracket and aid was quantum computing and Fernando Brandao head of quantum algorithms at AWS and Brent professor of theoretical physics at Caltech. Fernando, thanks for coming on, Richard. Thanks for joining us. >>You're welcome to be here. >>So, Fernando, first of all, love your title, quantum algorithms. That's the coolest title I've heard so far and you're pretty smart because you're a theoretical professor of physics at Caltech. So, um, which I'd never be able to get into, but I wish I could get into there someday, but, uh, thanks for coming on. Um, quantum has been quite the rage and you know, there's a lot of people talking about it. Um, it's not ready for prime time. Some say it's moving faster than others, but where are we on quantum right now? What are, what are you, what are you seeing Fernanda where the quantum, where are peg us in the evolution of, of, uh, where we are? >>Um, yeah, what quantum, uh, it's an emerging and rapidly developing fields. Uh, but we are see where are you on, uh, both in terms of, uh, hardware development and in terms of identifying the most impactful use cases of one company. Uh, so, so it's, it's, it's early days for everyone and, and we have like, uh, different players and different technologies that are being sport. And I think it's, it's, it's early, but it's exciting time to be doing quantum computing. And, uh, and it's very interesting to see the interest in industry growing and, and customers. Uh, for example, Casa from AWS, uh, being, uh, being willing to take part in this journey with us in developmental technology. >>Awesome. Richard, last year we talked to bill Vass about this and he was, you know, he set expectations really well, I thought, but it was pretty much in classic Amazonian way. You know, it makes the announcement a lot of progress then makes me give us the update on your end. You guys now are shipping brackets available. What's the update on your end and Verner mentioned in his keynote this week >> as well. Yeah, it was a, it was great until I was really looking at your interview with bill. It was, uh, that was when we launched the launch the service a year ago, almost exactly a year ago this week. And we've come a long way. So as you mentioned, we've, uh, we've, uh, we've gone to general availability with the service now that that happened in August. So now a customer can kind of look into the, uh, to the bracket console and, uh, installed programming concept computers. You know, there's, uh, there's tremendous excitement obviously, as, as you mentioned, and Fernando mentioned, you know, quantum computers, uh, we think >>Have the potential to solve problems that are currently, uh, uh, unsolvable. Um, the goal of bracket is to fundamentally give customers the ability to, uh, to go test, uh, some of those notions to explore the technology and to just start planning for the future. You know, our goal was always to try and solve some of the problems that customers have had for, you know, gee, a decade or so now, you know, they tell us from a variety of different industries, whether it's drug discovery or financial services, whether it's energy or there's chemical engineering, machine learning, you know, th the potential for quantum computer impacts may industries could potentially be disruptive to those industries. And, uh, it's, it's essential that customers can can plan for the future, you know, build their own internal resources, become experts, hire the right staff, figure out where it might impact their business and, uh, and potentially disrupt. >>So, uh, you know, in the past they're finding it hard to, to get involved. You know, these machines are very different, different technologies building in different ways of different characteristics. Uh, the tooling is very disparate, very fragmented. Historically, it's hard for companies to get access to the machines. These tend to be, you know, owned by startups or in, you know, physics labs or universities, very difficult to get access to these things, very different commercial models. Um, and, uh, as you, as you suggested, a lot of interests, a lot of hype, a lot of claims in the industry, customers want to cut through all that. They want to understand what's real, uh, what they can do today, uh, how they can experiment and, uh, and get started. So, you know, we see bracket as a catalyst for innovation. We want to bring together end-users, um, consultants, uh, software developers, um, providers that want to host services on top of bracket, try and get the industry, you know, rubbing along them. You spoke to lots of Amazonians. I'm sure you've heard the phrase innovation flywheel, plenty of times. Um, we see the same approach that we've used successfully in IOT and robotics and machine learning and apply that same approach to content, machine learning software, to quantum computing, and to learn, to bring it together. And, uh, if we get the tooling right, and we make it easy, um, then we don't see any reason why we can't, uh, you know, rapidly try and move this industry forward. And >>It was fun areas where there's a lot of, you know, intellectual computer science, um, technology science involved in super exciting. And Amazon's supposed to some of that undifferentiated heavy. >>That's what I am, you know, it's like, >>There's a Maslow hierarchy of needs in the tech industry. You know, people say, Oh, why five people freak out when there's no wifi? You know, you can't get enough compute. Right. So, you know, um, compute is one of those things with machine learning is seeing the benefits and quantum there's so much benefits there. Um, and you guys made some announcements at, at re-invent, uh, around BRACA. Can you share just quickly share some of those updates, Richard? >>Sure. I mean, it's the way we innovate at AWS. You know, we, we start simple and we, and we build up features. We listen to customers and we learn as we go along, we try and move as quickly as possible. So since going public in, uh, in, in August, we've actually had a string of releases, uh, pretty consistent, um, delivering new features. So we try to tie not the integration with the platform. Customers have told us really very early on that they, they don't just want to play with the technology. They want to figure out how to, how to envisage a production quantum computing service, how it might look, you know, in the context of a broad cloud platform with AWS. So we've, uh, we launched some integration with, uh, other AWS capabilities around security, managing limits, quotas, tagging resources, that type of thing, things that are familiar to, uh, to, to, to current AWS users. >>Uh, we launched some new hardware. Uh, all of our partners D-Wave launched some, uh, uh, you know, a 5,000 cubit machine, uh, just in September. Uh, so we made that available on bracket the same day that they launched that hardware, which was very cool. Um, you know, we've made it, uh, we've, we've made it easier for researchers. We've been, you know, impressed how many academics and researchers have used the service, not just large corporations. Um, they want to have really deep access to these machines. They want to program these things at a low level. So we launched some features, uh, to enable them to do their research, but reinvent, we were really focused on two things, um, simulators and making it much easier to use, uh, hybrid systems systems that, uh, incorporate classical compute, traditional digital computing with quantum machinery, um, in the vein that follow some of the liens that we've seen, uh, in machine learning. >>So, uh, simulators are important. They're a very important part of, uh, learning how to use concepts, computers. They're always available 24, seven they're super convenient to use. And of course they're critical in verifying the accuracy of the results that we get from quantum hardware. When we launched the service behind free simulator for customers to help debug their circuits and experiments quickly, um, but simulating large experiments and large systems is a real challenge on classical computers. You know, it, wasn't hard on classical. Uh, then you wouldn't need a quantum computer. That's the whole point. So running large simulations, you know, is expensive in terms of resources. It's complicated. Uh, we launched a pretty powerful simulator, uh, back in August, which we thought at the time was always powerful managed. Quantum stimulates circuit handled 34 cubits, and it reinvented last week, we launched a new simulator, which actually the first managed simulator to use tensor network technology. >>And it can run up to 50 cubits. So we think is, we think is probably the most powerful, uh, managed quantum simulator on the market today. And customers can flip easily between either using real quantum hardware or either of our, uh, stimulators just by changing a line of code. Um, the other thing we launched was the ability to run these hybrid systems. You know, quantum computers will get more, no don't get onto in a moment is, uh, today's computers are very imperfect, you know, lots of errors. Um, we working, obviously the industry towards fault-tolerant machines and Fernando can talk about some research papers that were published in that area, but right now the machines are far from perfect. And, uh, and the way that we can try to squeeze as much value out of these devices today is to run them in tandem with classical systems. >>We think of the notion of a self-learning quantum algorithm, where you use a classical optimization techniques, such as we see machine learning to tweak and tune the parameters of a quantum algorithm to try and iterate and converge on the best answer and try and overcome some of these issues surrounding errors. That's a lot of moving parts to orchestrate for customers, a lot of different systems, a lot of different programming techniques. And we wanted to make that much easier. We've been impressed with a, a, an open projects, been around for a couple of years, uh, called penny lane after the Beatles song. And, um, so we wanted to double down on that. We were getting a lot of positive feedback from customers about the penny lane talk it, so we decided to, uh, uh, make it a first class citizen on bracket, make it available as a native feature, uh, in our, uh, in our Jupiter notebooks and our tutorials learning examples, um, that open source project has very similar, um, guiding principles that we do, you know, it's open, it's cross platform, it's technology agnostic, and we thought he was a great fit to the service. >>So we, uh, we announced that and made it available to customers and, uh, and, and, uh, already getting great feedback. So, uh, you know, finishing the finishing the year strongly, I think, um, looking forward to 2021, you know, looking forward to some really cool technology it's on the horizon, uh, from a hardware point of view, making it easy to use, um, you know, and always, obviously trying to work back from customer problems. And so congratulations on the success. I'm sure it's not hard to hire people interested, at least finding qualified people it'd be different, but, you know, sign me up. I love quantum great people, Fernando real quick, understanding the relationship with Caltech unique to Amazon. Um, tell us how that fits into the, into this, >>Uh, right. John S no, as I was saying, it's it's early days, uh, for, for quantum computing, uh, and to make progress, uh, in abreast, uh, put together a team of experts, right. To work both on, on find new use cases of quantum computing and also, uh, building more powerful, uh, quantum hardware. Uh, so the AWS center for quantum computing is based at Caltech. Uh, and, and this comes from the belief of AWS that, uh, in quantum computing is key to, uh, to keep close, to stay close of like fresh ideas and to the latest scientific developments. Right. And Caltech is if you're near one computing. So what's the ideal place for doing that? Uh, so in the center, we, we put together researchers and engineers, uh, from computer science, physics, and other subjects, uh, from Amazon, but also from all the academic institutions, uh, of course some context, but we also have Stanford and university of Chicago, uh, among others. So we broke wrongs, uh, in the beauty for AWS and for quantum computer in the summer, uh, and under construction right now. Uh, but, uh, as we speak, John, the team is busy, uh, uh, you know, getting stuff in, in temporary lab space that we have at cottage. >>Awesome. Great. And real quick, I know we've got some time pressure here, but you published some new research, give a quick a plug for the new research. Tell us about that. >>Um, right. So, so, you know, as part of the effort or the integration for one company, uh, we are developing a new cubix, uh, which we choose a combination of acoustic and electric components. So this kind of hybrid Aquacel execute, it has the promise for a much smaller footprint, think about like a few microliters and much longer storage times, like up to settlements, uh, which, which is a big improvement over the scale of the arts sort of writing all export based cubits, but that's not the whole story, right? On six, if you have a good security should make good use of it. Uh, so what we did in this paper, they were just put out, uh, is, is a proposal for an architecture of how to build a scalable quantum computer using these cubits. So we found from our analysis that we can get more than a 10 X overheads in the resources required from URI, a universal thought around quantum computer. >>Uh, so what are these resources? This is like a smaller number of physical cubits. Uh, this is a smaller footprint is, uh, fewer control lines in like a smaller approach and a consistent, right. And, and these are all like, uh, I think this is a solid contribution. Uh, no, it's a theoretical analysis, right? So, so the, uh, the experimental development has to come, but I think this is a solid contribution in the big challenge of scaling up this quantum systems. Uh, so, so, so John, as we speak like, uh, data blessed in the, for quantum computing is, uh, working on the experimental development of this, uh, a highly adequacy architecture, but we also keep exploring other promising ways of doing scalable quantum computers and eventually, uh, to bring a more powerful computer resources to AWS customers. >>It's kind of like machine learning and data science, the smartest people work on it. Then you democratize that. I can see where this is going. Um, Richard real quick, um, for people who want to get involved and participate or consume, what do they do? Give us the playbook real quick. Uh, so simple, just go to the AWS console and kind of log onto the, to the bracket, uh, bracket console, jump in, you know, uh, create, um, create a Jupiter notebook, pull down some of our sample, uh, applications run through the notebook and program a quantum computer. It's literally that simple. There's plenty of tutorials. It's easy to get started, you know, classic cloud style right now from commitment. Jump in, start simple, get going. We want you to go quantum. You can't go back, go quantum. You can't go back to regular computing. I think people will be running concert classical systems in parallel for quite some time. So yeah, this is the, this is definitely not a one way door. You know, you go explore quantum computing and see how it fits into, uh, >>You know, into the, into solving some of the problems that you wanted to solve in the future. But definitely this is not a replacement technology. This is a complimentary technology. >>It's great. It's a great innovation. It's kind of intoxicating technically to get, think about the benefits Fernando, Richard, thanks for coming on. It's really exciting. I'm looking forward to keeping up keeping track of the progress. Thanks for coming on the cube coverage of reinvent, quantum computing going the next level coexisting building on top of the shoulders of other giant technologies. This is where the computing wave is going. It's different. It's impacting people's lives. This is the cube coverage of re-invent. Thanks for watching.

