 on 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 Think. And 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 of years. 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 it's been a year in this industry, it's 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 continue to advance the software stack that's attached to the technology because you have to have both the software and the hardware moving at the right rate and pace. We've advanced our Q 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 us well in this new digital world that we're finding ourselves in in terms of reaching out to developers and now we have over 300,000 unique downloads of the programming model and 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 as well the day is 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 or 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 and 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. It really get the value out of the machine and that's a very important metric. Yeah, we'd love to boil things down to a single metric. It's more complicated than that 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 and 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 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 a quadratic speedup 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 settlements. 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 into 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 and 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 of, 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 8,000 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 could 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. So 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? Absolutely, there's about a third of our network are in fact startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of them are focused on what I would call loosely the programming model, looking at improving algorithms across different industries and 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. So many of those startups are leveraging our Qiskit, our Quantum Information Science open programming model that we put out there. So it's open. So 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. But Jamie, it was interesting to 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&D, but you mentioned financial services, JPMC, it's almost like they're now doing advanced R&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 premier 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, right? So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. So 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 like five hours on this topic, looking at what could they do and learning from the work that's already done out there. So 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 a 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 drive in the industry anymore. It's this combination of data, AI and cloud for scale. But now you bring that, of course there are alternative processors going on. We're seeing that. But now as you bring in quantum that is 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 the caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. But clearly to the degree that 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. So there's a real promise in a lot of these different areas. I wonder if you could talk a little bit about sort of the landscape. And I really interested in what IBM brings to the table that's the sort of different. You're seeing a lot of companies enter this space, some big, many small. What's the unique aspect that IBM brings to the table and are you, you've mentioned co-creating before. Are you co-creating, co-operating 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. So 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, right? 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 client support 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. So you mentioned your experience in sort of traditional computing. Do you 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, you're going to go back to ENIAC and now, which is really exciting 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 and 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 are doing that in terms of being surrounded by this classical backplane that allows our two 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&D for the sake of R&D, you've actually, you know, 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, you know, specifically as it relates to where people start, right? I mean, if I'm interested in this, what do I do? Do I need new skills? Do I need, it's in the cloud, right? So I can spin 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 Kisket 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 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. So we have tremendous amount of education. We're also providing education to our business partners. So one of our key network members who I'll be speaking with later I think today is from Accenture. So Accenture is 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. You know, 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. I mean, it feels like almost like, you know, the return coming out of COVID-19, it's going to come in waves. And there's parts that are going to be commercialized early. And it's not binary. It's not like all of a sudden one day we're going to wait. Hey, quantum is here. It's really going to come in layers. Your thoughts? Yeah, I definitely agree with that. And 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. And so I think that the production level characteristics will occur at the rate and pace of the industry. And so 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, a premier 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. We'll be back right after this short break.