>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> Announcer: From around the globe, it's theCUBE. Covering the UPGRADE 2020, the NTT Research Summit. Presented by NTT research. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our ongoing coverage of UPGRADE 2020. It's the NTT Research Labs Summit, and it's all about upgrading reality. Heavy duty basic research around a bunch of very smart topics. And we're really excited to have our next guest to kind of dive in. I promise you, it'll be the deepest conversation you have today, unless you watch a few more of these segments. So our first guest we're welcoming back Kazuhiro Gomi He's the president and CEO of NTT research, Kaza great to see you. >> Good to see you. And joining him is Yoshi Yamamoto. He is a fellow for NTT Research and also the director of the Physics and Informatics Lab. Yoshi, great to meet you as well. >> Nice to meet you. >> So I was teasing the crew earlier, Yoshi, when I was doing some background work on you and I pulled up your Wikipedia page and I was like, okay guys, read this thing and tell me what a, what Yoshi does. You that have been knee deep in quantum computing and all of the supporting things around quantum heavy duty kind of next gen computing. I wonder if you can kind of share a little bit, you know, your mission running this labs and really thinking so far in advance of what we, you know, kind of experience and what we work with today and this new kind of basic research. >> NTT started the research on quantum computing back in 1986 87. So it is already more than 30 years. So, the company invested in this field. We have accumulated a lot of sort of our ideas, knowledge, technology in this field. And probably, it is the right time to establish the connection, close connection to US academia. And in this way, we will jointly sort of advance our research capabilities towards the future. The goal is still, I think, a long way to go. But by collaborating with American universities, and students we can accelerate NTT effort in this area. >> So, you've been moving, you've been working on quantum for 30 years. I had no idea that that research has been going on for such a very long time. We hear about it in the news and we hear about it a place like IBM and iSensor has a neat little demo that they have in the new sales force period. What, what is, what makes quantum so exciting and the potential to work so hard for so long? And what is it going to eventually open up for us when we get it to commercial availability? >> The honest answer to that question is we don't know yet. Still, I think after 30 years I think of hard working on quantum Physics and Computing. Still we don't know clean applications are even, I think we feel that the current, all the current efforts, are not necessarily, I think, practical from the engineering viewpoint. So, it is still a long way to go. But the reason why NTT has been continuously working on the subject is basically the very, sort of bottom or fundamental side of the present day communication and the computing technology. There is always a quantum principle and it is very important for us to understand the quantum principles and quantum limit for communication and computing first of all. And if we are lucky, maybe we can make a breakthrough for the next generation communication and computing technology based on quantum principles. >> Right. >> But the second, is really I think just a guess, and hope, researcher's hope and nothing very solid yet. >> Right? Well, Kazu I want to go, go to you cause it really highlights the difference between, you know, kind of basic hardcore fundamental research versus building new applications or building new products or building new, you know, things that are going to be, you know, commercially viable and you can build an ROI and you can figure out what the customers are going to buy. It really reflects that this is very different. This is very, very basic with very, very long lead times and very difficult execution. So when, you know, for NTT to spend that money and invest that time and people for long, long periods of time with not necessarily a clean ROI at the end, that really, it's really an interesting statement in terms of this investment and thinking about something big like upgrading reality. >> Yeah, so that's what this, yeah, exactly that you talked about what the basic research is, and from NTT perspective, yeah, we feel like we, as Dr. Yamamoto, he just mentioned that we've been investing into 30 plus years of a time in this field and, you know, and we, well, I can talk about why this is important. And some of them is that, you know, that the current computer that everybody uses, we are certainly, well, there might be some more areas of improvement, but we will someday in, I don't know, four years, five years, 10 years down the road, there might be some big roadblock in terms of more capacity, more powers and stuff. We may run into some issues. So we need to be prepared for those kinds of things. So, yes we are in a way of fortunate that we are, we have a great team to, and a special and an expertise in this field. And, you know, we have, we can spend some resource towards that. So why not? We should just do that in preparation for that big, big wall so to speak. I guess we are expecting to kind of run into, five, 10 years down the road. So let's just looking into it, invest some resources into it. So that's where we are, we're here. And again, I I'm, from my perspective, we are very fortunate that we have all the resources that we can do. >> It's great. Right, as they give it to you. Dr. Yamamoto, I wonder if you can share what it's like in terms of the industry and academic working together. You look at the presentations that are happening here at the event. All the great academic institutions are very well represented, very deep papers. You at NTT, you spend some time at Stanford, talk about how it is working between this joint development with great academic institutions, as well as the great company. >> Traditionally in the United States, there has been always two complementary opportunities for training next generation scientists and engineers. One opportunity is junior faculty position or possible position in academia, where main emphasis is education. The other opportunity is junior researcher position in industrial lab where apparently the focus emphasis is research. And eventually we need two types of intellectual leaders from two different career paths. When they sort of work together, with a strong educational background and a strong research background, maybe we can make wonderful breakthrough I think. So it is very important to sort of connect between two institutions. However, in the recent past, particularly after Better Lab disappeared, basic research activity in industrial lab decreases substantially. And we hope MTT research can contribute to the building of fundamental science in industry side. And for that purpose cross collaboration with research Universities are very important. So the first task we have been working so far, is to build up this industry academia connection. >> Huge compliment NTT to continue to fund the basic research. Cause as you said, there's a lot of companies that were in it before and are not in it any more. And when you often read the history of, of, of computing and a lot of different things, you know, it goes back to a lot of times, some basic, some basic research. And just for everyone to know what we're talking about, I want to read a couple of, of sessions that you could attend and learn within Dr. Yamamoto space. So it's Coherent nonlinear dynamics combinatorial optimization. That's just one session. I love it. Physics successfully implements Lagrange multiplier optimization. I love it. Photonics accelerators for machine learning. I mean, it's so it's so interesting to read basic research titles because, you know, it's like a micro-focus of a subset. It's not quantum computing, it's all these little smaller pieces of the quantum computing stack. And then obviously very deep and rich. Deep dives into those, those topics. And so, again, Kazu, this is the first one that's going to run after the day, the first physics lab. But then you've got the crypto cryptography and information security lab, as well as the medical and health information lab. You started with physics and informatics. Is that the, is that the history? Is that the favorite child you can lead that day off on day two of the event. >> We did throw a straw and Dr. Yamamoto won it Just kidding (all laugh) >> (indistinct), right? It's always fair. >> But certainly this quantum, Well, all the topics certainly are focuses that the basic research, that's definitely a commonality. But I think the quantum physics is in a way kind of very symbolic to kind of show that the, what the basic research is. And many people has a many ideas associated with the term basic research. But I think that the quantum physics is certainly one of the strong candidates that many people may think of. So well, and I think this is definitely a good place to start for this session, from my perspective. >> Right. >> Well, and it almost feels like that's kind of the foundational even for the other sessions, right? So you talk about medical or you talk about cryptography in information, still at the end of the day, there's going to be compute happening to drive those processes. Whether it's looking at, at, at medical slides or trying to do diagnosis, or trying to run a bunch of analysis against huge data sets, which then goes back to, you know, ultimately algorithms and ultimately compute, and this opening up of this entirely different set of, of horsepower. But Dr. Yamamoto, I'm just curious, how did you get started down this path of, of this crazy 30 year journey on quantum computing. >> The first quantum algorithm was invented by David Deutsch back in 1985. These particular algorithm turned out later the complete failure, not useful at all. And he spent seven years, actually, to fix loophole and invented the first successful algorithm that was 1992. Even though the first algorithm was a complete failure, that paper actually created a lot of excitement among the young scientists at NTT Basic Research Lab, immediately after the paper appeared. And 1987 is actually, I think, one year later. So this paper appeared. And we, sort of agreed that maybe one of the interesting future direction is quantum information processing. And that's how it started. It's it's spontaneous sort of activity, I think among young scientists of late twenties and early thirties at the time. >> And what do you think Dr. Yamamoto that people should think about? If, if, if again, if we're at a, at a cocktail party, not with not with a bunch of, of people that, that intimately know the topic, how do you explain it to them? How, how should they think about this great opportunity around quantum that's kept you engaged for decades and decades and decades. >> The quantum is everywhere. Namely, I think this world I think is fundamentally based on and created from quantum substrate. At the very bottom of our, sort of world, consist of electrons and photons and atoms and those fundamental particles sort of behave according to quantum rule. And which is a very different from classical reality, namely the world where we are living every day. The relevant question which is also interesting is how our classical world or classical reality surfaces from the general or universal quantum substrate where our intuition never works. And that sort of a fundamental question actually opens the possibility I think by utilizing quantum principle or quantum classical sort of crossover principle, we can revolutionize the current limitation in communication and computation. That's basically the start point. We start from quantum substrate. Under classical world the surface is on top of quantum substrate exceptional case. And we build the, sort of communication and computing machine in these exceptional sort of world. But equally dig into quantum substrate, new opportunities is open for us. That's somewhat the fundamental question. >> That's great. >> Well, I'm not, yeah, we can't get too deep cause you'll lose me, you'll lose me long before, before you get to the bottom of the, of the story, but, you know, I really appreciate it. And of course back to you this is your guys' first event. It's a really bold statement, right? Upgrade reality. I just wonder if, when you look at the, at the registrant's and you look at the participation and what do you kind of anticipate, how much of the anticipation is, is kind of people in the business, you know, kind of celebrating and, and kind of catching up to the latest research and how much of it is going to be really inspirational for those next, you know, early 20 somethings who are looking to grab, you know, an exciting field to hitch their wagon to, and to come away after this, to say, wow, this is something that really hooked me and I want to get down and really kind of advance this technology a little bit, further advance this research a little bit further. >> So yeah, for, from my point of view for this event, I'm expecting, there are quite wide range of people. I'm, I'm hoping that are interested in to this event. Like you mentioned that those are the, you know, the business people who wants to know what NTT does, and then what, you know, the wider spectrum of NTT does. And then, and also, especially like today's events and onwards, very specific to each topic. And we go into very deep dive. And, and so to, to this session, especially in a lot of participants from the academia's world, for each, each subject, including students, and then some other, basically students and professors and teachers and all those people as well. So, so that's are my expectations. And then from that program arrangement perspective, that's always something in my mind that how do we address those different kind of segments of the people. And we all welcoming, by the way, for those people. So to me to, so yesterday was the general sessions where I'm kind of expecting more that the business, and then perhaps some other more and more general people who're just curious what NTT is doing. And so instead of going too much details, but just to give you the ideas that the what's that our vision is and also, you know, a little bit of fla flavor is a good word or not, but give you some ideas of what we are trying to do. And then the better from here for the next three days, obviously for the academic people, and then those who are the experts in each field, probably day one is not quite deep enough. Not quite addressing what they want to know. So day two, three, four are the days that designed for that kind of requirements and expectations. >> Right? And, and are most of the presentations built on academic research, that's been submitted to journals and other formal, you know, peer review and peer publication types of activities. So this is all very formal, very professional, and very, probably accessible to people that know where to find this information. >> Mmh. >> Yeah, it's great. >> Yeah. >> Well, I, I have learned a ton about NTT and a ton about this crazy basic research that you guys are doing, and a ton about the fact that I need to go back to school if I ever want to learn any of this stuff, because it's, it's a fascinating tale and it's it's great to know as we've seen these other basic research companies, not necessarily academic but companies kind of go away. We mentioned Xerox PARC and Bell Labs that you guys have really picked up that mantle. Not necessarily picked it up, you're already doing it yourselves. but really continuing to carry that mantle so that we can make these fundamental, basic building block breakthroughs to take us to the next generation. And as you say, upgrade the future. So again, congratulations. Thanks for sharing this story and good luck with all those presentations. >> Thank you very much. >> Thank you. >> Thank you. Alright, Yoshi, Kazu I'm Jeff, NTT UPGRADE 2020. We're going to upgrade the feature. Thanks for watching. See you next time. (soft music)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

Hi, I'm Hideo Mabuchi from Stanford University. This is my presentation on coherent nonlinear dynamics, and combinatorial optimization. This is going to be a talk, to introduce an approach, we are taking to the analysis, of the performance of Coherent Ising Machines. So let me start with a brief introduction, to ising optimization. The ising model, represents a set of interacting magnetic moments or spins, with total energy given by the expression, shown at the bottom left of the slide. Here the cigna variables are meant to take binary values. The matrix element jij, represents the interaction, strength and sign, between any pair of spins ij, and hi represents a possible local magnetic field, acting on each thing. The ising ground state problem, is defined in an assignment of binary spin values, that achieves the lowest possible value of total energy. And an instance of the easing problem, is specified by given numerical values, for the matrix j and vector h, although the ising model originates in physics, we understand the ground state problem, to correspond to what would be called, quadratic binary optimization, in the field of operations research. And in fact, in terms of computational complexity theory, it can be established that the, ising ground state problem is NP complete. Qualitatively speaking, this makes the ising problem, a representative sort of hard optimization problem, for which it is expected, that the runtime required by any computational algorithm, to find exact solutions, should asyntonically scale, exponentially with the number of spins, and four worst case instances at each end. Of course, there's no reason to believe that, the problem instances that actually arise, in practical optimization scenarios, are going to be worst case instances. And it's also not generally the case, in practical optimization scenarios, that we demand absolute optimum solutions. Usually we're more interested in, just getting the best solution we can, within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for computation. This focus is great interest on, so-called heuristic algorithms, for the ising problem and other NP complete problems, which generally get very good, but not guaranteed optimum solutions, and run much faster than algorithms, that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem, for which extensive compilations, of benchmarking data may be found online. A recent study found that, the best known TSP solver required median runtimes, across a library of problem instances, that scaled as a very steep route exponential, for an up to approximately 4,500. This gives some indication of the change, in runtime scaling for generic, as opposed to worst case problem instances. Some of the instances considered in this study, were taken from a public library of TSPs, derived from real world VLSI design data. This VLSI TSP library, includes instances within ranging from 131 to 744,710, instances from this library within between 6,880 and 13,584, were first solved just a few years ago, in 2017 requiring days of runtime, and a 48 core two gigahertz cluster, all instances with n greater than or equal to 14,233, remain unsolved exactly by any means. Approximate solutions however, have been found by heuristic methods, for all instances in the VLSI TSP library, with, for example, a solution within 0.014% of a known lower bound, having been discovered for an instance, with n equal 19,289, requiring approximately two days of runtime, on a single quarter at 2.4 gigahertz. Now, if we simple-minded the extrapolate, the route exponential scaling, from the study yet to n equal 4,500, we might expect that an exact solver, would require something more like a year of runtime, on the 48 core cluster, used for the n equals 13,580 for instance, which shows how much, a very small concession on the quality of the solution, makes it possible to tackle much larger instances, with much lower costs, at the extreme end, the largest TSP ever solved exactly has n equal 85,900. This is an instance derived from 1980s VLSI design, and this required 136 CPU years of computation, normalized to a single core, 2.4 gigahertz. But the 20 fold larger, so-called world TSP benchmark instance, with n equals 1,904,711, has been solved approximately, with an optimality gap bounded below 0.0474%. Coming back to the general practical concerns, of applied optimization. We may note that a recent meta study, analyze the performance of no fewer than, 37 heuristic algorithms for MaxCut, and quadratic binary optimization problems. And find the performance... Sorry, and found that a different heuristics, work best for different problem instances, selected from a large scale heterogeneous test bed, with some evidence, the cryptic structure, in terms of what types of problem instances, were best solved by any given heuristic. Indeed, there are reasons to believe, that these results for MaxCut, and quadratic binary optimization, reflect to general principle, of a performance complementarity, among heuristic optimization algorithms, and the practice of solving hard optimization problems. There thus arises the critical pre processing issue, of trying to guess, which of a number of available, good heuristic algorithms should be chosen, to tackle a given problem instance. Assuming that any one of them, would incur high cost to run, on a large problem of incidents, making an astute choice of heuristic, is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight, about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This is certainly pinpointed by researchers in the field, as a circumstance and must be addressed. So adding this all up, we see that a critical frontier, for cutting edge academic research involves, both the development of novel heuristic algorithms, that deliver better performance with lower costs, on classes of problem instances, that are underserved by existing approaches, as well as fundamental research, to provide deep conceptual insight, into what makes a given problem instance, easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law, and speculate about a so-called second quantum revolution, it's natural to talk not only about novel algorithms, for conventional CPUs, but also about highly customized, special purpose hardware architectures, on which we may run entirely unconventional algorithms, for common tutorial optimizations, such as ising problem. So against that backdrop, I'd like to use my remaining time, to introduce our work on, analysis of coherent using machine architectures, and associated optimization algorithms. Ising machines in general, are a novel class of information processing architectures, for solving combinatorial optimization problems, by embedding them in the dynamics, of analog, physical, or a cyber-physical systems. In contrast to both more traditional engineering approaches, that build ising machines using conventional electronics, and more radical proposals, that would require large scale quantum entanglement the emerging paradigm of coherent ising machines, leverages coherent nominal dynamics, in photonic or optical electronic platforms, to enable near term construction, of large scale prototypes, that leverage posting as information dynamics. The general structure of current of current CIM systems, as shown in the figure on the right, the role of the easing spins, is played by a train of optical pulses, circulating around a fiber optical storage ring, that beam splitter inserted in the ring, is used to periodically sample, the amplitude of every optical pulse. And the measurement results, are continually read into an FPGA, which uses then to compute perturbations, to be applied to each pulse, by a synchronized optical injections. These perturbations are engineered to implement, the spin-spin coupling and local magnetic field terms, of the ising hamiltonian, corresponding to a linear part of the CIM dynamics. Asynchronously pumped parametric amplifier, denoted here as PPL and wave guide, adds a crucial nonlinear component, to the CIM dynamics as well. And the basic CIM algorithm, the pump power starts very low, and is gradually increased, at low pump powers, the amplitudes of the easing spin pulses, behave as continuous complex variables, whose real parts which can be positive or negative, by the role of soft or perhaps mean field spins. Once the pump power crosses the threshold, for perimetric self oscillation in the optical fiber ring, however, the amplitudes of the easing spin pulses, become effectively quantized into binary values, while the pump power is being ramped up, the FPGA subsystem continuously applies, its measurement based feedback implementation, of the using hamiltonian terms. The interplay of the linearized easing dynamics, implemented by the FPGA , and the thresholds quantization dynamics, provided by the sink pumped parametric amplifier, result in a final state, of the optical plus amplitudes, at the end of the pump ramp, that can be read as a binary strain, giving a proposed solution, of the ising ground state problem. This method of solving ising problems, seems quite different from a conventional algorithm, that runs entirely on a digital computer. As a crucial aspect, of the computation is performed physically, by the analog continuous coherent nonlinear dynamics, of the optical degrees of freedom, in our efforts to analyze CA and performance. We have therefore turn to dynamical systems theory. Namely a study of bifurcations, the evolution of critical points, and typologies of heteroclitic orbits, and basins of attraction. We conjecture that such analysis, can provide fundamental insight, into what makes certain optimization instances, hard or easy for coherent ising machines, and hope that our approach, can lead to both improvements of the course CIM algorithm, and the pre processing rubric, for rapidly assessing the CIM insuibility of the instances. To provide a bit of intuition about how this all works. It may help to consider the threshold dynamics, of just one or two optical parametric oscillators, in the CIM architecture just described. We can think of each of the pulse time slots, circulating around the fiber ring, as are presenting an independent OPO. We can think of a single OPO degree of freedom, as a single resonant optical mode, that experiences linear dissipation, due to coupling loss, and gain in a pump near crystal, as shown in the diagram on the upper left of the slide, as the pump power is increased from zero. As in the CIM algorithm, the non-linear gain is initially too low, to overcome linear dissipation. And the OPO field remains in a near vacuum state, at a critical threshold value, gain equals dissipation, and the OPO undergoes a sort of lasing transition. And the steady States of the OPO, above this threshold are essentially coherent States. There are actually two possible values, of the OPO coherent amplitude, and any given above threshold pump power, which are equal in magnitude, but opposite in phase, when the OPO cross this threshold, it basically chooses one of the two possible phases, randomly, resulting in the generation, of a single bit of information. If we consider two uncoupled OPOs, as shown in the upper right diagram, pumped at exactly the same power at all times, then as the pump power is increased through threshold, each OPO will independently choose a phase, and thus two random bits are generated, for any number of uncoupled OPOs, the threshold power per OPOs is unchanged, from the single OPO case. Now, however, consider a scenario, in which the two appeals are coupled to each other, by a mutual injection of their out coupled fields, as shown in the diagram on the lower right. One can imagine that, depending on the sign of the coupling parameter alpha, when one OPO is lasing, it will inject a perturbation into the other, that may interfere either constructively or destructively, with the field that it is trying to generate, via its own lasing process. As a result, when can easily show that for alpha positive, there's an effective ferromagnetic coupling, between the two OPO fields, and their collective oscillation threshold, is lowered from that of the independent OPO case, but only for the two collective oscillation modes, in which the two OPO phases are the same. For alpha negative, the collective oscillation threshold, is lowered only for the configurations, in which the OPO phases are opposite. So then looking at how alpha is related to the jij matrix, of the ising spin coupling hamilitonian, it follows the, we could use this simplistic to OPO CIM, to solve the ground state problem, of the ferromagnetic or antiferromagnetic angles, to ising model, simply by increasing the pump power, from zero and observing what phase relation occurs, as the two appeals first start to lase. Clearly we can imagine generalizing the story to larger, and, however, the story doesn't stay as clean and simple, for all larger problem instances. And to find a more complicated example, we only need to go to n equals four, for some choices of jij for n equals four, the story remains simple, like the n equals two case. The figure on the upper left of this slide, shows the energy of various critical points, for a non frustrated n equals for instance, in which the first bifurcated critical point, that is the one that, by forgets of the lowest pump value a, this first bifurcated critical point, flows asyntonically into the lowest energy using solution, and the figure on the upper right, however, the first bifurcated critical point, flows to a very good, but suboptimal minimum at large pump power. The global minimum is actually given, by a distinct critical point. The first appears at a higher pump power, and is not needed radically connected to the origin. The basic CIM algorithm, is this not able to find this global minimum, such non-ideal behavior, seems to become more common at margin end, for the n equals 20 instance show in the lower plots, where the lower right pod is just a zoom into, a region of the lower left block. It can be seen that the global minimum, corresponds to a critical point, that first appears that of pump parameter a around 0.16, at some distance from the adriatic trajectory of the origin. That's curious to note that, in both of these small and examples, however, the critical point corresponding to the global minimum, appears relatively close, to the adiabatic trajectory of the origin, as compared to the most of the other, local minimum that appear. We're currently working to characterise, the face portrait typology, between the global minimum, and the adiabatic trajectory of the origin, taking clues as to how the basic CIM algorithm, could be generalized, to search for non-adiabatic trajectories, that jumped to the global minimum, during the pump up, of course, n equals 20 is still too small, to be of interest for practical optimization applications. But the advantage of beginning, with the study of small instances, is that we're able to reliably to determine, their global minima, and to see how they relate to the idea, that trajectory of the origin, and the basic CIM algorithm. And the small land limit, We can also analyze, for the quantum mechanical models of CAM dynamics, but that's a topic for future talks. Existing large-scale prototypes, are pushing into the range of, n equals, 10 to the four, 10 to the five, 10 to the six. So our ultimate objective in theoretical analysis, really has to be, to try to say something about CAM dynamics, and regime of much larger in. Our initial approach to characterizing CAM behavior, in the large end regime, relies on the use of random matrix theory. And this connects to prior research on spin classes, SK models, and the tap equations, et cetera, at present we're focusing on, statistical characterization, of the CIM gradient descent landscape, including the evolution of critical points, And their value spectra, as the pump powers gradually increase. We're investigating, for example, whether there could be some way, to explain differences in the relative stability, of the global minimum versus other local minima. We're also working to understand the deleterious, or potentially beneficial effects, of non-ideologies such as asymmetry, in the implemented using couplings, looking one step ahead, we plan to move next into the direction, of considering more realistic classes of problem instances, such as quadratic binary optimization with constraints. So in closing I should acknowledge, people who did the hard work, on these things that I've shown. So my group, including graduate students, Edwin Ng, Daniel Wennberg, Ryatatsu Yanagimoto, and Atsushi Yamamura have been working, in close collaboration with, Surya Ganguli, Marty Fejer and Amir Safavi-Naeini. All of us within the department of applied physics, at Stanford university and also in collaboration with Yoshihisa Yamamoto, over at NTT-PHI research labs. And I should acknowledge funding support, from the NSF by the Coherent Ising Machines, expedition in computing, also from NTT-PHI research labs, army research office, and ExxonMobil. That's it. Thanks very much.

(upbeat electronic music) >> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation! >> Hi, I'm Stu Miniman, your host for Cloud Native Insight. When we launched this series, one of the things we wanted to talk about was that we're not just using cloud as a destination, but really enabling new ways of thinking, being able to use the innovations underneath the cloud, and that if you use services in the cloud, that you're not necessarily locked into a solution or can't move forward. And that's why I'm really excited to help welcome to the program, I have the co-founders of Vendia. First we have Dr. Tim Wagner, he is the co-founder and CEO of the company, as well as generally known in the industry as the father of Serverless from the AWS Lambda, and his co-founder, Shruthi Rao, she is the chief business officer at Vendia, also came from AWS where she worked on blockchain solutions. Tim, Shruthi, thanks so much for joining us. >> Thanks for having us in here, Stu. Great to join the show. >> All right, so Shruthi, actually if we could start with you because before we get into Vendia, coming out of stealth, you know, really interesting technology space, you and Tim both learned a lot from working with customers in your previous jobs, why don't we start from you. Block chain of course had a lot of learnings, a lot of things that people don't understand about what it is and what it isn't, so give us a little bit about what you've learned and how that lead towards what you and Tim and the team are doing with Vendia. >> Yeah, absolutely, Stu! One, the most important thing that we've all heard of was this great gravitational pull towards blockchain in 2018 and 2019. Well, I was one of the founders and early adopters of blockchain from Bitcoin and Ethereum space, all the way back from 2011 and onwards. And at AWS I started the Amazon Managed Blockchain and launched Quantum Ledger Database, two services in the block chain category. What I learned there was, no surprise, there was a gold rush to blockchain from many customers. We, I personally talked to over 1,092 customers when I ran Amazon Managed Blockchain for the last two years. And I found that customers were looking at solving this dispersed data problem. Most of my customers had invested in IoT and edge devices, and these devices were gathering massive amounts of data, and on the flip side they also had invested quite a bit of effort in AI and ML and analytics to crunch this data, give them intelligence. But guess what, this data existed in multiple parties, in multiple clouds, in multiple technology stacks, and they needed a mechanism to get this data from wherever they were into one place so they could the AI, ML, analytics investment, and they wanted all of this to be done in real time, and to gravitated towards blockchain. But blockchain had quite a bit of limitations, it was not scalable, it didn't work with the existing stack that you had. It forced enterprises to adopt this new technology and entirely new type of infrastructure. It didn't work cross-cloud unless you hired expensive consultants or did it yourself, and required these specialized developers. For all of these reasons, we've seen many POCs, majority of POCs just dying on the vine and not ever reaching the production potential. So, that is when I realized that what the problem to be solved was not a trust problem, the problem was dispersed data in multiple clouds and multiple stacks problem. Sometimes multiple parties, even, problem. And that's when Tim and I started talking about, about how can we bring all of the nascent qualities of Lambda and Serverless and use all of the features of blockchain and build something together? And he has an interesting story on his own, right. >> Yeah. Yeah, Shruthi, if I could, I'd like to get a little bit of that. So, first of all for our audience, if you're watching this on the minute, probably want to hit pause, you know, go search Tim, go watch a video, read his Medium post, about the past, present, and future of Serverless. But Tim, I'm excited. You and I have talked in the past, but finally getting you on theCUBE program. >> Yeah! >> You know, I've looked through my career, and my background is infrastructure, and the role of infrastructure we know is always just to support the applications and the data that run business, that's what is important! Even when you talk about cloud, it is the applications, you know, the code, and the data that are important. So, it's not that, you know, okay I've got near infinite compute capacity, it's the new things that I can do with it. That's a comment I heard in one of your sessions. You talked about one of the most fascinating things about Serverless was just the new creativity that it inspired people to do, and I loved it wasn't just unlocking developers to say, okay I have new ways to write things, but even people that weren't traditional coders, like lots of people in marketing that were like, "I can start with this and build something new." So, I guess the question I have for you is, you know we had this idea of Platform as a Service, or even when things like containers launched, it was, we were trying to get close to that atomic unit of the application, and often it was talked about, well, do I want it for portability? Is it for ease of use? So, you've been wrangling and looking at this (Tim laughing) from a lot of different ways. So, is that as a starting point, you know, what did you see the last few years with Lambda, and you know, help connect this up to where Shruthi just left off her bit of the story. >> Absolutely. You know, the great story, the great success of the cloud is this elimination of undifferentiated heavy lifting, you know, from getting rid of having to build out a data center, to all the complexity of managing hardware. And that first wave of cloud adoption was just phenomenally successful at that. But as you say, the real thing businesses wrestle with are applications, right? It's ultimately about the business solution, not the hardware and software on which it runs. So, the very first time I sat down with Andy Jassy to talk about what eventually become Lambda, you know, one of the things I said was, look, if we want to get 10x the number of people to come and, you know, and be in the cloud and be successful it has to be 10 times simpler than it is today. You know, if step one is hire an amazing team of distributed engineers to turn a server into a full tolerance, scalable, reliable business solution, now that's going to be fundamentally limiting. We have to find a way to put that in a box, give that capability, you know, to people, without having them go hire that and build that out in the first place. And so that kind of started this journey for, for compute, we're trying to solve the problem of making compute as easy to use as possible. You know, take some code, as you said, even if you're not a diehard programmer or backend engineer, maybe you're just a full-stack engineer who loves working on the front-end, but the backend isn't your focus, turn that into something that is as scalable, as robust, as secure as somebody who has spent their entire career working on that. And that was the promise of Serverless, you know, outside of the specifics of any one cloud. Now, the challenge of course when you talk to customers, you know, is that you always heard the same two considerations. One is, I love the idea of Lamdba, but it's AWS, maybe I have multiple departments or business partners, or need to kind of work on multiple clouds. The other challenge is fantastic for compute, what about data? You know, you've kind of left me with, you're giving me sort of half the solution, you've made my compute super easy to use, can you make my data equally easy to use? And so you know, obviously the part of the genesis of Vendia is going and tackling those pieces of this, giving all that promise and ease of use of Serverless, now with a model for replicated state and data, and one that can cross accounts, machines, departments, clouds, companies, as easily as it scales on a single cloud today. >> Okay, so you covered quite a bit of ground there Tim, if you could just unpack that a little bit, because you're talking about state, cutting across environments. What is it that Vendia is bringing, how does that tie into solutions like, you know, Lamdba as you mentioned, but other clouds or even potentially on premises solutions? So, what is, you know, the IP, the code, the solution that Vendia's offering? >> Happy to! So, let's start with the customer problem here. The thing that every enterprise, every company, frankly, wrestles with is in the modern world they're producing more data than ever, IMT, digital journeys, you know, mobile, edge devices. More data coming in than ever before, at the same time, more data getting consumed than ever before with deep analytics, supply chain optimization, AI, ML. So, even more consumers of ever more data. The challenge, of course, is that data isn't always inside a company's four walls. In fact, we've heard 80% or more of that data actually lives outside of a company's control. So, step one to doing something like AI, ML, isn't even just picking a product or selecting a technology, it's getting all of your data back together again, so that's the problem that we set out to solve with Vendia, and we realized that, you know, and kind of part of the genesis for the name here, you know, Vendia comes from Venn Diagram. So, part of that need to bring code and data together across companies, across tech stacks, means the ability to solve some of these long-standing challenges. And we looked at the two sort of big movements out there. Two of them, you know, we've obviously both been involved in, one of them was Serverless, which amazing ability to scale, but single account, single cloud, single company. The other one is blockchain and distributed ledgers, manages to run more across parties, across clouds, across tech stacks, but doesn't have a great mechanism for scalability, it's really a single box deployment model, and obviously there are a lot of limitations with that. So, our technology, and kind of our insight and breakthrough here was bringing those two things together by solving the problems in each of them with the best parts of the other. So, reimagine the blockchain as a cloud data implementation built entirely out of Serverless components that have all of the scale, the cost efficiencies, the high utilization, like all of the ease of deployment that something like Lambda has today, and at the same time, you know, bring state to Serverless. Give things like Lambda and the equivalent of other clouds a simple, easy, built-in model so that applications can have multicloud, multi-account state at all times, rather than turning that into a complicated DIY project. So, that was our insight here, you know and frankly where a lot of the interesting technology for us is in turning those centralized services, a centralized version of Serverless Compute or Serverless Database into a multi-account, multicloud experience. And so that's where we spent a lot of time and energy trying to build something that gives customers a great experience. >> Yeah, so I've got plenty of background in customers that, you know, have the "information silos", if you will, so we know, when the unstructured data, you know so much of it is not searchable, I can't leverage it. Shruthi, but maybe it might make sense, you know, what is, would you say some of the top things some of your early customers are saying? You know, I have this pain point, that's pointing me in your direction, what was leading them to you? And how does the solution help them solve that problem? >> Yeah, absolutely! One of our design partners, our lead design partners is this automotive company, they're a premier automotive company, they want, their end goal is to track car parts for warranty recall issues. So, they want to track every single part that goes into a particular car, so they're about 30 to 35,000 parts in each of these cars, and then all the way from manufacturing floor to when the car is sold, and when that particular part is replaced eventually, towards the end of the lifecycle of that part. So for this, they have put together a small test group of their partners, a couple of the parts manufacturers, they're second care partners, National Highway Safety Administration is part of this group, also a couple of dealers and service centers. Now, if you just look at this group of partners, you will see some of these parties have high technology, technology backgrounds, just like the auto manufacturers themselves or the part manufacturers. Low modality or low IT-competency partners such as the service centers, for them desktop PCs are literally the IT competency, and so does the service centers. Now, most of, majority of these are on multiple clouds. This particular auto customer is on AWS and manufactures on Azure, another one is on GCP. Now, they all have to share these large files between each other, making sure that there are some transparency and business rules applicable. For example, two partners who make the same parts or similar parts cannot see each other's data. Most of the participants cannot see the PII data that are not applicable, only the service center can see that. National Highway Safety Administration has read access, not write access. A lot of that needed to be done, and their alternatives before they started using Vendia was either use point-to-point APIs, which was very expensive, very cumbersome, it works for a finite small set of parties, it does not scale, as in when you add more participants into this particular network. And the second option for them was blockchain, which they did use, and used Hyperledger Fabric, they used Ethereum Private to see how this works, but the scalability, with Ethereum Private, it's about 14 to 15 transactions per second, with Hyperledger Fabric it taps out at 100, or 150 on a good day, transaction through, but it's not just useful. All of these are always-on systems, they're not Serverless, so just provisioning capacity, our customers said it took them two to three weeks per participant. So, it's just not a scalable solution. With Vendia, what we delivered to them was this virtual data lake, where the sources of this data are on multiple clouds, are on multiple accounts owned by multiple parties, but all of that data is shared on a virtual data lake with all of the permissions, with all of the logging, with all of the security, PII, and compliance. Now, this particular auto manufacturer and the National Highway Safety Administration can run their ML algorithms to gain intelligence off of it, and start to understand patterns, so when certain parts go bad, or what's the propensity of a certain manufacturing unit producing faulty parts, and so on, and so forth. This really shows you this concept of unstructured data being shared between parties that are not, you know, connected with each other, when there are data silos. But I'd love to follow this up with another example of, you know, the democratization, democratization is very important to Vendia. When Tim launched Lambda and founded the AWS Serverless movement as a whole, and at AWS, one thing, very important thing happened, it lowered the barrier to entry for a new wave of businesses that could just experiment, try out new things, if it failed, they scrap it, if it worked, they could scale it out. And that was possible because of the entry point, because of the paper used, and the architecture itself, and we are, our vision and mission for Vendia is that Vendia fuels the next generation of multi-party connected distributed applications. My second design partner is actually a non-profit that, in the animal welfare industry. Their mission is to maintain a no-kill for dogs and cats in the United States. And the number one reason for over populations of dogs and cats in the shelters is dogs lost, dogs and cats lost during natural disasters, like the hurricane season. And when that happens, and when, let's say your dogs get lost, and you want to find a dog, the ID or the chip-reading is not reliable, they want to search this through pictures. But we also know that if you look at a picture of a dog, four people can come up with four different breed names, and this particular non-profit has 2,500 plus partners across the U.S., and they're all low to no IT modalities, some of them have higher IT competency, and a huge turnover because of volunteer employees. So, what we did for them was came up with a mechanism where they could connect with all 2,500 of these participants very easily in a very cost-effective way and get all of the pictures of all of the dogs in all these repositories into one data lake so they can run some kind of a dog facial recognition algorithm on it and identify where my lost dog is in minutes as opposed to days it used to take before. So, you see a very large customer with very sophisticated IT competency use this, also a non-profit being able to use this. And they were both able to get to this outcome in days, not months or years, as, blockchain, but just under a few days, so we're very excited about that. >> Thank you so much for the examples. All right, Tim, before we get to the end, I wonder if you could take us under the hood a little bit here. My understanding, the solution that you talk about, it's universal apps, or what you call "unis" -- >> Tim: Unis? (laughs) >> I believe, so if I saw that right, give me a little bit of compare and contrast, if you will. Obviously there's been a lot of interest in what Kubernetes has been doing. We've been watching closely, you know there's connections between what Kubernetes is doing and Serverless with the Knative project. When I saw the first video talking about Vendia, you said, "We're serverless, and we're containerless underneath." So, help us understand, because at, you know, a super high level, some of the multicloud and making things very flexible sound very similar. So you know, how is Vendia different, and why do you feel your architecture helps solve this really challenging problem? >> Sure, sure, awesome! You know, look, one of the tenets that we had here was that things have to be as easy as possible for customers, and if you think about the way somebody walks up today to an existing database system, right? They say, "Look, I've got a schema, I know the shape of my data." And a few minutes later I can get a production database, now it's single user, single cloud, single consumer there, but it's a very fast, simple process that doesn't require having code, hiring a team, et cetera, and we wanted Vendia to work the same way. Somebody can walk up with a JSON schema, hand it to us, five minutes later they have a database, only now it's a multiparty database that's decentralized, so runs across multiple platforms, multiple clouds, you know, multiple technology stacks instead of being single user. So, that's kind of goal one, is like make that as easy to use as possible. The other key tenet though is we don't want to be the least common denominator of the cloud. One of the challenges with saying everyone's going to deploy their own servers, they're going to run all their own software, they're going to build, you know, they're all going to co-deploy a Kubernetes cluster, one of the challenges with that is that, as Shruthi was saying, first, anyone for whom that's a challenge, if you don't have a whole IT department wrapped around you that's a difficult proposition to get started on no matter how amazing that technology might be. The other challenge with it though is that it locks you out, sort of the universe of a lock-in process, right, is the lock-out process. It locks you out of some of the best and brightest things the public cloud providers have come up with, and we wanted to empower customers, you know, to pick the best degree. Maybe they want to go use IBM Watson, maybe they want to use a database on Google, and at the same time they want to ingest IoT on AWS, and they wanted all to work together, and want all of that to be seamless, not something where they have to recreate an experience over, and over, and over again on three different clouds. So, that was our goal here in producing this. What we designed as an architecture was decentralized data storage at the core of it. So, think about all the precepts you hear with blockchain, they're all there, they all just look different. So, we use a no SQL database to store data so that we can scale that easily. We still have a consensus algorithm, only now it's a high speed serverless and cloud function based mechanism. You know, instead of smart contracts, you write things in a cloud function like Lambda instead, so no more learning Solidity, now you can use any language you want. So, we changed how we think about that architecture, but many of those ideas about people, really excited about blockchain and its capabilities and the vision for the future are still alive and well, they've just been implemented in a way that's far more practical and effective for the enterprise. >> All right, so what environments can I use today for your solution, Shruthi talked about customers spanning across some of the cloud, so what's available kind of today, what's on the roadmap in the future? Will this include beyond, you know, maybe the top five or six hyper scalers? Can I do, does it just require Serverless underneath? So, will things that are in a customer's own data center eventually support that. >> Absolutely. So, what we're doing right now is having people sign up for our preview release, so in the next few weeks, we're going to start turning that on for early access to developers. That'll be, the early access program, will be multi-account, focused on AWS, and then end of summer, well be doing our GA release, which will be multicloud, so we'll actually be able to operate across multiple clouds, multiple cloud services, on different platforms. But even from day one, we'll have API support in there. So, if you got a service, could even be running on a mainframe, could be on-prem, if it's API based you can still interact with the data, and still get the benefits of the system. So, developers, please start signing up, you can go find more information on vendia.net, and we're really looking forward to getting some of that early feedback and hear more from the people that we're the most excited to have start building these projects. >> Excellent, what a great call to action to get the developers and users in there. Shruthi, if you could just give us the last bit, you know, the thing that's been fascinating, Tim, when I look at the Serverless movement, you know, I've talked to some amazing companies that were two or three people (Tim laughing) and out of their basement, and they created a business, and they're like, "Oh my gosh, I got VC funding, and it's usually sub $10,000,000. So, I look at your team, I'd heard, Tim, you're the primary coder on the team. (Tim laughing) And when it comes to the seed funding it's, you know, compared to many startups, it's a small number. So, Shruthi, give us a little bit if you could the speeds and feeds of the company, your funding, and any places that you're hiring. Yeah, we are definitely hiring, lets me start from there! (Tim laughing) We're hiring for developers, and we are also hiring for solution architects, so please go to vendia.net, we have all the roles listed there, we would love to hear from you! And the second one, funding, yes. Tim is our main developer and solutions architect here, and look, the Serverless movement really helped quite a few companies, including us, to build this, bring this to market in record speeds, and we're very thankful that Tim and AWS started taking the stands, you know back in 2014, 2013, to bring this to market and democratize this. I think when we brought this new concept to our investors, they saw what this could be. It's not an easy concept to understand in the first wave, but when you understand the problem space, you see that the opportunity is pretty endless. And I'll say this for our investors, on behalf of our investors, that they saw a real founder market-fit between us. We're literally the two people who have launched and ran businesses for both Serverless and blockchain at scale, so that's what they thought was very attractive to them, and then look, it's Tim and I, and we're looking to hire 8 to 10 folks, and I think we have gotten to a space where we're making a meaningful difference to the world, and we would love for more people to join us, join this movement and democratize this big dispersed data problem and solve for this. And help us create more meanings to the data that our customers and companies worldwide are creating. We're very excited, and we're very thankful for all of our investors to be deeply committed to us and having conviction on us. >> Well, Shruthi and Tim, first of all, congratulations -- >> Thank you, thank you. >> Absolutely looking forward to, you know, watching the progress going forward. Thanks so much for joining us. >> Thank you, Stu, thank you. >> Thanks, Stu! >> All right, and definitely tune in to our regular conversations on Cloud Native Insights. I'm your host Stu Miniman, and looking forward to hearing more about your Cloud Native Insights! (upbeat electronic music)

Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> We're back. You're watching theCUBE and our coverage of IBM Think 2020, the digital IBM thinking. We're here with Jamie Thomas, who's the general manager of strategy and development for IBM Systems. Jamie, great to see you. >> It's great to see you as always. >> You have been knee deep in qubits, the last couple years. And we're going to talk quantum. We've talked quantum a lot in the past, but it's a really interesting field. We spoke to you last year at IBM Think about this topic. And a year in this industry is a long time, but so give us the update what's new in quantum land? >> Well, Dave first of all, I'd like to say that in this environment we find ourselves in, I think we can all appreciate why innovation of this nature is perhaps more important going forward, right? If we look at some of the opportunities to solve some of the unsolvable problems, or solve problems much more quickly, in the case of pharmaceutical research. But for us in IBM, it's been a really busy year. First of all, we worked to advance the technology, which is first and foremost in terms of this journey to quantum. We just brought online our 53 qubit computer, which also has a quantum volume of 32, which we can talk about. And we've continued to advance the software stack that's attached to the technology because you have to have both the software and the hardware thing, right rate and pace. We've advanced our new network, which you and I have spoken about, which are those individuals across the commercial enterprises, academic and startups, who are working with us to co-create around quantum to help us understand the use cases that really can be solved in the future with quantum. And we've also continued to advance our community, which is serving as well in this new digital world that we're finding ourselves in, in terms of reaching out to developers. Now, we have over 300,000 unique downloads of the programming model that represents the developers that we're touching out there every day with quantum. These developers have, in the last year, have run over 140 billion quantum circuits. So, our machines in the cloud are quite active, and the cloud model, of course, is serving us well. The data's, in addition, to all the other things that I mentioned. >> So Jamie, what metrics are you trying to optimize on? You mentioned 53 qubits I saw that actually came online, I think, last fall. So you're nearly six months in now, which is awesome. But what are you measuring? Are you measuring stability or coherence or error rates? Number of qubits? What are the things that you're trying to optimize on to measure progress? >> Well, that's a good question. So we have this metric that we've defined over the last year or two called quantum volume. And quantum volume 32, which is the capacity of our current machine really is a representation of many of the things that you mentioned. It represents the power of the quantum machine, if you will. It includes a definition of our ability to provide error correction, to maintain states, to really accomplish workloads with the computer. So there's a number of factors that go into quantum volume, which we think are important. Now, qubits and the number of qubits is just one such metric. It really depends on the coherence and the effect of error correction, to really get the value out of the machine, and that's a very important metric. >> Yeah, we love to boil things down to a single metric. It's more complicated than that >> Yeah, yeah. >> specifically with quantum. So, talk a little bit more about what clients are doing and I'm particularly interested in the ecosystem that you're forming around quantum. >> Well, as I said, the ecosystem is both the network, which are those that are really intently working with us to co-create because we found, through our long history in IBM, that co-creation is really important. And also these researchers and developers realize that some of our developers today are really researchers, but as you as you go forward you get many different types of developers that are part of this mix. But in terms of our ecosystem, we're really fundamentally focused on key problems around chemistry, material science, financial services. And over the last year, there's over 200 papers that have been written out there from our network that really embody their work with us on this journey. So we're looking at things like quadratic speed up of things like Monte Carlo simulation, which is used in the financial services arena today to quantify risk. There's papers out there around topics like trade settlements, which in the world today trade settlements is a very complex domain with very interconnected complex rules and trillions of dollars in the purview of trade settlement. So, it's just an example. Options pricing, so you see examples around options pricing from corporations like JPMC in the area of financial services. And likewise in chemistry, there's a lot of research out there focused on batteries. As you can imagine, getting everything to electric powered batteries is an important topic. But today, the way we manufacture batteries can in fact create air pollution, in terms of the process, as well as we want batteries to have more retention in life to be more effective in energy conservation. So, how do we create batteries and still protect our environment, as we all would like to do? And so we've had a lot of research around things like the next generation of electric batteries, which is a key topic. But if you can think, you know Dave, there's so many topics here around chemistry, also pharmaceuticals that could be advanced with a quantum computer. Obviously, if you look at the COVID-19 news, our supercomputer that we installed at Oak Ridge National Laboratory for instance, is being used to analyze 8000 different compounds for specifically around COVID-19 and the possibilities of using those compounds to solve COVID-19, or influence it in a positive manner. You can think of the quantum computer when it comes online as an accelerator to a supercomputer like that, helping speed up this kind of research even faster than what we're able to do with something like the Summit supercomputer. Oak Ridge is one of our prominent clients with the quantum technology, and they certainly see it that way, right, as an accelerator to the capacity they already have. So a great example that I think is very germane in the time that we find ourselves in. >> How 'about startups in this ecosystem? Are you able to-- I mean there must be startups popping up all over the place for this opportunity. Are you working with any startups or incubating any startups? Can you talk about that? >> Oh yep. Absolutely. There's about a third of our network are in VC startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of 'em are focused on what I would call loosely, the programming model, looking at improving algorithms across different industries, making it easier for those that are, perhaps more skilled in domains, whether that is chemistry or financial services or mathematics, to use the power of the quantum computer. Many of those startups are leveraging our Qiskit, our quantum information science open programming model that we put out there so it's open. Many of the startups are using that programming model and then adding their own secret sauce, if you will, to understand how they can help bring on users in different ways. So it depends on their domain. You see some startups that are focused on the hardware as well, of course, looking at different hardware technologies that can be used to solve quantum. I would say I feel like more of them are focused on the software programming model. >> Well Jamie, it was interesting hear you talk about what some of the clients are doing. I mean obviously in pharmaceuticals, and battery manufacturers do a lot of advanced R and D, but you mentioned financial services, you know JPMC. It's almost like they're now doing advanced R and D trying to figure out how they can apply quantum to their business down the road. >> Absolutely, and we have a number of financial institutions that we've announced as part of the network. JPMC is just one of our premiere references who have written papers about it. But I would tell you that in the world of Monte Carlo simulation, options pricing, risk management, a small change can make a big difference in dollars. So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. The ability to use quantum as an accelerator for these kind of operations is very important. And I can tell you, even in the last few weeks, we've had a number of briefings with financial companies for five hours on this topic. Looking at what could they do and learning from the work that's already done out there. I think this kind of advanced research is going to be very important. We also had new members that we announced at the beginning of the year at the CES show. Delta Airlines joined. First Transportation Company, Amgen joined, a pharmaceutical, an example of pharmaceuticals, as well as a number of other research organizations. Georgia Tech, University of New Mexico, Anthem Insurance, just an example of the industries that are looking to take advantage of this kind of technology as it matures. >> Well, and it strikes me too, that as you start to bring machine intelligence into the equation, it's a game changer. I mean, I've been saying that it's not Moore's Law driving the industry anymore, it's this combination of data, AI, and cloud for scale, but now-- Of course there are alternative processors going on, we're seeing that, but now as you bring in quantum that actually adds to that innovation cocktail, doesn't it? >> Yes, and as you recall when you and I spoke last year about this, there are certain domains today where you really cannot get as much effective gain out of classical computing. And clearly, chemistry is one of those domains because today, with classical computers, we're really unable to model even something as simple as a caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. (laughs) But you know, clearly, to the degree we can actually apply molecular modeling and the advantages that quantum brings to those fields, we'll be able to understand so much more about materials that affect all of us around the world, about energy, how to explore energy, and create energy without creating the carbon footprint and the bad outcomes associated with energy creation, and how to obviously deal with pharmaceutical creation much more effectively. There's a real promise in a lot of these different areas. >> I wonder if you could talk a little bit about some of the landscape and I'm really interested in what IBM brings to the table that's sort of different. You're seeing a lot of companies enter this space, some big and many small, what's the unique aspect that IBM brings to the table? You've mentioned co-creating before. Are you co-creating, coopertating with some of the other big guys? Maybe you could address that. >> Well, obviously this is a very hot topic, both within the technology industry and across government entities. I think that some of the key values we bring to the table is we are the only vendor right now that has a fleet of systems available in the cloud, and we've been out there for several years, enabling clients to take advantage of our capacity. We have both free access and premium access, which is what the network is paying for because they get access to the highest fidelity machines. Clearly, we understand intently, classical computing and the ability to leverage classical with quantum for advantage across many of these different industries, which I think is unique. We understand the cloud experience that we're bringing to play here with quantum since day one, and most importantly, I think we have strong relationships. We have, in many cases, we're still running the world. I see it every day coming through my clients' port vantage point. We understand financial services. We understand healthcare. We understand many of these important domains, and we're used to solving tough problems. So, we'll bring that experience with our clients and those industries to the table here and help them on this journey. >> You mentioned your experience in sort of traditional computing, basically if I understand it correctly, you're still using traditional silicon microprocessors to read and write the data that's coming out of quantum. I don't know if they're sitting physically side by side, but you've got this big cryogenic unit, cables coming in. That's the sort of standard for some time. It reminds me, can it go back to ENIAC? And now, which is really excites me because you look at the potential to miniaturize this over the next several decades, but is that right, you're sort of side by side with traditional computing approaches? >> Right, effectively what we do with quantum today does not happen without classical computers. The front end, you're coming in on classical computers. You're storing your data on classical computers, so that is the model that we're in today, and that will continue to happen. In terms of the quantum processor itself, it is a silicon based processor, but it's a superconducting technology, in our case, that runs inside that cryogenics unit at a very cold temperature. It is powered by next-generation electronics that we in IBM have innovated around and created our own electronic stack that actually sends microwave pulses into the processor that resides in the cryogenics unit. So when you think about the components of the system, you have to be innovating around the processor, the cryogenics unit, the custom electronic stack, and the software all at the same time. And yes, we're doing that in terms of being surrounded by this classical backplane that allows our Q network, as well as the developers around the world to actually communicate with these systems. >> The other thing that I really like about this conversation is it's not just R and D for the sake of R and D, you've actually, you're working with partners to, like you said, co-create, customers, financial services, airlines, manufacturing, et cetera. I wonder if you could maybe kind of address some of the things that you see happening in the sort of near to midterm, specifically as it relates to where people start. If I'm interested in this, what do I do? Do I need new skills? Do I need-- It's in the cloud, right? >> Yeah. >> So I can spit it up there, but where do people get started? >> Well they can certainly come to the Quantum Experience, which is our cloud experience and start to try out the system. So, we have both easy ways to get started with visual composition of circuits, as well as using the programming model that I mentioned, the Qiskit programming model. We've provided extensive YouTube videos out there already. So, developers who are interested in starting to learn about quantum can go out there and subscribe to our YouTube channel. We've got over 40 assets already recorded out there, and we continue to do those. We did one last week on quantum circuits for those that are more interested in that particular domain, but I think that's a part of this journey is making sure that we have all the assets out there digitally available for those around the world that want to interact with us. We have tremendous amount of education. We're also providing education to our business partners. One of our key network members, who I'll be speaking with later, I think today, is from Accenture. Accenture's an example of an organization that's helping their clients understand this quantum journey, and of course they're providing their own assets, if you will, but once again, taking advantage of the education that we're providing to them as a business partner. >> People talk about quantum being a decade away, but I think that's the wrong way to think about it, and I'd love your thoughts on this. It feels like, almost like the return coming out of COVID-19, it's going to come in waves, and there's parts that are going to be commercialized thoroughly and it's not binary. It's not like all of a sudden one day we're going to wake, "Hey, quantum is here!" It's really going to come in layers. Your thoughts? >> Yeah, I definitely agree with that. It's very important, that thought process because if you want to be competitive in your industry, you should think about getting started now. And that's why you see so many financial services, industrial firms, and others joining to really start experimentation around some of these domain areas to understand jointly how we evolve these algorithms to solve these problems. I think that the production level characteristics will curate the rate and pace of the industry. The industry, as we know, can drive things together faster. So together, we can make this a reality faster, and certainly none of us want to say it's going to be a decade, right. I mean, we're getting advantage today, in terms of the experimentation and the understanding of these problems, and we have to expedite that, I think, in the next few years. And certainly, with this arms race that we see, that's going to continue. One of the things I didn't mention is that IBM is also working with certain countries and we have significant agreements now with the countries of Germany and Japan to put quantum computers in an IBM facility in those countries. It's in collaboration with Fraunhofer Institute or miR Scientific Organization in Germany and with the University of Tokyo in Japan. So you can see that it's not only being pushed by industry, but it's also being pushed from the vantage of countries and bringing this research and technology to their countries. >> All right, Jamie, we're going to have to leave it there. Thanks so much for coming on theCUBE and give us the update. It's always great to see you. Hopefully, next time I see you, it'll be face to face. >> That's right, I hope so too. It's great to see you guys, thank you. Bye. >> All right, you're welcome. Keep it right there everybody. This is Dave Vellante for theCUBE. Be back right after this short break. (gentle music)

[Music] from the cube studios in Palo Alto in Boston it's the cube covering the IBM thing brought to you by IBM welcome back everybody you're watching the cube and our continuous coverage of IBM think Digital 20/20 events it's we've been wall-to-wall for a couple days now and and we bring in you all the action a bass fall is here here he is the global league for quantum education and open science at IBM quantum gave great to see you thanks for coming on yeah thanks for having me here Dave you're very welcome love the discussion on quantum but I gotta say so I'm reading your bio in your bio I see quantum algorithms experimental quantum computation nanoscale device fabrication cryogenic measurements and quantum software development hardware programming etc so you're obviously qualified to talk about quantum but but how how can somebody learn about quantum do I have to be like a rocket scientist then understand this stuff so Dave this is one of the things that I'm very passionate about it's also my job to make sure that anyone can learn about quantum computing today so primarily what I'm focused on is making sure that you don't need a PhD to program a quantum computer when I was going through my graduate studies trying to learn quantum computing I needed access to a lab so I have to go to graduate school to do this but in 2016 IBM put a quantum computer on the cloud in that dramatically changes the field it allows access to anyone from the world with just an internet connection to program a quantum computer so the question I'm trying to answer on a daily basis now is the question that you asked how do I learn to program a quantum computer well I'm trying to make several resources available for you to do that okay well let's talk about those resources I mean you have quantum you have access to quantum computers I talked to Jamie Thomas the other day she said that you guys it's all available in the IBM cloud I can't even I can't even imagine what the infrastructure behind that looks like but as a user I don't have to see that so how do I get access to this stuff so there are several quantum computers available on the cloud now and every time I think about this it's fascinating to me because I needed access to a lab to access these things but now you don't you can go to quantum computing dot ibm.com and get free access to several quantum computers now the question becomes if I give you this access to the quantum computers how do you learn to program them the software that you use to program them is called kiss kit just like we've made access to the quantum computers open for everyone our software is also open source you can access it by going to Kiska torgue that's QIS ki t org and if you go in particular to Kiska org slash education we've put together a textbook to help you go through everything that you'd learn in a classroom about quantum algorithms and to start programming the real quantum systems yourself so everything's ready for you to program immediately what was the it can you give me the quantity IBM want them - computing URL again yeah that's quantum - computing IBM com once you create an account there you immediately get access to several quantum computers which is an impressive thing to think about the cryogenics that you mentioned earlier the hardware the software all of it is ready for you to take advantage of but I gotta ask you I know it's sort of off topic here but but if I had to look under the covers I'm gonna see some big cryogenic unit with a bunch of cables coming in is that right that's exactly it very cold inside that's right so the way to here's the way to think about it outer space is about 200 times colder than room temperature and the temperature where the chip the quantum chips it's is another 200 times lower than that so we're talking very cold here we're talking only 15 Mille kelvins above absolute zero that's zero point zero one five degrees above absolute zero so it's a very cold system and you'd have several wires that are going down into this coil system to try to communicate with the quantum ship well and what's exciting to me about this whole thing Abe is it is it brings me back to the sort of the early days of computing and the you know huge rooms and now look where we are today and so I would expect that over the next many decades you're going to see sort of similar advanced advances in quantum and being able to actually execute at somewhat higher temperatures and in miniaturization it's very exciting time and we're really obviously at the very very early innings but I want to ask you just in terms of if if I'm a programmer and I'm a Java programmer can I actually come in and start using quantum if you what do I need to know to get started so you need to know two things the first thing is you need to be familiar with any programming language the easiest programming language to pick up today by far is Python so kiss kit is built based on Python so if you're able to quickly catch up with a few things in Python and we have a chapter dedicated to this topic in our textbook that's the first thing the second thing is simply having the ability to learn something new simply being excited about this field once you have those two together you can learn quantum computing very quickly within a few months the question then becomes catching up with the research and reading research papers that can take some time but for us to be able to talk through a quantum program takes only a few a few days of reading let's talk about what some of the folks are doing with quantum we talked again to Jamie Thomas and she gave me some examples not surprisingly you know you saw for instance some some examples in pharmaceutical and to the other obvious industries but then banking came in it's a but what what is it what are people doing with quantum today maybe you could add some color to that primarily most of the working quantum today is focused on understanding how to take problems in industry whether it is to understand how to simulate molecules whether it is to understand how to optimize a financial portfolio taking those problems and mapping them onto a quantum computer so that they can get solved so you'll see various various industries exploring how to take their problems and map onto a quantum computer so one one exciting one that I'm seeing a lot of progress in is chemistry learning how to simulate molecules using these quantum computers as someone with a physics background for me the exciting thing to see here is also how people are using these quantum computers which fundamentally are taking advantage of quantum mechanics to simulate other quantum systems so to understand nature better by using nature itself so this is another exciting progress that we're seeing in the field so exciting both from industry and from educational and science purpose so obviously it's a fascinating field and people would you say with curiosity it can get excited about it but but let's say I actually want you know some some kind of career in part of I mean what well how would people sort of get involved do you see you know on the horizon that this is gonna be something that is actually gonna be a vocation for you know young folks that want to get involved I could not tell you how challenging it is to find people who have the right combination of quantum computing knowledge and classical programming knowledge so in order to be able to take full advantage of the quantum systems today we need people who understand both the hardware and the software to some level and there is an extreme shortage of that kind of talent so the work that I'm focused on is exactly this problem of solving the workforce development problem so we're trying to make sure that people have access to anything that they need in order to be able to program a quantum computer and to learn how to then map their own problems into these quantum computers in the future the question becomes let's say we now understand how to use quantum computers to make financial portfolio optimization every bank in the world is going to want someone to implement this in their systems which immediately creates lots of jobs so this is going to become something that's in demand once it becomes possible on a on a large quantum computer so today is the right time to learn how to work with these quantum systems so that when the time comes that there are industries that are needing quantum skills you're ready to be hired for those positions okay so big skills gap you kind of gave an example in financial services where maybe some of the other things that you hope that that people are going to be able to do over time with these skills I cannot under I cannot over us overstate how important it is to learn how to simulate chemistry problems on these quantum computers that will have impacts anywhere ranging from whether it's drug design whether it's making better efficient solar panels more efficient batteries there are many applications where you'll see impact from these so the there are many industries that can benefit from understanding how to work with quantum computers that's something exciting I'm looking forward to see you know you read in the press that you know we're at least a decade away you know from from quantum being a reality but you're giving some examples where it's sort of here today I feel like it's going to come in layers you know not gonna be one big bang it's gonna come over time but but maybe you could you know frame that for us in terms of how you see this market developing I don't even want to call it a market but just this technology developing into a market what what has to take place and what kind of things can we expect along that journey sure so I think it's very important to keep in mind that quantum computers are fairly young technology so we're improving the technology as we go and there has been dramatic improvement in the technology itself but we're still learning as we go so one of the things that you'll find is that all of the applications work that's being done today is exploring how to take advantage of the quantum computer in some way if I immediately gave you a fully functional perfect quantum computer today you wouldn't even know what to do with it right you need to understand how to map problems on to that quantum computer so in preparation for that time several years away you'll see a lot of people trying to learn how to take advantage of quantum computers today and as they get better and better learning how to take advantage of whatever incremental progress is being made so as much as it seems like quantum computers are several years away many people are learning how to program them today just in preparation for that time when they're ready for use and my understanding is we're gonna get there with you know hybrid models today you're using you know traditional microprocessor technology to sort of read and write data from quantum that's likely going to continue for quite some time maybe maybe indefinitely but but but perhaps not right so Dave the important thing to remember is that a quantum computer works jointly with a classical computer if you ask me the question of how do i optimize my portfolio the numbers that I would need to compute with our classical there's nothing quantum about them these are numbers so there's classical information that you then have to take and map on to the quantum computer and then once the quantum computer is done you have to take the data out of that computer and then turn it back into classical information so you'll always have a quantum computer working jointly with a classical computer the question now is how do you make those two work together so that you can extract some benefit that you couldn't have attained with just the classic what do you see is the big sort of technical challenges that you're paying attention to you paying attention to I mean is it getting more you know qubits is a coherence working at higher temperatures what are the things that you see is as the the scientists are working on to move things forward so one of the things that I can do immediately Dave if you and I agreed right now is we can go to the lab and take a quantum chip and put a thousand cubits on that quantum chip that's fine we can do that immediately the problem that you'll find is that it doesn't matter that you have a thousand cubits if the qubits are not good quality cuteness so the technology should focus on improving the fundamental qualities of the qubits themselves before scaling them up to larger numbers in addition to that as you're scaling to larger and larger numbers new problems come into the picture so making better qubits scaling up seeing how the technology is doing learning new things and then scaling farther up that seems to be the model that's working today so in addition to monitoring the quality of the qubits themselves I'm monitoring within the technology how people are implementing solutions to scaling problems in addition to that another important problem that deserves a lot of attention is the question of how do you make good software that can take problems and map them onto quantum computers in in quantum computing when I say I'm running upon a program really what I'm doing is building a quantum circuit and then running that quantum circuit on the real device well if that circuit has certain operations in it maybe you want to tailor the way you transfer that circuit onto the device in a way that takes full advantage of the device itself but then in order to do that you need to write good software so improvements in the software along with improvements in the quantum technology itself will be how we get to success and at IBM we're focused on finding a metric that wraps all of these things together and it's called quantum volume and we're seeing improvements in the quantum volume of our systems as we go yeah Jamie talked about that you're essentially taking the key metrics and putting them into a you know a single observable metric that obviously you can track over time so I want to ask you about security a lot of people are concerned that the quantum is just going to blow away everything that we know cryptography and all the you know the the passwords and security systems that we we've put in place is that a legitimate concern will quantum you both get us into that problem and take us out of that that problem I wonder if you could talk about that so there are two ways to think about this problem one is just fundamentally if you ask me what does it take to put the the cryptography that has our bank accounts safe over the internet connections that we use it takes roughly about a thousand good cubits okay if I tell you a thousand good cubits that doesn't seem like a lot of work but when you think about it what it really requires is an overhead of about a thousand cubits for each qubit that we have today so the numbers of qubits that you need are in the millions in order to put the the kind of cryptography that we're using today at stake so certainly there's a long way to go that's one aspect of the story the other aspect of the story is that we should never underestimate the progress of technology so even though the time when we can use Shor's algorithm which is the algorithm that can be used to break the cryptographic algorithms like RSA even though that's several years away you still want to be ready for that time and what that means is if you have sensitive information today you need to be making sure that that information itself is protected with quantum resistant cryptographic techniques so that when the time comes you can't use a quantum computer to get back the data from today and break so two perspectives one is we're quite a while away from this kind of danger but at the same time it doesn't mean we should be complacent today we should be taking preparations make sure that our critical information is protected yeah that's so that that makes a lot of sense but when you say we're a ways away or we are we decades away we years away we can you and you quantify that in any reasonable way it's hard to speculate on that number so I'll refrain from giving you a specific timeline just to give you an idea the quantum bits that were in development ten years ago had a coherence time so the amount of time that they can store the quantum information of roughly a hundred times smaller than they are today and ten years ago if you asked people how do we get to a hundred times better qubits nobody would have been able to give you a clear answer you could have guessed some ways but nobody would have been able to tell you we'll get there in ten years but we did so instead of coming up with estimates of timelines that depend on what we know today it's probably a better idea to monitor the technology as it goes and keep adapting we're probably talking this century where we're talking to the century hopefully it is my last mission to enable enough people to learn quantum such that it happens within my life very exciting field a I can't thank you enough for helping us educate the audience and and my and myself personally really I'm I'm so fascinated by this it's something that you know jumper and I and the team have been really focused on and I think it's really time to your point the start digging and start learning you've given us some resources there give us give them give us those two reasons one more time there's there's the IBM site and the the the the the queue kit site use that site what are those again just those to wrap so you can access the quantum computers at quantum - computing ibm.com and once you're there the way to learn how to program these quantum computers is by using kiss kit which you can learn about by going to kiss kit org slash education once here at that education page you can access our textbook which we make open-source it's a textbook that's co-written with professors in the field and is open source so it's continually getting updated you can access that textbook at tisket org slash textbook if you go to our youtube channel you'll find several videos that allow you to also learn very quickly so kiss gets YouTube channel is another great place to look so lots of resources and that's kiss kit with a Q which is why I wrote it that way so alright exact thanks so much it was great to see you stay safe and next time hopefully we'll see you face-to-face and you can draw some some cool pictures to help me understand this even better Dave it was nice talking with you I look forward to learning quantum programming with you yeah Cheers and thank you for watching everybody this is the cubes coverage of the IBM think 2020 digital event experience we'll be right back Brennan for this short break [Music] you

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. Here live in Las Vegas for aws reinvent I'm John for a devil on the ads, always extracting the signal from the noise. We're here for 1/7 reinvent of the eight years that they've had at what a wave. One of the biggest waves is the modernization of procurement, the modernization of business, commercial business and the rapid acceleration of public sector. We're here with the chief of public sector for AWS. Teresa Carlson, vice president publics that globally great to have you >>so great to have the Q begin this year. We appreciate you being here, >>so we're just seeing so much acceleration of modernization. Even in the commercial side, 80 talks about transformation. It's just a hard core on the public sector side. You have so many different areas transforming faster because they haven't transformed before. That's correct. This is a lot of change. What's changed the most for you in your business? >>Well, again, I'll be here 10 years this mad that A B s and my eighth reinvent, and what really changed, which was very exciting this year, is on Monday. We had 550 international government executives here from 40 countries who were talking about their modernization efforts at every level. Now again, think about that. 40 different governments, 550 executives. We had a fantastic day for them planned. It was really phenomenal because the way that these international governments or think about their budget, how much are they going to use that for maintaining? And they want to get that lesson last. Beckett for Modernization The Thin John It's a Beckett for innovation so that they continue not only modernized, but they're really looking at innovation cycles. So that's a big one. And then you heard from somewhere customers at the breakfast this morning morning from from a T. F. As part of the Department of Justice. What they're doing out. I'll call to back on firearms. They completely made you the cloud. They got rid of 20 years of technical debt thio the Veterans Administration on what they're digging for V A benefits to educational institutions like our mighty >>nose, and he had on stages Kino, Cerner, which the health care companies and what struck me about that? I think it relates to your because I want to get your reaction is that the health care is such an acute example that everyone can relate to rising costs. So cloud helping reduce costs increase the efficiencies and patient care is a triple win. The same thing happens in public sector. There's no place to hide anymore. You have a bona fide efficiencies that could come right out of the gate with cloud plus innovation. And it's happening in all the sectors within the public sector. >>So true. Well, Cerner is a great example because they won the award at V a Veteran's administration to do the whole entire medical records modernization. So you have a company on stage that's commercial as I met, commercial as they are public sector that are going into these large modernization efforts. And as you sit on these air, not easy. This takes focus and leadership and a real culture change to make these things happen. >>You know, the international expansion is impressive. We saw each other in London. We did the health care drill down at your office is, of course, a national health. And then you guys were in Bahrain, and what I deserve is it's not like these organizations. They're way behind. I mean, especially the ones that it moved to. The clouds are moving really fast. So well, >>they don't have as much technical debt internationally. It's what we see here in the U. S. So, like I was just in Africa and you know what we talked about digitizing paper. Well, there's no technology on that >>end >>there. It's kind of exciting because they can literally start from square one and get going. And there's a really hunger and the need to make that happen. So it's different for every country in terms of where they are in their cloud journey. >>So I want to ask you about some of the big deals. I'll see Jet eyes in the news, and you can't talk about it because it's in protest and little legal issues. But you have a lot of big deals that you've done. You share some color commentary on from the big deals and what it really means. >>Yeah, well, first of all, let me just say with Department of Defense, Jet are no jet. I We have a very significant business, you know, doing work at every part of different defense. Army, Navy, Air Force in the intelligence community who has a mission for d o d terminus a t o N g eight in a row on And we are not slowing down in D. O d. We had, like, 250 people at a breakfast. Are Lantian yesterday giving ideas on what they're doing and sharing best practices around the fence. So we're not slowing down in D. O d. We're really excited. We have amazing partners. They're doing mission work with us. But in terms of some really kind of fend, things have happened. We did a press announcement today with Finn Rat, the financial regulatory authority here in the U. S. That regulates markets at this is the largest financial transactions you'll ever see being processed and run on the cloud. And the program is called Cat Consolidated Audit Trail. And if you remember the flash crash and the markets kind of going crazy from 2000 day in 2008 when it started, Finneran's started on a journey to try to understand why these market events were happening, and now they have once have been called CAT, which will do more than 100 billion market points a day that will be processed on the cloud. And this is what we know of right now, and they'll be looking for indicators of nefarious behavior within the markets. And we'll look for indicators on a continuous basis. Now what? We've talked about it. We don't even know what we don't know yet because we're getting so much data, we're going to start processing and crunching coming out of all kinds of groups that they're working with, that this is an important point even for Finn rep. They're gonna be retiring technical debt that they have. So they roll out Cat. They'll be retiring other systems, like oats and other programs that they >>just say so that flash crash is really important. Consolidated, honest, because the flash crash, we'll chalk it up to a glitch in the system. Translation. We don't really know what happened. Soto have a consolidated auto trail and having the data and the capabilities, I understand it is really, really important for transparency and confidence in the >>huge and by the way, thinner has been working with us since 2014. They're one of our best partners and are prolific users of the cloud. And I will tell you it's important that we have industries like thin red regulatory authorities, that air going in and saying, Look, we couldn't possibly do what we're doing without cloud computing. >>Tell me about the technical debt because I like this conversation is that we talk about in the commercial side and developer kind of thinking. Most businesses start ups, Whatever. What is technical debt meet in public sector? Can you be specific? >>Well, it's years and years of legacy applications that never had any modernization associated with them in public sector. You know now, because you've talked about these procurement, your very best of your very savvy now public sector >>like 1995 >>not for the faint of heart, for sure that when you do procurement over the years when they would do something they wouldn't build in at new innovations or modernizations. So if you think about if you build a data center today a traditional data center, it's outdated. Tomorrow, the same thing with the procurement. By the time that they delivered on those requirements. They were outdated. So technical debt then has been built up years of on years of not modernizing, just kind of maintaining a status quo with no new insides or analytics. You couldn't add any new tooling. So that is where you see agencies like a T F. That has said, Wow, if I'm gonna if I'm gonna have a modern agency that tracks things like forensics understands the machine learning of what's happening in justice and public safety, I need to have the most modern tools. And I can't do that on an outdated system. So that's what we kind of call technical death that just maintains that system without having anything new that you're adding to >>their capabilities lag. Everything's products bad. Okay, great. Thanks for definite. I gotta ask you about something that's near and dear to our heart collaboration. If you look at the big successes in the world and within Amazon Quantum Caltex partnering on the quantum side, you've done a lot of collaboration with Cal Cal Poly for ground station Amazon Educate. You've been very collaborative in your business, and that's a continuing to be a best practice you have now new things like the cloud innovation centers. Talk about that dynamic and how collaboration has become an important part of your business model. >>What we use their own principles from Amazon. We got building things in our plan. Innovation centers. We start out piloting those two to see, Could they work? And it's really a public private partnership between eight MPs and universities, but its universities that really want to do something. And Cal Poly's a great example. Arizona State University A great example. The number one most innovative university in the US for like, four years in a row. And what we do is we go in and we do these public sector challenges. So the collaboration happens. John, between the public sector Entity, university with students and us, and what we bring to the table is technical talent, air technology and our mechanisms and processes, like they're working backwards processes, and they were like, We want you to bring your best and brightest students. Let's bring public sector in the bowl. They bring challenges there, riel that we can take on, and then they can go back and absorb, and they're pretty exciting. I today I talked about we have over 44 today that we've documented were working at Cal Poly. The one in Arizona State University is about smart cities. And then you heard We're announcing new ones. We've got two in France, one in Germany now, one that we're doing on cybersecurity with our mighty in Australia to be sitting bata rain. So you're going to see us Add a lot more of these and we're getting the results out of them. So you know we won't do if we don't like him. But right now we really like these partnerships. >>Results are looking good. What's going on with >>you? All right. And I'll tell you why. That why they're different, where we are taking on riel public sector issues and challenges that are happening, they're not kind of pie in the sky. We might get there because those are good things to do. But what we want to do is let's tackle things that are really homelessness, opioid crisis, human sex trafficking, that we're seeing things that are really in these communities and those air kind of grand. But then we're taking on areas like farming where we talked about Can we get strawberries rotting on the vine out of the field into the market before you lose billions of dollars in California. So it's things like that that were so its challenges that are quick and riel. And the thing about Cloud is you can create an application and solution and test it out very rapidly without high cost of doing that. No technical Dan, >>you mentioned Smart Cities. I just attended a session. Marty Walsh, the mayor of Boston's, got this 50 50 years smart city plan, and it's pretty impressive, but it's a heavy lift. So what do you see going on in smart cities? And you really can't do it without the cloud, which was kind of my big input cloud. Where's the data? What do you say, >>cloud? I O. T is a big part at these. All the centers that Andy talked about yesterday in his keynote and why the five G partnerships are so important. These centers, they're gonna be everywhere, and you don't even know they really exist because they could be everywhere. And if you have the five G capabilities to move those communications really fast and crypt them so you have all the security you need. This is game changing, but I'll give you an example. I'll go back to the kids for a minute at at Arizona State University, they put Io TI centers everywhere. They no traffic patterns. Have any parking slots? Airfield What Utilities of water, if they're trash bins are being filled at number of seats that are being taken up in stadiums. So it's things like that that they're really working to understand. What are the dynamics of their city and traffic flow around that smart city? And then they're adding things on for the students like Alexis skills. Where's all the activity? So you're adding all things like Alexa Abs, which go into a smart city kind of dynamic. We're not shop. Where's the best activities for about books, for about clothes? What's the pizza on sale tonight? So on and then two things like you saw today on Singapore, where they're taking data from all different elements of agencies and presenting that bad to citizen from their child as example Day one of a birth even before, where's all the service is what I do? How do I track these things? How do I navigate my city? to get all those service is the same. One can find this guy things they're not. They're really and they're actually happening. >>Seems like they're instrumented a lot of the components of the city learning from that and then deciding. Okay, where do we double down on where do we place? >>You're making it Every resilient government, a resilient town. I mean, these were the things that citizens can really help take intro Web and have a voice in doing >>threes. I want to say congratulations to your success. I know it's not for the faint of heart in the public sector of these days, a lot of blockers, a lot of politics, a lot of government lockers and the old procurement system technical debt. I mean, Windows 95 is probably still in a bunch of PCs and 50 45 fighters. 15 fighters. Oh, you've got a great job. You've been doing a great job and riding that wave. So congratulations. >>Well, I'll just say it's worth it. It is worth it. We are committed to public sector, and we really want to see everyone from our war fighters. Are citizens have the capabilities they need. So >>you know, you know that we're very passionate this year about going in the 2020 for the Cube and our audience to do a lot more tech for good programming. This'll is something that's near and dear to your heart as well. You have a chance to shape technology. >>Yes, well, today you saw we had a really amazing not for profit on stage with It's called Game Changer. And what we found with not for profits is that technology can be a game changer if they use it because it makes their mission dollars damage further. And they're an amazing father. And send a team that started game changer at. Taylor was in the hospital five years with terminal cancer, and he and his father, through these five years, kind of looked around. Look at all these Children what they need and they started. He is actually still here with us today, and now he's a young adult taking care of other young Children with cancer, using gaming technologies with their partner, twitch and eight MPs and helping analyze and understand what these young affected Children with cancer need, both that personally and academically and the tools he has He's helping really permit office and get back and it's really hard, Warren says. I was happy. My partner, Mike Level, who is my Gran's commercial sales in business, and I ran public Sector Day. We're honored to give them at a small token of our gift from A to B s to help support their efforts. >>Congratulates, We appreciate you coming on the Cube sharing the update on good luck into 2020. Great to see you 10 years at AWS day one. Still, >>it's day one. I feel like I started >>it like still, like 10 o'clock in the morning or like still a day it wasn't like >>I still wake up every day with the jump in my staff and excited about what I'm gonna do. And so I am. You know, I am really excited that we're doing and like Andy and I say we're just scratching the surface. >>You're a fighter. You are charging We love you, Great executive. You're the chief of public. Get a great job. Great, too. Follow you and ride the wave with Amazon and cover. You guys were documenting history. >>Yeah, exactly. We're in happy holidays to you all and help seeing our seventh and 20 >>so much. Okay, Cube coverage here live in Las Vegas. This is the cube coverage. Extracting the signals. Wanna shout out to eight of us? An intel for putting on the two sets without sponsorship, we wouldn't be able to support the mission of the Cube. I want to thank them. And thank you for watching with more after this short break.